out-jvm/heldout.jsonl

{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO journalist SELECT min_temperature_f, patient_name FROM dws.dws_sessions_df WHERE min_temperature_f > 187\"))\n", "labels": {"reads": [{"table": "dws.dws_sessions_df", "columns": ["min_temperature_f", "patient_name"]}], "writes": [{"table": "journalist", "columns": ["min_temperature_f", "patient_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": "# TODO: 旧逻辑 INSERT INTO water_treatment_plant_upgrades SELECT * FROM legacy\ncur.execute(\"SELECT mh_id, is_public FROM tech_transactions LIMIT 169\")\n", "labels": {"reads": [{"table": "tech_transactions", "columns": ["mh_id", "is_public"]}], "writes": []}, "meta": {"template_id": "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": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO product_suppliers (image_url, party_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "product_suppliers", "columns": ["image_url", "party_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": "@pipeline(reads=\"gas_processing_plants\", writes=\"ancient_ceramics\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "gas_processing_plants", "columns": null}], "writes": [{"table": "ancient_ceramics", "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": "SCALA", "content": "spark.read.table(\"non_profit_employees\").select(\"id\", \"amt\").write.insertInto(\"individuals\")\n", "labels": {"reads": [{"table": "non_profit_employees", "columns": null}], "writes": [{"table": "individuals", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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 supplierfabric SELECT mining_operation_id, hours, pages_per_minute_color FROM culture_company WHERE mining_operation_id > 399\"))\n", "labels": {"reads": [{"table": "culture_company", "columns": ["mining_operation_id", "hours", "pages_per_minute_color"]}], "writes": [{"table": "supplierfabric", "columns": ["mining_operation_id", "hours", "pages_per_minute_color"]}]}, "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 = pd.read_sql(\"SELECT * FROM elections\", conn)\ndf.to_sql(\"national_security_agencies\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "elections", "columns": null}], "writes": [{"table": "national_security_agencies", "columns": null}]}, "meta": {"template_id": "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": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO salmon_farms SELECT lastclaimdate, schedule_id, fiscal_year FROM well WHERE lastclaimdate > 180;\nEOF\n", "labels": {"reads": [{"table": "well", "columns": ["lastclaimdate", "schedule_id", "fiscal_year"]}], "writes": [{"table": "salmon_farms", "columns": ["lastclaimdate", "schedule_id", "fiscal_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"vesseldocking\").executeInsert(\"cargo\");\n", "labels": {"reads": [{"table": "vesseldocking", "columns": null}], "writes": [{"table": "cargo", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 instruments 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": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO satellite_launches SELECT artist_id, costid, check_in_id, socially_responsible FROM news_articles WHERE artist_id > 84\"))\n", "labels": {"reads": [{"table": "news_articles", "columns": ["artist_id", "costid", "check_in_id", "socially_responsible"]}], "writes": [{"table": "satellite_launches", "columns": ["artist_id", "costid", "check_in_id", "socially_responsible"]}]}, "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(\"crane\").toPandas()\ndf[[\"fabric_id\", \"orgname\"]].to_sql(\"residential_buildings\", engine, index=False)\n", "labels": {"reads": [{"table": "crane", "columns": null}], "writes": [{"table": "residential_buildings", "columns": ["fabric_id", "orgname"]}]}, "meta": {"template_id": "py-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": "spark-sql --master yarn -e \"INSERT INTO extractiondata SELECT mode, ll_hours, status FROM biosensor WHERE mode > 455\"\n", "labels": {"reads": [{"table": "biosensor", "columns": ["mode", "ll_hours", "status"]}], "writes": [{"table": "extractiondata", "columns": ["mode", "ll_hours", "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": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO privacy_settings SELECT school_colors, pass_fail, user_rating, ship_id FROM sustainable_buildings WHERE school_colors > 167\")\n", "labels": {"reads": [{"table": "sustainable_buildings", "columns": ["school_colors", "pass_fail", "user_rating", "ship_id"]}], "writes": [{"table": "privacy_settings", "columns": ["school_colors", "pass_fail", "user_rating", "ship_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": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM union_stats\"\n", "labels": {"reads": [{"table": "union_stats", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "Dataset<Row> df = spark.table(\"ocean_basin\");\ndf.write().mode(\"overwrite\").saveAsTable(\"micro_mobility\");\n", "labels": {"reads": [{"table": "ocean_basin", "columns": null}], "writes": [{"table": "micro_mobility", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"job_history\").where(\"dt = current_date()\")\nevents.writeTo(\"ads.ads_member_point_delta\").append()\n", "labels": {"reads": [{"table": "job_history", "columns": null}], "writes": [{"table": "ads.ads_member_point_delta", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table freight --target-dir /tmp/land\n", "labels": {"reads": [{"table": "freight", "columns": null}], "writes": []}, "meta": {"template_id": "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=\"unique_devices\", limit=5000)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "unique_devices", "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 marine_protected_areas --columns quantitysold,address --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "marine_protected_areas", "columns": ["quantitysold", "address"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO construction_workers SELECT feature_details, medical_condition FROM ads.exposure_daily WHERE feature_details > 321\"\n", "labels": {"reads": [{"table": "ads.exposure_daily", "columns": ["feature_details", "medical_condition"]}], "writes": [{"table": "construction_workers", "columns": ["feature_details", "medical_condition"]}]}, "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(\"ai_researcher\").select([\"id\", \"amt\"]).copy_into(\"organisation_types\").commit()\n", "labels": {"reads": [{"table": "ai_researcher", "columns": null}], "writes": [{"table": "organisation_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": "SHELL", "content": "# model autonomous_taxis depends on ads.ads_users_delta\ndbt run --select autonomous_taxis --vars '{\"src\":\"ads.ads_users_delta\"}'\n", "labels": {"reads": [{"table": "ads.ads_users_delta", "columns": null}], "writes": [{"table": "autonomous_taxis", "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=\"public_buildings\", writes=\"ads.ads_users_delta\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "public_buildings", "columns": null}], "writes": [{"table": "ads.ads_users_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": "# model menus depends on stg.stg_refunds\ndbt run --select menus --vars '{\"src\":\"stg.stg_refunds\"}'\n", "labels": {"reads": [{"table": "stg.stg_refunds", "columns": null}], "writes": [{"table": "menus", "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 farm_soil_moisture depends on company_data\ndbt run --select farm_soil_moisture --vars '{\"src\":\"company_data\"}'\n", "labels": {"reads": [{"table": "company_data", "columns": null}], "writes": [{"table": "farm_soil_moisture", "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(\"has_pet\")\nsrc.write.insertInto(\"student_course_enrolment\", overwrite=True)\n", "labels": {"reads": [{"table": "has_pet", "columns": null}], "writes": [{"table": "student_course_enrolment", "columns": null}]}, "meta": {"template_id": "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": "# TODO: 旧逻辑 INSERT INTO sustainable_tourism SELECT * FROM legacy\ncur.execute(\"SELECT friend, functional_area_description FROM client LIMIT 235\")\n", "labels": {"reads": [{"table": "client", "columns": ["friend", "functional_area_description"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO classes SELECT member_in_charge_id, transaction_category, temp, vegetable FROM dept_locations WHERE member_in_charge_id > 223\")\n", "labels": {"reads": [{"table": "dept_locations", "columns": ["member_in_charge_id", "transaction_category", "temp", "vegetable"]}], "writes": [{"table": "classes", "columns": ["member_in_charge_id", "transaction_category", "temp", "vegetable"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO incident_responses SELECT requestdate, explainability_score, forest_type FROM national_security_agencies WHERE requestdate > 280\");\n", "labels": {"reads": [{"table": "national_security_agencies", "columns": ["requestdate", "explainability_score", "forest_type"]}], "writes": [{"table": "incident_responses", "columns": ["requestdate", "explainability_score", "forest_type"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "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 autonomous_taxis SELECT book_club_id, document_type_name FROM dwd.device_log_daily WHERE book_club_id > 347\"\n", "labels": {"reads": [{"table": "dwd.device_log_daily", "columns": ["book_club_id", "document_type_name"]}], "writes": [{"table": "autonomous_taxis", "columns": ["book_club_id", "document_type_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": "impala-shell -i impalad01 -q \"INSERT INTO satellite_missions SELECT user_category, chemical_id, violation_id FROM meetings WHERE user_category > 132\"\n", "labels": {"reads": [{"table": "meetings", "columns": ["user_category", "chemical_id", "violation_id"]}], "writes": [{"table": "satellite_missions", "columns": ["user_category", "chemical_id", "violation_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": "SCALA", "content": "spark.read.table(\"educational_programs\").select(\"id\", \"amt\").write.insertInto(\"dental_clinics\")\n", "labels": {"reads": [{"table": "educational_programs", "columns": null}], "writes": [{"table": "dental_clinics", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"daily_transactions\").select(\"id\", \"amt\").write.insertInto(\"public_buses\")\n", "labels": {"reads": [{"table": "daily_transactions", "columns": null}], "writes": [{"table": "public_buses", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "Dataset<Row> df = spark.table(\"student_course_enrolment\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "student_course_enrolment", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "Dataset<Row> df = spark.table(\"virtual_tours_usa\");\ndf.write().mode(\"overwrite\").saveAsTable(\"travel_advisories\");\n", "labels": {"reads": [{"table": "virtual_tours_usa", "columns": null}], "writes": [{"table": "travel_advisories", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"arts_culture.programs\")\npersist_to_output(df, \"language_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "arts_culture.programs", "columns": null}], "writes": [{"table": "language_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"trend_popularity\", limit=5000)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "trend_popularity", "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": "beeline -u \"$HS2_URL\" -e \"INSERT INTO trench_depths SELECT accommodationtype, provider_id, monthlyactiveusers FROM mill WHERE accommodationtype > 161\"\n", "labels": {"reads": [{"table": "mill", "columns": ["accommodationtype", "provider_id", "monthlyactiveusers"]}], "writes": [{"table": "trench_depths", "columns": ["accommodationtype", "provider_id", "monthlyactiveusers"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"veteranemployees\").where(\"dt = current_date()\")\nevents.writeTo(\"vesseldocking\").append()\n", "labels": {"reads": [{"table": "veteranemployees", "columns": null}], "writes": [{"table": "vesseldocking", "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": "spark.sql(\"SELECT origin, to_address FROM actors LIMIT 495\")\nimport logging\nspark.sql(\"INSERT INTO ads.exposure_daily SELECT impressions, trend, roomname FROM autonomous_vehicles WHERE impressions > 35\")\n", "labels": {"reads": [{"table": "actors", "columns": ["origin", "to_address"]}, {"table": "autonomous_vehicles", "columns": ["impressions", "trend", "roomname"]}], "writes": [{"table": "ads.exposure_daily", "columns": ["impressions", "trend", "roomname"]}]}, "meta": {"template_id": "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": "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": "SCALA", "content": "val df = spark.table(\"ods.shipments_daily\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dw.dw_vendors_hourly\")\n", "labels": {"reads": [{"table": "ods.shipments_daily", "columns": null}], "writes": [{"table": "dw.dw_vendors_hourly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "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 sessions SELECT hireid, service_id, health_equity_metric_1, plant_id FROM ods.ods_device_log_df WHERE hireid > 462\"\n", "labels": {"reads": [{"table": "ods.ods_device_log_df", "columns": ["hireid", "service_id", "health_equity_metric_1", "plant_id"]}], "writes": [{"table": "sessions", "columns": ["hireid", "service_id", "health_equity_metric_1", "plant_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": "events = spark.table(\"aircraft_manufacturer\").where(\"dt = current_date()\")\nevents.writeTo(\"supplierfabric\").append()\n", "labels": {"reads": [{"table": "aircraft_manufacturer", "columns": null}], "writes": [{"table": "supplierfabric", "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 donors SELECT chemical_type, bill_id, school_name FROM visitor_demographics WHERE chemical_type > 390\"], check=True)\n", "labels": {"reads": [{"table": "visitor_demographics", "columns": ["chemical_type", "bill_id", "school_name"]}], "writes": [{"table": "donors", "columns": ["chemical_type", "bill_id", "school_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.draft_details > 317).all()\n# src table: artifactcounts\nengine.execute(\"INSERT INTO community_development.schools SELECT * FROM artifactcounts\")\n", "labels": {"reads": [{"table": "artifactcounts", "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": "SCALA", "content": "spark.read.table(\"customer_orders\").select(\"id\", \"amt\").write.insertInto(\"attractions\")\n", "labels": {"reads": [{"table": "customer_orders", "columns": null}], "writes": [{"table": "attractions", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"hotel_ai\").select([\"id\", \"amt\"]).copy_into(\"techniques\").commit()\n", "labels": {"reads": [{"table": "hotel_ai", "columns": null}], "writes": [{"table": "techniques", "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(\"ai_adoption\").select([\"id\", \"amt\"]).copy_into(\"public_buses\").commit()\n", "labels": {"reads": [{"table": "ai_adoption", "columns": null}], "writes": [{"table": "public_buses", "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 network_investments SELECT billingcountry, date_joined_staff, founding_location, volunteer_date FROM gas_production WHERE billingcountry > 266\"))\n", "labels": {"reads": [{"table": "gas_production", "columns": ["billingcountry", "date_joined_staff", "founding_location", "volunteer_date"]}], "writes": [{"table": "network_investments", "columns": ["billingcountry", "date_joined_staff", "founding_location", "volunteer_date"]}]}, "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(\"genres\").where(\"dt = current_date()\")\nevents.writeTo(\"debate_people\").append()\n", "labels": {"reads": [{"table": "genres", "columns": null}], "writes": [{"table": "debate_people", "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(\"trench_depths\").where(\"dt = current_date()\")\nevents.writeTo(\"threat_intelligence_data\").append()\n", "labels": {"reads": [{"table": "trench_depths", "columns": null}], "writes": [{"table": "threat_intelligence_data", "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=\"csu_fees\", limit=5000)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "csu_fees", "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(\"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": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO mouse SELECT humidity, invested FROM addresses WHERE humidity > 162\")\n", "labels": {"reads": [{"table": "addresses", "columns": ["humidity", "invested"]}], "writes": [{"table": "mouse", "columns": ["humidity", "invested"]}]}, "meta": {"template_id": "py-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 bi.bi_clicks_daily SELECT age_group_id, funding_amount, founder, trade FROM performance WHERE age_group_id > 295\"\n", "labels": {"reads": [{"table": "performance", "columns": ["age_group_id", "funding_amount", "founder", "trade"]}], "writes": [{"table": "bi.bi_clicks_daily", "columns": ["age_group_id", "funding_amount", "founder", "trade"]}]}, "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(\"company_data\").select([\"id\", \"amt\"]).copy_into(\"dept_locations\").commit()\n", "labels": {"reads": [{"table": "company_data", "columns": null}], "writes": [{"table": "dept_locations", "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": "SCALA", "content": "spark.read.table(\"trip_segments\").select(\"id\", \"amt\").write.insertInto(\"tech_transactions\")\n", "labels": {"reads": [{"table": "trip_segments", "columns": null}], "writes": [{"table": "tech_transactions", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"carriers\").executeInsert(\"delivery_routes\");\n", "labels": {"reads": [{"table": "carriers", "columns": null}], "writes": [{"table": "delivery_routes", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"virtual_tours_usa\").executeInsert(\"dws.dws_vendors_daily\");\n", "labels": {"reads": [{"table": "virtual_tours_usa", "columns": null}], "writes": [{"table": "dws.dws_vendors_daily", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"ai_recs\").where(\"dt = current_date()\")\nevents.writeTo(\"football_teams\").append()\n", "labels": {"reads": [{"table": "ai_recs", "columns": null}], "writes": [{"table": "football_teams", "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 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-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 trend_popularity SELECT agegroup, location_name, daily_consumption FROM safety_test_results WHERE agegroup > 379\"))\n", "labels": {"reads": [{"table": "safety_test_results", "columns": ["agegroup", "location_name", "daily_consumption"]}], "writes": [{"table": "trend_popularity", "columns": ["agegroup", "location_name", "daily_consumption"]}]}, "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=\"ai_recs\", limit=5000)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "ai_recs", "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 document_sections SELECT financially_capable, discount, veteran_unemployment_rate FROM privacy_settings WHERE financially_capable > 48\"\n", "labels": {"reads": [{"table": "privacy_settings", "columns": ["financially_capable", "discount", "veteran_unemployment_rate"]}], "writes": [{"table": "document_sections", "columns": ["financially_capable", "discount", "veteran_unemployment_rate"]}]}, "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 = fetch_frame(ctx, \"news_articles\")\nupsert_to_target(df, \"london.lines\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "news_articles", "columns": null}], "writes": [{"table": "london.lines", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO skills_required_to_fix SELECT a.count_id, b.sales_count FROM union_members_demographics a JOIN dept_locations b ON a.pd_id = b.pd_id\"\n", "labels": {"reads": [{"table": "union_members_demographics", "columns": null}, {"table": "dept_locations", "columns": null}], "writes": [{"table": "skills_required_to_fix", "columns": null}]}, "meta": {"template_id": "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": "@pipeline(reads=\"dysprosium_production\", writes=\"healthcare_providers\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "dysprosium_production", "columns": null}], "writes": [{"table": "healthcare_providers", "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 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": "PYTHON", "content": "@pipeline(reads=\"oceans\", writes=\"cargoships\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "oceans", "columns": null}], "writes": [{"table": "cargoships", "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": "src = spark.read.table(\"ref_document_types\")\nsrc.write.insertInto(\"media_content\", overwrite=True)\n", "labels": {"reads": [{"table": "ref_document_types", "columns": null}], "writes": [{"table": "media_content", "columns": null}]}, "meta": {"template_id": "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": "SCALA", "content": "val df = spark.table(\"indianmines\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"musicevents\")\n", "labels": {"reads": [{"table": "indianmines", "columns": null}], "writes": [{"table": "musicevents", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"bi.cart_item\").executeInsert(\"company_data\");\n", "labels": {"reads": [{"table": "bi.cart_item", "columns": null}], "writes": [{"table": "company_data", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 supplychain SELECT founder_country, enddate FROM supplier WHERE founder_country > 169\"))\n", "labels": {"reads": [{"table": "supplier", "columns": ["founder_country", "enddate"]}], "writes": [{"table": "supplychain", "columns": ["founder_country", "enddate"]}]}, "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": "SCALA", "content": "// legacy: INSERT INTO fairness_reports SELECT * FROM legacy\nspark.sql(\"INSERT INTO african_region_table SELECT region_id, price_in_dollar, detention_summary FROM stg.stg_orders_df WHERE region_id > 254\")\n", "labels": {"reads": [{"table": "stg.stg_orders_df", "columns": ["region_id", "price_in_dollar", "detention_summary"]}], "writes": [{"table": "african_region_table", "columns": ["region_id", "price_in_dollar", "detention_summary"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "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'))\nsql = \"INSERT INTO airports SELECT a.forest_type, b.area_type FROM factory a JOIN skincare_ingredients b ON a.cultivatorid = b.cultivatorid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "factory", "columns": null}, {"table": "skincare_ingredients", "columns": null}], "writes": [{"table": "airports", "columns": null}]}, "meta": {"template_id": "py-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": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO london.lines SELECT production_mwh, daily_distance, co_owner_count, negative FROM workout_records WHERE production_mwh > 350\"))\n", "labels": {"reads": [{"table": "workout_records", "columns": ["production_mwh", "daily_distance", "co_owner_count", "negative"]}], "writes": [{"table": "london.lines", "columns": ["production_mwh", "daily_distance", "co_owner_count", "negative"]}]}, "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": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO payment SELECT course_description, city FROM public_buses WHERE course_description > 193\"], check=True)\n", "labels": {"reads": [{"table": "public_buses", "columns": ["course_description", "city"]}], "writes": [{"table": "payment", "columns": ["course_description", "city"]}]}, "meta": {"template_id": "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=\"clients\", writes=\"product_ingredients\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "clients", "columns": null}], "writes": [{"table": "product_ingredients", "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=\"dws_refunds_daily\", limit=5000)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "dws_refunds_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": "PYTHON", "content": "df = pd.read_sql(\"SELECT evaluated_for_fairness, service_type_code FROM smart_cities.ev_charging_stations\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"order_details\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "smart_cities.ev_charging_stations", "columns": ["evaluated_for_fairness", "service_type_code"]}], "writes": [{"table": "order_details", "columns": null}]}, "meta": {"template_id": "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": "# model church depends on public_transportation\ndbt run --select church --vars '{\"src\":\"public_transportation\"}'\n", "labels": {"reads": [{"table": "public_transportation", "columns": null}], "writes": [{"table": "church", "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(\"ads.ads_users_delta\").where(\"dt = current_date()\")\nevents.writeTo(\"threat_actors\").append()\n", "labels": {"reads": [{"table": "ads.ads_users_delta", "columns": null}], "writes": [{"table": "threat_actors", "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 intangible_heritage SELECT membergender, is_operational, oct, leader_name FROM medical_staff WHERE membergender > 355\"))\n", "labels": {"reads": [{"table": "medical_staff", "columns": ["membergender", "is_operational", "oct", "leader_name"]}], "writes": [{"table": "intangible_heritage", "columns": ["membergender", "is_operational", "oct", "leader_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": "JAVA", "content": "tableEnv.from(\"dwd.dwd_campaigns_daily\").executeInsert(\"job_history\");\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_daily", "columns": null}], "writes": [{"table": "job_history", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 photos SELECT alert_id, service_name FROM policyholder WHERE alert_id > 317;\nEOF\n", "labels": {"reads": [{"table": "policyholder", "columns": ["alert_id", "service_name"]}], "writes": [{"table": "photos", "columns": ["alert_id", "service_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO workforce_diversity SELECT * FROM legacy\ncur.execute(\"SELECT carbon_offset_tons, transact_count FROM mlb_teams_mascots LIMIT 144\")\n", "labels": {"reads": [{"table": "mlb_teams_mascots", "columns": ["carbon_offset_tons", "transact_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"cultural_sites\").executeInsert(\"visitors\");\n", "labels": {"reads": [{"table": "cultural_sites", "columns": null}], "writes": [{"table": "visitors", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 dw.users_di SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"fares\").executeInsert(\"cultural_sites\");\n", "labels": {"reads": [{"table": "fares", "columns": null}], "writes": [{"table": "cultural_sites", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO gas_production SELECT playerid, number_of_sightings FROM member_workout_date WHERE playerid > 476\");\n", "labels": {"reads": [{"table": "member_workout_date", "columns": ["playerid", "number_of_sightings"]}], "writes": [{"table": "gas_production", "columns": ["playerid", "number_of_sightings"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "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 client_transactions SELECT winning_aircraft, brand_id FROM population WHERE winning_aircraft > 52\"\n", "labels": {"reads": [{"table": "population", "columns": ["winning_aircraft", "brand_id"]}], "writes": [{"table": "client_transactions", "columns": ["winning_aircraft", "brand_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"event_types\").executeInsert(\"dws.dws_products_di\");\n", "labels": {"reads": [{"table": "event_types", "columns": null}], "writes": [{"table": "dws.dws_products_di", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"studentsmentalhealth\").select(\"id\", \"amt\").write.insertInto(\"ods.ods_products\")\n", "labels": {"reads": [{"table": "studentsmentalhealth", "columns": null}], "writes": [{"table": "ods.ods_products", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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 cultural_sites SELECT mgr_start_date, building_full_name, coupon_amount, wellid FROM mining_activities WHERE mgr_start_date > 358\"\n", "labels": {"reads": [{"table": "mining_activities", "columns": ["mgr_start_date", "building_full_name", "coupon_amount", "wellid"]}], "writes": [{"table": "cultural_sites", "columns": ["mgr_start_date", "building_full_name", "coupon_amount", "wellid"]}]}, "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": "SCALA", "content": "spark.table(\"ai_researchers\").where(\"dt = current_date()\").writeTo(\"ocean_trenches\").append()\n", "labels": {"reads": [{"table": "ai_researchers", "columns": null}], "writes": [{"table": "ocean_trenches", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"company_data\")\nupsert_to_output(df, \"readers\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "company_data", "columns": null}], "writes": [{"table": "readers", "columns": null}]}, "meta": {"template_id": "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 donations_gender_us SELECT customer_type_code, tech_type, dnumber FROM flight WHERE customer_type_code > 109\"\n", "labels": {"reads": [{"table": "flight", "columns": ["customer_type_code", "tech_type", "dnumber"]}], "writes": [{"table": "donations_gender_us", "columns": ["customer_type_code", "tech_type", "dnumber"]}]}, "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 classes SELECT genrename, max_dissolved_oxygen FROM skills_required_to_fix WHERE genrename > 103\"\n", "labels": {"reads": [{"table": "skills_required_to_fix", "columns": ["genrename", "max_dissolved_oxygen"]}], "writes": [{"table": "classes", "columns": ["genrename", "max_dissolved_oxygen"]}]}, "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 ods.ods_products SELECT * FROM legacy\ncur.execute(\"SELECT subscribe_date, discovered_date FROM donationdates LIMIT 81\")\n", "labels": {"reads": [{"table": "donationdates", "columns": ["subscribe_date", "discovered_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO ocean_basin SELECT forest_id, inventory_id, catalog_id, claimamount FROM ods.ods_shipments WHERE forest_id > 112\"\n", "labels": {"reads": [{"table": "ods.ods_shipments", "columns": ["forest_id", "inventory_id", "catalog_id", "claimamount"]}], "writes": [{"table": "ocean_basin", "columns": ["forest_id", "inventory_id", "catalog_id", "claimamount"]}]}, "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 assists, tournament_id FROM esports_participants LIMIT 107;\nINSERT INTO lending_initiatives SELECT password, university, trend, center_name FROM public_buildings WHERE password > 201;\nSQL\n", "labels": {"reads": [{"table": "esports_participants", "columns": ["assists", "tournament_id"]}, {"table": "public_buildings", "columns": ["password", "university", "trend", "center_name"]}], "writes": [{"table": "lending_initiatives", "columns": ["password", "university", "trend", "center_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO dw.dw_refunds_delta SELECT carrierid, start_station_name FROM mart.cart_item_daily WHERE carrierid > 66\"))\n", "labels": {"reads": [{"table": "mart.cart_item_daily", "columns": ["carrierid", "start_station_name"]}], "writes": [{"table": "dw.dw_refunds_delta", "columns": ["carrierid", "start_station_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": "JAVA", "content": "tableEnv.from(\"union_members_demographics\").executeInsert(\"broadcast\");\n", "labels": {"reads": [{"table": "union_members_demographics", "columns": null}], "writes": [{"table": "broadcast", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 coastal_erosion\ndbt run --select ocean_trenches --vars '{\"src\":\"coastal_erosion\"}'\n", "labels": {"reads": [{"table": "coastal_erosion", "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": "result = value * ratio + offset\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO ods.ods_shipments SELECT crime_date, mining_operation, class_senator_vote FROM country WHERE crime_date > 45\"\n", "labels": {"reads": [{"table": "country", "columns": ["crime_date", "mining_operation", "class_senator_vote"]}], "writes": [{"table": "ods.ods_shipments", "columns": ["crime_date", "mining_operation", "class_senator_vote"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model algorithm_fairness depends on daily_transactions\ndbt run --select algorithm_fairness --vars '{\"src\":\"daily_transactions\"}'\n", "labels": {"reads": [{"table": "daily_transactions", "columns": null}], "writes": [{"table": "algorithm_fairness", "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 dws.dws_sessions_df SELECT lifespan, cancel_date, artpiecename FROM network_investments WHERE lifespan > 398\"\n", "labels": {"reads": [{"table": "network_investments", "columns": ["lifespan", "cancel_date", "artpiecename"]}], "writes": [{"table": "dws.dws_sessions_df", "columns": ["lifespan", "cancel_date", "artpiecename"]}]}, "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 national_security_agencies SELECT cost_id, spectators, tournament_name FROM gas_processing_plants WHERE cost_id > 483\"))\n", "labels": {"reads": [{"table": "gas_processing_plants", "columns": ["cost_id", "spectators", "tournament_name"]}], "writes": [{"table": "national_security_agencies", "columns": ["cost_id", "spectators", "tournament_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=\"transactions_lots\", writes=\"donationcategories\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "transactions_lots", "columns": null}], "writes": [{"table": "donationcategories", "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": "SCALA", "content": "val df = spark.table(\"ads_exposure_daily\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"exoplanets\")\n", "labels": {"reads": [{"table": "ads_exposure_daily", "columns": null}], "writes": [{"table": "exoplanets", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "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 eu_ets\", conn)\ndf.to_sql(\"client\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "eu_ets", "columns": null}], "writes": [{"table": "client", "columns": null}]}, "meta": {"template_id": "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": "SCALA", "content": "spark.read.table(\"trench_depths\").select(\"id\", \"amt\").write.insertInto(\"carriers\")\n", "labels": {"reads": [{"table": "trench_depths", "columns": null}], "writes": [{"table": "carriers", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "Dataset<Row> df = spark.table(\"supplier_products\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "supplier_products", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"donors\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "donors", "columns": null}], "writes": []}, "meta": {"template_id": "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": "JAVA", "content": "Dataset<Row> df = spark.table(\"cargoships\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "cargoships", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"solar_installations\").where(\"dt = current_date()\")\nevents.writeTo(\"sales_data_last_year\").append()\n", "labels": {"reads": [{"table": "solar_installations", "columns": null}], "writes": [{"table": "sales_data_last_year", "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": "SCALA", "content": "// legacy: INSERT INTO production_quebec SELECT * FROM legacy\nspark.sql(\"INSERT INTO dwd.dwd_refunds_hourly SELECT publicationid, emissions, dept_id FROM sessions WHERE publicationid > 407\")\n", "labels": {"reads": [{"table": "sessions", "columns": ["publicationid", "emissions", "dept_id"]}], "writes": [{"table": "dwd.dwd_refunds_hourly", "columns": ["publicationid", "emissions", "dept_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO recipe SELECT petid, sustainable_practice FROM stg.stg_refunds_hourly WHERE petid > 448;\nEOF\n", "labels": {"reads": [{"table": "stg.stg_refunds_hourly", "columns": ["petid", "sustainable_practice"]}], "writes": [{"table": "recipe", "columns": ["petid", "sustainable_practice"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"media_content\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"project_info\")\n", "labels": {"reads": [{"table": "media_content", "columns": null}], "writes": [{"table": "project_info", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "val df = spark.table(\"workforce_diversity\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "workforce_diversity", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "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 public_transportation SELECT stop, account_name, ip_address, year FROM sessions WHERE stop > 409\"\n", "labels": {"reads": [{"table": "sessions", "columns": ["stop", "account_name", "ip_address", "year"]}], "writes": [{"table": "public_transportation", "columns": ["stop", "account_name", "ip_address", "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 flu_vaccinations SELECT feb, gamename FROM public_buildings WHERE feb > 80\"\n", "labels": {"reads": [{"table": "public_buildings", "columns": ["feb", "gamename"]}], "writes": [{"table": "flu_vaccinations", "columns": ["feb", "gamename"]}]}, "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 gamerevenue SELECT document_type_description, usage FROM community_development WHERE document_type_description > 446\"))\n", "labels": {"reads": [{"table": "community_development", "columns": ["document_type_description", "usage"]}], "writes": [{"table": "gamerevenue", "columns": ["document_type_description", "usage"]}]}, "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": "JAVA", "content": "tableEnv.from(\"bills\").executeInsert(\"ruralinfrastructure\");\n", "labels": {"reads": [{"table": "bills", "columns": null}], "writes": [{"table": "ruralinfrastructure", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 marine_protected_areas SELECT agency_id, peakhourid FROM nonprofits WHERE agency_id > 481\"\n", "labels": {"reads": [{"table": "nonprofits", "columns": ["agency_id", "peakhourid"]}], "writes": [{"table": "marine_protected_areas", "columns": ["agency_id", "peakhourid"]}]}, "meta": {"template_id": "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": "events = spark.table(\"quick_service.menu_items\").where(\"dt = current_date()\")\nevents.writeTo(\"dws.products_daily\").append()\n", "labels": {"reads": [{"table": "quick_service.menu_items", "columns": null}], "writes": [{"table": "dws.products_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": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO actual_orders SELECT hospitalid, customer_email_address FROM social_impact_scores WHERE hospitalid > 160\"))\n", "labels": {"reads": [{"table": "social_impact_scores", "columns": ["hospitalid", "customer_email_address"]}], "writes": [{"table": "actual_orders", "columns": ["hospitalid", "customer_email_address"]}]}, "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": "SCALA", "content": "// legacy: INSERT INTO claim_headers SELECT * FROM legacy\nspark.sql(\"INSERT INTO furniture_manufacte SELECT arrival_time, authid, catalog_publisher FROM ancient_ceramics WHERE arrival_time > 341\")\n", "labels": {"reads": [{"table": "ancient_ceramics", "columns": ["arrival_time", "authid", "catalog_publisher"]}], "writes": [{"table": "furniture_manufacte", "columns": ["arrival_time", "authid", "catalog_publisher"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "val df = spark.table(\"nonprofits\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "nonprofits", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "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 students_disabilities SELECT outcome_name, causename, license_type FROM client WHERE outcome_name > 327\"))\n", "labels": {"reads": [{"table": "client", "columns": ["outcome_name", "causename", "license_type"]}], "writes": [{"table": "students_disabilities", "columns": ["outcome_name", "causename", "license_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": "df = spark.read.table(\"payment\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "payment", "columns": null}], "writes": []}, "meta": {"template_id": "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": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mill SELECT payment_method, diplomacy_id, weight FROM medication WHERE payment_method > 280\"\n", "labels": {"reads": [{"table": "medication", "columns": ["payment_method", "diplomacy_id", "weight"]}], "writes": [{"table": "mill", "columns": ["payment_method", "diplomacy_id", "weight"]}]}, "meta": {"template_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": "@pipeline(reads=\"models\", writes=\"ethical_ai_courses_year\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "models", "columns": null}], "writes": [{"table": "ethical_ai_courses_year", "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": "SCALA", "content": "spark.read.table(\"travel_advisories\").select(\"id\", \"amt\").write.insertInto(\"students_disabilities\")\n", "labels": {"reads": [{"table": "travel_advisories", "columns": null}], "writes": [{"table": "students_disabilities", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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=\"sector\", limit=5000)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "sector", "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": "JAVA", "content": "tableEnv.from(\"emergency_incidents\").executeInsert(\"dw.dw_refunds_delta\");\n", "labels": {"reads": [{"table": "emergency_incidents", "columns": null}], "writes": [{"table": "dw.dw_refunds_delta", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"techniques\").select([\"id\", \"amt\"]).copy_into(\"statements\").commit()\n", "labels": {"reads": [{"table": "techniques", "columns": null}], "writes": [{"table": "statements", "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": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO ancient_ceramics SELECT claim_status_name, day_of_week, sea FROM transactions WHERE claim_status_name > 84\"\n", "labels": {"reads": [{"table": "transactions", "columns": ["claim_status_name", "day_of_week", "sea"]}], "writes": [{"table": "ancient_ceramics", "columns": ["claim_status_name", "day_of_week", "sea"]}]}, "meta": {"template_id": "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": "set -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table medical_staff --target-dir /tmp/land\n", "labels": {"reads": [{"table": "medical_staff", "columns": null}], "writes": []}, "meta": {"template_id": "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": "echo \"dry-run: INSERT INTO criminal_database SELECT 1\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO stock_data SELECT max_gust_speed_mph, energy_star_rating, location_id FROM residents WHERE max_gust_speed_mph > 175\"\n", "labels": {"reads": [{"table": "residents", "columns": ["max_gust_speed_mph", "energy_star_rating", "location_id"]}], "writes": [{"table": "stock_data", "columns": ["max_gust_speed_mph", "energy_star_rating", "location_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": "SCALA", "content": "spark.read.table(\"document_sections\").select(\"id\", \"amt\").write.insertInto(\"has_pet\")\n", "labels": {"reads": [{"table": "document_sections", "columns": null}], "writes": [{"table": "has_pet", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ai_recs\").toPandas()\ndf[[\"building_description\", \"production\"]].to_sql(\"autonomous_vehicles\", engine, index=False)\n", "labels": {"reads": [{"table": "ai_recs", "columns": null}], "writes": [{"table": "autonomous_vehicles", "columns": ["building_description", "production"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"stg_exposure_delta\").select([\"id\", \"amt\"]).copy_into(\"infrastructure_development\").commit()\n", "labels": {"reads": [{"table": "stg_exposure_delta", "columns": null}], "writes": [{"table": "infrastructure_development", "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(\"farmland\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"sessions\")\n", "labels": {"reads": [{"table": "farmland", "columns": null}], "writes": [{"table": "sessions", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO pallets SELECT affirmative, credits FROM job_history WHERE affirmative > 27\"))\n", "labels": {"reads": [{"table": "job_history", "columns": ["affirmative", "credits"]}], "writes": [{"table": "pallets", "columns": ["affirmative", "credits"]}]}, "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": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ipl_runs\"\n", "labels": {"reads": [{"table": "ipl_runs", "columns": null}], "writes": []}, "meta": {"template_id": "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(\"research_outcomes\").select([\"id\", \"amt\"]).copy_into(\"tourists\").commit()\n", "labels": {"reads": [{"table": "research_outcomes", "columns": null}], "writes": [{"table": "tourists", "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 vr_games SELECT highscore, serving_size, financing_date, recruiterid FROM public_buses WHERE highscore > 419\"\n", "labels": {"reads": [{"table": "public_buses", "columns": ["highscore", "serving_size", "financing_date", "recruiterid"]}], "writes": [{"table": "vr_games", "columns": ["highscore", "serving_size", "financing_date", "recruiterid"]}]}, "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, \"stock_data\")\nsave_to_store(df, \"funding\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stock_data", "columns": null}], "writes": [{"table": "funding", "columns": null}]}, "meta": {"template_id": "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": "JAVA", "content": "tableEnv.from(\"document_drafts\").executeInsert(\"union_members_demographics\");\n", "labels": {"reads": [{"table": "document_drafts", "columns": null}], "writes": [{"table": "union_members_demographics", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 elements SELECT pediatrician_id, carrier, award, salinity FROM tour_types WHERE pediatrician_id > 31\"\n", "labels": {"reads": [{"table": "tour_types", "columns": ["pediatrician_id", "carrier", "award", "salinity"]}], "writes": [{"table": "elements", "columns": ["pediatrician_id", "carrier", "award", "salinity"]}]}, "meta": {"template_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": "JAVA", "content": "Dataset<Row> df = spark.table(\"moviebudgets\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "moviebudgets", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"menus\").where(\"dt = current_date()\")\nevents.writeTo(\"virtual_tours_usa\").append()\n", "labels": {"reads": [{"table": "menus", "columns": null}], "writes": [{"table": "virtual_tours_usa", "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(\"artifactcounts\").select([\"id\", \"amt\"]).copy_into(\"vehicle_safety\").commit()\n", "labels": {"reads": [{"table": "artifactcounts", "columns": null}], "writes": [{"table": "vehicle_safety", "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 research_outcomes SELECT a.menuitemid, b.stop FROM statements a JOIN carriers b ON a.address_line_1 = b.address_line_1\"\n", "labels": {"reads": [{"table": "statements", "columns": null}, {"table": "carriers", "columns": null}], "writes": [{"table": "research_outcomes", "columns": null}]}, "meta": {"template_id": "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": "# model newssource depends on techniques\ndbt run --select newssource --vars '{\"src\":\"techniques\"}'\n", "labels": {"reads": [{"table": "techniques", "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": "impala-shell -i impalad01 -q \"INSERT INTO dws.dws_exposure_df SELECT roomid, license_id FROM sales_data WHERE roomid > 95\"\n", "labels": {"reads": [{"table": "sales_data", "columns": ["roomid", "license_id"]}], "writes": [{"table": "dws.dws_exposure_df", "columns": ["roomid", "license_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": "JAVA", "content": "tableEnv.from(\"farmland\").executeInsert(\"dws.dws_inventory_full\");\n", "labels": {"reads": [{"table": "farmland", "columns": null}], "writes": [{"table": "dws.dws_inventory_full", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 discount, head_id FROM document_drafts WHERE discount > 491\"\n", "labels": {"reads": [{"table": "document_drafts", "columns": ["discount", "head_id"]}], "writes": [{"table": "ads.exposure_daily", "columns": ["discount", "head_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": "msg = \"would run: INSERT INTO bi.bi_clicks_daily SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"lanthanummines\").executeInsert(\"instruments\");\n", "labels": {"reads": [{"table": "lanthanummines", "columns": null}], "writes": [{"table": "instruments", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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=\"order_details\", limit=5000)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "order_details", "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 dwd.dwd_risk_score SELECT drug, sale_quantity, preference_score, char_cells FROM micro_mobility WHERE drug > 199\"))\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": "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 casesattorneys depends on mature_forest\ndbt run --select casesattorneys --vars '{\"src\":\"mature_forest\"}'\n", "labels": {"reads": [{"table": "mature_forest", "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": "msg = \"would run: INSERT INTO autonomous_vehicles SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.table(\"union_finance\").where(\"dt = current_date()\").writeTo(\"lanthanummines\").append()\n", "labels": {"reads": [{"table": "union_finance", "columns": null}], "writes": [{"table": "lanthanummines", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"buses\").where(\"dt = current_date()\")\nevents.writeTo(\"dws.dws_cart_item_di\").append()\n", "labels": {"reads": [{"table": "buses", "columns": null}], "writes": [{"table": "dws.dws_cart_item_di", "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 biosensor SELECT case_type, outcome_type, life_expectancy FROM bi.bi_coupon_use_di WHERE case_type > 500\"))\n", "labels": {"reads": [{"table": "bi.bi_coupon_use_di", "columns": ["case_type", "outcome_type", "life_expectancy"]}], "writes": [{"table": "biosensor", "columns": ["case_type", "outcome_type", "life_expectancy"]}]}, "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": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO musicevents SELECT postal_code, authors, group_equity_shareholding FROM club_rank WHERE postal_code > 114\"], check=True)\n", "labels": {"reads": [{"table": "club_rank", "columns": ["postal_code", "authors", "group_equity_shareholding"]}], "writes": [{"table": "musicevents", "columns": ["postal_code", "authors", "group_equity_shareholding"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"dept_locations\", limit=5000)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "dept_locations", "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": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO farmwatertemp SELECT operation_type, gender FROM fares WHERE operation_type > 31\")\n", "labels": {"reads": [{"table": "fares", "columns": ["operation_type", "gender"]}], "writes": [{"table": "farmwatertemp", "columns": ["operation_type", "gender"]}]}, "meta": {"template_id": "py-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 network_investments SELECT dispensaryid, scores FROM worker_salaries WHERE dispensaryid > 357\"\n", "labels": {"reads": [{"table": "worker_salaries", "columns": ["dispensaryid", "scores"]}], "writes": [{"table": "network_investments", "columns": ["dispensaryid", "scores"]}]}, "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=\"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": "SHELL", "content": "# model dwd_campaigns_daily depends on order_details\ndbt run --select dwd_campaigns_daily --vars '{\"src\":\"order_details\"}'\n", "labels": {"reads": [{"table": "order_details", "columns": null}], "writes": [{"table": "dwd_campaigns_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": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO worker SELECT common_name, cargo_weight, exhibitions FROM playlists WHERE common_name > 125\")\n", "labels": {"reads": [{"table": "playlists", "columns": ["common_name", "cargo_weight", "exhibitions"]}], "writes": [{"table": "worker", "columns": ["common_name", "cargo_weight", "exhibitions"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "Dataset<Row> df = spark.table(\"meetings\");\ndf.write().mode(\"overwrite\").saveAsTable(\"tour_types\");\n", "labels": {"reads": [{"table": "meetings", "columns": null}], "writes": [{"table": "tour_types", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"humanitarianassistance\", writes=\"ads.exposure_daily\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "humanitarianassistance", "columns": null}], "writes": [{"table": "ads.exposure_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": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO policies SELECT meal_date, supplier, partner FROM broadcast WHERE meal_date > 195\"))\n", "labels": {"reads": [{"table": "broadcast", "columns": ["meal_date", "supplier", "partner"]}], "writes": [{"table": "policies", "columns": ["meal_date", "supplier", "partner"]}]}, "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": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO population SELECT content, case_status FROM aircraft_manufacturer WHERE content > 77\")\n", "labels": {"reads": [{"table": "aircraft_manufacturer", "columns": ["content", "case_status"]}], "writes": [{"table": "population", "columns": ["content", "case_status"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "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 stayid, fan_name FROM delivery_route_locations LIMIT 378;\nINSERT INTO policies SELECT royal_family_details, image_date, defense_contractor_id, nutrient_level FROM ref_transaction_types WHERE royal_family_details > 135;\nSQL\n", "labels": {"reads": [{"table": "delivery_route_locations", "columns": ["stayid", "fan_name"]}, {"table": "ref_transaction_types", "columns": ["royal_family_details", "image_date", "defense_contractor_id", "nutrient_level"]}], "writes": [{"table": "policies", "columns": ["royal_family_details", "image_date", "defense_contractor_id", "nutrient_level"]}]}, "meta": {"template_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": "SCALA", "content": "spark.read.table(\"type_of_restaurant\").select(\"id\", \"amt\").write.insertInto(\"ods.shipments_daily\")\n", "labels": {"reads": [{"table": "type_of_restaurant", "columns": null}], "writes": [{"table": "ods.shipments_daily", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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 african_region_table SELECT sales_details, dateundergoes FROM projects_pakistan WHERE sales_details > 51\"\n", "labels": {"reads": [{"table": "projects_pakistan", "columns": ["sales_details", "dateundergoes"]}], "writes": [{"table": "african_region_table", "columns": ["sales_details", "dateundergoes"]}]}, "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 donor_country, shipment_year FROM fairness_reports LIMIT 277\")\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO donationdates SELECT medical_professional_id, initiativename, dissolved_oxygen, tripid FROM cultural_heritage_sites WHERE medical_professional_id > 23\")\n", "labels": {"reads": [{"table": "fairness_reports", "columns": ["donor_country", "shipment_year"]}, {"table": "cultural_heritage_sites", "columns": ["medical_professional_id", "initiativename", "dissolved_oxygen", "tripid"]}], "writes": [{"table": "donationdates", "columns": ["medical_professional_id", "initiativename", "dissolved_oxygen", "tripid"]}]}, "meta": {"template_id": "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": "df = pd.read_sql(\"SELECT trainingtype, eventname FROM problems\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"ref_transaction_types\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "problems", "columns": ["trainingtype", "eventname"]}], "writes": [{"table": "ref_transaction_types", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"vehicle_safety\").select(\"id\", \"amt\").write.insertInto(\"customer_complaints\")\n", "labels": {"reads": [{"table": "vehicle_safety", "columns": null}], "writes": [{"table": "customer_complaints", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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 urban.buildings SELECT guest_last_name, affiliation FROM vehicle_safety WHERE guest_last_name > 227\"))\n", "labels": {"reads": [{"table": "vehicle_safety", "columns": ["guest_last_name", "affiliation"]}], "writes": [{"table": "urban.buildings", "columns": ["guest_last_name", "affiliation"]}]}, "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 gas_station SELECT hireyear, email_address, productivity, treatment_year FROM nonprofits WHERE hireyear > 13\"\n", "labels": {"reads": [{"table": "nonprofits", "columns": ["hireyear", "email_address", "productivity", "treatment_year"]}], "writes": [{"table": "gas_station", "columns": ["hireyear", "email_address", "productivity", "treatment_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": "# model mart_cart_item_daily depends on product_suppliers\ndbt run --select mart_cart_item_daily --vars '{\"src\":\"product_suppliers\"}'\n", "labels": {"reads": [{"table": "product_suppliers", "columns": null}], "writes": [{"table": "mart_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": "PYTHON", "content": "cur.execute(\"SELECT followers, album_id FROM military_operations LIMIT 430\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "military_operations", "columns": ["followers", "album_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": "sqoop export --connect \"$JDBC\" --table ads.ads_coupon_use --columns performancedate,label --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ads.ads_coupon_use", "columns": ["performancedate", "label"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"donationcategories\", limit=5000)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "donationcategories", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"playlists\").where(\"dt = current_date()\")\nevents.writeTo(\"fish_species\").append()\n", "labels": {"reads": [{"table": "playlists", "columns": null}], "writes": [{"table": "fish_species", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO graduatestudents SELECT foreign, ride_id, incident_type_description, operation_name FROM mlb_teams_mascots WHERE foreign > 299\"\n", "labels": {"reads": [{"table": "mlb_teams_mascots", "columns": ["foreign", "ride_id", "incident_type_description", "operation_name"]}], "writes": [{"table": "graduatestudents", "columns": ["foreign", "ride_id", "incident_type_description", "operation_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": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO digital_divide SELECT pieces, gross_in_dollar, stadium_id, origin_city FROM project_info WHERE pieces > 458\"\n", "labels": {"reads": [{"table": "project_info", "columns": ["pieces", "gross_in_dollar", "stadium_id", "origin_city"]}], "writes": [{"table": "digital_divide", "columns": ["pieces", "gross_in_dollar", "stadium_id", "origin_city"]}]}, "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(\"citizen_feedback_records\").where(\"dt = current_date()\")\nevents.writeTo(\"community_education\").append()\n", "labels": {"reads": [{"table": "citizen_feedback_records", "columns": null}], "writes": [{"table": "community_education", "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 sustainable_tourism SELECT creation_date, case_status, severity FROM addresses WHERE creation_date > 18\"\n", "labels": {"reads": [{"table": "addresses", "columns": ["creation_date", "case_status", "severity"]}], "writes": [{"table": "sustainable_tourism", "columns": ["creation_date", "case_status", "severity"]}]}, "meta": {"template_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": "rows = session.query(Src).filter(Src.pieces > 34).all()\n# src table: language_projects\nengine.execute(\"INSERT INTO sculpture_sales SELECT * FROM language_projects\")\n", "labels": {"reads": [{"table": "language_projects", "columns": null}], "writes": [{"table": "sculpture_sales", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO london.lines SELECT booking_status_code, material_date FROM ods.ods_member_point_di WHERE booking_status_code > 31\"\n", "labels": {"reads": [{"table": "ods.ods_member_point_di", "columns": ["booking_status_code", "material_date"]}], "writes": [{"table": "london.lines", "columns": ["booking_status_code", "material_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"moviebudgets\", limit=5000)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "moviebudgets", "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 dapp_name, sponsor_name FROM network_investments LIMIT 86\")\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO mining_activities SELECT vulnerability, volunteer_id FROM energy_efficiency WHERE vulnerability > 164\")\n", "labels": {"reads": [{"table": "network_investments", "columns": ["dapp_name", "sponsor_name"]}, {"table": "energy_efficiency", "columns": ["vulnerability", "volunteer_id"]}], "writes": [{"table": "mining_activities", "columns": ["vulnerability", "volunteer_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": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO algorithm_fairness SELECT county, eia_date, reported_by_staff_id FROM ads_products WHERE county > 248\");\n", "labels": {"reads": [{"table": "ads_products", "columns": ["county", "eia_date", "reported_by_staff_id"]}], "writes": [{"table": "algorithm_fairness", "columns": ["county", "eia_date", "reported_by_staff_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "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\"$PW\" -e \"INSERT INTO non_profit_employees (incident_type_description, menu_item_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "non_profit_employees", "columns": ["incident_type_description", "menu_item_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"oil_spills\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"crane\")\n", "labels": {"reads": [{"table": "oil_spills", "columns": null}], "writes": [{"table": "crane", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM claims_processing\"\n", "labels": {"reads": [{"table": "claims_processing", "columns": null}], "writes": []}, "meta": {"template_id": "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": "# model esports_participants depends on performance\ndbt build -s esports_participants --vars '{\"src\":\"performance\"}'\n", "labels": {"reads": [{"table": "performance", "columns": null}], "writes": [{"table": "esports_participants", "columns": null}]}, "meta": {"template_id": "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 dwd.dwd_risk_score SELECT audienceid, supporter, num_stops, all_home FROM ferry_routes WHERE audienceid > 341\"))\n", "labels": {"reads": [{"table": "ferry_routes", "columns": ["audienceid", "supporter", "num_stops", "all_home"]}], "writes": [{"table": "dwd.dwd_risk_score", "columns": ["audienceid", "supporter", "num_stops", "all_home"]}]}, "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": "rows = session.query(Src).filter(Src.count_id > 23).all()\n# src table: farmwatertemp\nengine.execute(\"INSERT INTO ingredient_sources SELECT * FROM farmwatertemp\")\n", "labels": {"reads": [{"table": "farmwatertemp", "columns": null}], "writes": [{"table": "ingredient_sources", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "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": "PYTHON", "content": "Repo.of(\"dwd.dwd_refunds_hourly\").select([\"id\", \"amt\"]).copy_into(\"safetyrecord\").commit()\n", "labels": {"reads": [{"table": "dwd.dwd_refunds_hourly", "columns": null}], "writes": [{"table": "safetyrecord", "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 dws_products_di depends on dw.campaigns\ndbt run --select dws_products_di --vars '{\"src\":\"dw.campaigns\"}'\n", "labels": {"reads": [{"table": "dw.campaigns", "columns": null}], "writes": [{"table": "dws_products_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": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO peacekeeping_operations SELECT max_speed, max_wind_speed_mph, meal_id, purchase_transaction_id FROM investigative_journalism WHERE max_speed > 398\");\n", "labels": {"reads": [{"table": "investigative_journalism", "columns": ["max_speed", "max_wind_speed_mph", "meal_id", "purchase_transaction_id"]}], "writes": [{"table": "peacekeeping_operations", "columns": ["max_speed", "max_wind_speed_mph", "meal_id", "purchase_transaction_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"public_buses\").select([\"id\", \"amt\"]).copy_into(\"safetyrecord\").commit()\n", "labels": {"reads": [{"table": "public_buses", "columns": null}], "writes": [{"table": "safetyrecord", "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=\"enroll\", writes=\"crane\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "enroll", "columns": null}], "writes": [{"table": "crane", "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 community_development.schools SELECT year_founded, org FROM emergency_incidents WHERE year_founded > 365\"\n", "labels": {"reads": [{"table": "emergency_incidents", "columns": ["year_founded", "org"]}], "writes": [{"table": "community_development.schools", "columns": ["year_founded", "org"]}]}, "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=\"food\", writes=\"ods.ods_shipments\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "food", "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": "PYTHON", "content": "@pipeline(reads=\"injuries\", writes=\"claims_processing\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "injuries", "columns": null}], "writes": [{"table": "claims_processing", "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": "SCALA", "content": "spark.table(\"dws.products_daily\").where(\"dt = current_date()\").writeTo(\"joint_operations\").append()\n", "labels": {"reads": [{"table": "dws.products_daily", "columns": null}], "writes": [{"table": "joint_operations", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model regionwildlifehabitats depends on threat_intelligence_data\ndbt run -s regionwildlifehabitats --vars '{\"src\":\"threat_intelligence_data\"}'\n", "labels": {"reads": [{"table": "threat_intelligence_data", "columns": null}], "writes": [{"table": "regionwildlifehabitats", "columns": null}]}, "meta": {"template_id": "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": "SCALA", "content": "spark.read.table(\"destinations\").select(\"id\", \"amt\").write.insertInto(\"funds\")\n", "labels": {"reads": [{"table": "destinations", "columns": null}], "writes": [{"table": "funds", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"dw.campaigns\").select([\"id\", \"amt\"]).copy_into(\"problems\").commit()\n", "labels": {"reads": [{"table": "dw.campaigns", "columns": null}], "writes": [{"table": "problems", "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 performance depends on social_impact_scores\ndbt run --select performance --vars '{\"src\":\"social_impact_scores\"}'\n", "labels": {"reads": [{"table": "social_impact_scores", "columns": null}], "writes": [{"table": "performance", "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": "SCALA", "content": "spark.read.table(\"sustainable_tourism\").select(\"id\", \"amt\").write.insertInto(\"dw.payments_full\")\n", "labels": {"reads": [{"table": "sustainable_tourism", "columns": null}], "writes": [{"table": "dw.payments_full", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"habitat1\").select([\"id\", \"amt\"]).copy_into(\"sustainable_buildings\").commit()\n", "labels": {"reads": [{"table": "habitat1", "columns": null}], "writes": [{"table": "sustainable_buildings", "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 privacy_settings\", conn)\ndf.to_sql(\"dwd.member_point_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "privacy_settings", "columns": null}], "writes": [{"table": "dwd.member_point_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO europe_org SELECT a.party_id, b.labor_hour_id FROM cultural_heritage_sites a JOIN playlist_tracks b ON a.connection = b.connection\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "cultural_heritage_sites", "columns": null}, {"table": "playlist_tracks", "columns": null}], "writes": [{"table": "europe_org", "columns": null}]}, "meta": {"template_id": "py-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": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO community_education SELECT family_name, festival_name, vendor_id, sale_amount FROM ods.ods_events_delta WHERE family_name > 474\"))\n", "labels": {"reads": [{"table": "ods.ods_events_delta", "columns": ["family_name", "festival_name", "vendor_id", "sale_amount"]}], "writes": [{"table": "community_education", "columns": ["family_name", "festival_name", "vendor_id", "sale_amount"]}]}, "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 ocean_temperature depends on church\ndbt run --select ocean_temperature --vars '{\"src\":\"church\"}'\n", "labels": {"reads": [{"table": "church", "columns": null}], "writes": [{"table": "ocean_temperature", "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=\"biosensor\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "biosensor", "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(\"pumped_hydro_projects\").where(\"dt = current_date()\")\nevents.writeTo(\"elements\").append()\n", "labels": {"reads": [{"table": "pumped_hydro_projects", "columns": null}], "writes": [{"table": "elements", "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 artist_changes SELECT cargo_weight, maintenance_id FROM dept_locations WHERE cargo_weight > 447\"))\n", "labels": {"reads": [{"table": "dept_locations", "columns": ["cargo_weight", "maintenance_id"]}], "writes": [{"table": "artist_changes", "columns": ["cargo_weight", "maintenance_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=\"sessions\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "sessions", "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 techniques SELECT fairness_score, length_feet, dept_code FROM renewable_energy WHERE fairness_score > 34\"\n", "labels": {"reads": [{"table": "renewable_energy", "columns": ["fairness_score", "length_feet", "dept_code"]}], "writes": [{"table": "techniques", "columns": ["fairness_score", "length_feet", "dept_code"]}]}, "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(\"digital_divide\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"sustainablepractices\")\n", "labels": {"reads": [{"table": "digital_divide", "columns": null}], "writes": [{"table": "sustainablepractices", "columns": null}]}, "meta": {"template_id": "py-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": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO property SELECT customer, coach_name, interest_group, artworkid FROM graduate_students WHERE customer > 9\")\n", "labels": {"reads": [{"table": "graduate_students", "columns": ["customer", "coach_name", "interest_group", "artworkid"]}], "writes": [{"table": "property", "columns": ["customer", "coach_name", "interest_group", "artworkid"]}]}, "meta": {"template_id": "py-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": "events = spark.table(\"intelligencebudget\").where(\"dt = current_date()\")\nevents.writeTo(\"policies\").append()\n", "labels": {"reads": [{"table": "intelligencebudget", "columns": null}], "writes": [{"table": "policies", "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 mill SELECT vendorid, creator, directed_by, ticket_id FROM lifelong_learning WHERE vendorid > 495\"\n", "labels": {"reads": [{"table": "lifelong_learning", "columns": ["vendorid", "creator", "directed_by", "ticket_id"]}], "writes": [{"table": "mill", "columns": ["vendorid", "creator", "directed_by", "ticket_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": "# model financial_institutions depends on ai_recs\ndbt run --select financial_institutions --vars '{\"src\":\"ai_recs\"}'\n", "labels": {"reads": [{"table": "ai_recs", "columns": null}], "writes": [{"table": "financial_institutions", "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(\"jeans_sales\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"fish_species\")\n", "labels": {"reads": [{"table": "jeans_sales", "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": "SHELL", "content": "# model games depends on healthcare_access\ndbt run --select games --vars '{\"src\":\"healthcare_access\"}'\n", "labels": {"reads": [{"table": "healthcare_access", "columns": null}], "writes": [{"table": "games", "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 client_transactions SELECT home_games, task_details, org_name FROM organic_meals WHERE home_games > 273\"\n", "labels": {"reads": [{"table": "organic_meals", "columns": ["home_games", "task_details", "org_name"]}], "writes": [{"table": "client_transactions", "columns": ["home_games", "task_details", "org_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": "@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": "SCALA", "content": "spark.read.table(\"contractors\").select(\"id\", \"amt\").write.insertInto(\"graduatestudents\")\n", "labels": {"reads": [{"table": "contractors", "columns": null}], "writes": [{"table": "graduatestudents", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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=\"students_in_detention\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "students_in_detention", "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": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO haircareproducts SELECT spending, opname FROM enroll WHERE spending > 254\")\n", "labels": {"reads": [{"table": "enroll", "columns": ["spending", "opname"]}], "writes": [{"table": "haircareproducts", "columns": ["spending", "opname"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"medical\").where(\"dt = current_date()\")\nevents.writeTo(\"baseball_players\").append()\n", "labels": {"reads": [{"table": "medical", "columns": null}], "writes": [{"table": "baseball_players", "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": "JAVA", "content": "tableEnv.from(\"sector\").executeInsert(\"dws.dws_vendors_daily\");\n", "labels": {"reads": [{"table": "sector", "columns": null}], "writes": [{"table": "dws.dws_vendors_daily", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 mart.mart_events_delta SELECT investor, volunteerage FROM mouse WHERE investor > 87\"))\n", "labels": {"reads": [{"table": "mouse", "columns": ["investor", "volunteerage"]}], "writes": [{"table": "mart.mart_events_delta", "columns": ["investor", "volunteerage"]}]}, "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(\"ticket_prices\").select([\"id\", \"amt\"]).copy_into(\"stg.stg_exposure_df\").commit()\n", "labels": {"reads": [{"table": "ticket_prices", "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": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO trained_in SELECT q1_2022_views, pass_fail, payment_method_code, subject FROM ads.ads_users_delta WHERE q1_2022_views > 476\")\n", "labels": {"reads": [{"table": "ads.ads_users_delta", "columns": ["q1_2022_views", "pass_fail", "payment_method_code", "subject"]}], "writes": [{"table": "trained_in", "columns": ["q1_2022_views", "pass_fail", "payment_method_code", "subject"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "Dataset<Row> df = spark.table(\"defenseagreements\");\ndf.write().mode(\"overwrite\").saveAsTable(\"storm\");\n", "labels": {"reads": [{"table": "defenseagreements", "columns": null}], "writes": [{"table": "storm", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"spaceships\").select(\"id\", \"amt\").write.insertInto(\"ref_document_types\")\n", "labels": {"reads": [{"table": "spaceships", "columns": null}], "writes": [{"table": "ref_document_types", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nsql = \"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\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "country_production", "columns": null}, {"table": "studentsmentalhealth", "columns": null}], "writes": [{"table": "ods_vendors_di", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"chemical_safety\").executeInsert(\"bi_exposure_daily\");\n", "labels": {"reads": [{"table": "chemical_safety", "columns": null}], "writes": [{"table": "bi_exposure_daily", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"farm_soil_moisture\", writes=\"plant_safety_protocols\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "farm_soil_moisture", "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": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ads.ads_member_point_delta SELECT provider_name, visit_id, dept_address, app_id FROM wastewaterplant WHERE provider_name > 85\"\n", "labels": {"reads": [{"table": "wastewaterplant", "columns": ["provider_name", "visit_id", "dept_address", "app_id"]}], "writes": [{"table": "ads.ads_member_point_delta", "columns": ["provider_name", "visit_id", "dept_address", "app_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"volunteer_programs\", limit=5000)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "volunteer_programs", "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 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": "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 students_disabilities depends on sales_data\ndbt run --select students_disabilities --vars '{\"src\":\"sales_data\"}'\n", "labels": {"reads": [{"table": "sales_data", "columns": null}], "writes": [{"table": "students_disabilities", "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": "JAVA", "content": "tableEnv.from(\"mascaras\").executeInsert(\"ma_restaurants\");\n", "labels": {"reads": [{"table": "mascaras", "columns": null}], "writes": [{"table": "ma_restaurants", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"residential_buildings\").select(\"id\", \"amt\").write.insertInto(\"dw.payments_full\")\n", "labels": {"reads": [{"table": "residential_buildings", "columns": null}], "writes": [{"table": "dw.payments_full", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO schedule SELECT creation, container_count, ad_type, catalog_id FROM lanthanummines WHERE creation > 314\")\n", "labels": {"reads": [{"table": "lanthanummines", "columns": ["creation", "container_count", "ad_type", "catalog_id"]}], "writes": [{"table": "schedule", "columns": ["creation", "container_count", "ad_type", "catalog_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "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 dws.dws_inventory_full SELECT fund_name, status_of_thing_code, communitytype, statement_id FROM vehicle_safety WHERE fund_name > 100\"))\n", "labels": {"reads": [{"table": "vehicle_safety", "columns": ["fund_name", "status_of_thing_code", "communitytype", "statement_id"]}], "writes": [{"table": "dws.dws_inventory_full", "columns": ["fund_name", "status_of_thing_code", "communitytype", "statement_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(\"carriers\").where(\"dt = current_date()\")\nevents.writeTo(\"vendor\").append()\n", "labels": {"reads": [{"table": "carriers", "columns": null}], "writes": [{"table": "vendor", "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 sessions SELECT outcome_type, wellid FROM ods.ods_products WHERE outcome_type > 278\"\n", "labels": {"reads": [{"table": "ods.ods_products", "columns": ["outcome_type", "wellid"]}], "writes": [{"table": "sessions", "columns": ["outcome_type", "wellid"]}]}, "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": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO student_course_enrolment SELECT amount_funded, contract_start_date FROM mental_health_facilities WHERE amount_funded > 396\"\n", "labels": {"reads": [{"table": "mental_health_facilities", "columns": ["amount_funded", "contract_start_date"]}], "writes": [{"table": "student_course_enrolment", "columns": ["amount_funded", "contract_start_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO sustainable_tourism (caseid, birthday) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "sustainable_tourism", "columns": ["caseid", "birthday"]}]}, "meta": {"template_id": "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": "Repo.of(\"ods.ods_events_delta\").select([\"id\", \"amt\"]).copy_into(\"stg.stg_refunds_hourly\").commit()\n", "labels": {"reads": [{"table": "ods.ods_events_delta", "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": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO supplier_products (file_size, settlement_amount) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "supplier_products", "columns": ["file_size", "settlement_amount"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"news_articles\").select(\"id\", \"amt\").write.insertInto(\"stg.stg_refunds_hourly\")\n", "labels": {"reads": [{"table": "news_articles", "columns": null}], "writes": [{"table": "stg.stg_refunds_hourly", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model ads_exposure_daily depends on treatment\ndbt build -s ads_exposure_daily --vars 'source: treatment'\n", "labels": {"reads": [{"table": "treatment", "columns": null}], "writes": [{"table": "ads_exposure_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": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO supplier_products SELECT problem_id, recipe_id, don_name, classtype FROM oregondispensaries WHERE problem_id > 263\")\n", "labels": {"reads": [{"table": "oregondispensaries", "columns": ["problem_id", "recipe_id", "don_name", "classtype"]}], "writes": [{"table": "supplier_products", "columns": ["problem_id", "recipe_id", "don_name", "classtype"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"smartcityinitiatives\").select(\"id\", \"amt\").write.insertInto(\"residential_buildings\")\n", "labels": {"reads": [{"table": "smartcityinitiatives", "columns": null}], "writes": [{"table": "residential_buildings", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"treatment_type\").select(\"id\", \"amt\").write.insertInto(\"water_treatment_plant_upgrades\")\n", "labels": {"reads": [{"table": "treatment_type", "columns": null}], "writes": [{"table": "water_treatment_plant_upgrades", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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 startup SELECT amount_paid, painting_name FROM stg_exposure_delta WHERE amount_paid > 281\"\n", "labels": {"reads": [{"table": "stg_exposure_delta", "columns": ["amount_paid", "painting_name"]}], "writes": [{"table": "startup", "columns": ["amount_paid", "painting_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": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO freight_forwarding SELECT dept_code, healthcareid, innovation_id FROM nurse WHERE dept_code > 124\"\n", "labels": {"reads": [{"table": "nurse", "columns": ["dept_code", "healthcareid", "innovation_id"]}], "writes": [{"table": "freight_forwarding", "columns": ["dept_code", "healthcareid", "innovation_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": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table urban_agriculture --target-dir /tmp/land\n", "labels": {"reads": [{"table": "urban_agriculture", "columns": null}], "writes": []}, "meta": {"template_id": "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": "impala-shell -i impalad01 -q \"INSERT INTO startup SELECT calendar_date, mission_name, quarter FROM haircareproducts WHERE calendar_date > 4\"\n", "labels": {"reads": [{"table": "haircareproducts", "columns": ["calendar_date", "mission_name", "quarter"]}], "writes": [{"table": "startup", "columns": ["calendar_date", "mission_name", "quarter"]}]}, "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": "JAVA", "content": "tableEnv.from(\"nonprofits\").executeInsert(\"project_staff\");\n", "labels": {"reads": [{"table": "nonprofits", "columns": null}], "writes": [{"table": "project_staff", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"dw.dw_vendors_hourly\", writes=\"intangible_heritage\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "dw.dw_vendors_hourly", "columns": null}], "writes": [{"table": "intangible_heritage", "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(\"spaceships\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "spaceships", "columns": null}], "writes": []}, "meta": {"template_id": "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 deliveries SELECT clubdesc, laborproductivity, item, is_false FROM cyber_attacks WHERE clubdesc > 400\"\n", "labels": {"reads": [{"table": "cyber_attacks", "columns": ["clubdesc", "laborproductivity", "item", "is_false"]}], "writes": [{"table": "deliveries", "columns": ["clubdesc", "laborproductivity", "item", "is_false"]}]}, "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=\"students_in_detention\", limit=5000)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "students_in_detention", "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 artifactcounts SELECT artwork_name, account_balance, end_time, purchase_date FROM workforce_diversity WHERE artwork_name > 265\"))\n", "labels": {"reads": [{"table": "workforce_diversity", "columns": ["artwork_name", "account_balance", "end_time", "purchase_date"]}], "writes": [{"table": "artifactcounts", "columns": ["artwork_name", "account_balance", "end_time", "purchase_date"]}]}, "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": "import logging\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"energy_efficiency_stats\").select([\"id\", \"amt\"]).copy_into(\"dws.dws_exposure_df\").commit()\n", "labels": {"reads": [{"table": "energy_efficiency_stats", "columns": null}], "writes": [{"table": "dws.dws_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": "@pipeline(reads=\"hotel_ai\", writes=\"sculpture_sales\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "hotel_ai", "columns": null}], "writes": [{"table": "sculpture_sales", "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 storm depends on claim_headers\ndbt run --select storm --vars '{\"src\":\"claim_headers\"}'\n", "labels": {"reads": [{"table": "claim_headers", "columns": null}], "writes": [{"table": "storm", "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(\"crane\").where(\"dt = current_date()\")\nevents.writeTo(\"techniques\").append()\n", "labels": {"reads": [{"table": "crane", "columns": null}], "writes": [{"table": "techniques", "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(\"treatment_type\").where(\"dt = current_date()\")\nevents.writeTo(\"payment\").append()\n", "labels": {"reads": [{"table": "treatment_type", "columns": null}], "writes": [{"table": "payment", "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": "rows = session.query(Src).filter(Src.detention_summary > 277).all()\n# src table: salmon_farms\nengine.execute(\"INSERT INTO london.lines SELECT * FROM salmon_farms\")\n", "labels": {"reads": [{"table": "salmon_farms", "columns": null}], "writes": [{"table": "london.lines", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"mart.cart_item_daily\").select([\"id\", \"amt\"]).copy_into(\"ref_transaction_types\").commit()\n", "labels": {"reads": [{"table": "mart.cart_item_daily", "columns": null}], "writes": [{"table": "ref_transaction_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": "SCALA", "content": "// legacy: INSERT INTO pallets SELECT * FROM legacy\nspark.sql(\"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": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"funds\").select([\"id\", \"amt\"]).copy_into(\"screenings\").commit()\n", "labels": {"reads": [{"table": "funds", "columns": null}], "writes": [{"table": "screenings", "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 chemical_safety depends on wastewaterplant\ndbt run --select chemical_safety --vars '{\"src\":\"wastewaterplant\"}'\n", "labels": {"reads": [{"table": "wastewaterplant", "columns": null}], "writes": [{"table": "chemical_safety", "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 artist_changes SELECT total_cost, trench_id, train_number, is_electric FROM pollution_incidents WHERE total_cost > 191\"\n", "labels": {"reads": [{"table": "pollution_incidents", "columns": ["total_cost", "trench_id", "train_number", "is_electric"]}], "writes": [{"table": "artist_changes", "columns": ["total_cost", "trench_id", "train_number", "is_electric"]}]}, "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 museumart depends on supplier_products\ndbt run --select museumart --vars '{\"src\":\"supplier_products\"}'\n", "labels": {"reads": [{"table": "supplier_products", "columns": null}], "writes": [{"table": "museumart", "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=\"heritage_tours\", writes=\"project_info\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "heritage_tours", "columns": null}], "writes": [{"table": "project_info", "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": "JAVA", "content": "tableEnv.from(\"ruralinfrastructure\").executeInsert(\"dwd.dwd_refunds_hourly\");\n", "labels": {"reads": [{"table": "ruralinfrastructure", "columns": null}], "writes": [{"table": "dwd.dwd_refunds_hourly", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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_refunds_daily SELECT bookings, product_name FROM customer_orders WHERE bookings > 126\"\n", "labels": {"reads": [{"table": "customer_orders", "columns": ["bookings", "product_name"]}], "writes": [{"table": "dws_refunds_daily", "columns": ["bookings", "product_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": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO marine_trenches SELECT other_account_details, paritystatus, organization_details FROM teacher_development WHERE other_account_details > 44\"\n", "labels": {"reads": [{"table": "teacher_development", "columns": ["other_account_details", "paritystatus", "organization_details"]}], "writes": [{"table": "marine_trenches", "columns": ["other_account_details", "paritystatus", "organization_details"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"lanthanumshipments\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [{"table": "lanthanumshipments", "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=\"mart.mart_events_delta\", writes=\"sleep\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "mart.mart_events_delta", "columns": null}], "writes": [{"table": "sleep", "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": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO healthcare_workers SELECT drug_id, brand, post_date, certification_id FROM jobopenings WHERE drug_id > 228\");\n", "labels": {"reads": [{"table": "jobopenings", "columns": ["drug_id", "brand", "post_date", "certification_id"]}], "writes": [{"table": "healthcare_workers", "columns": ["drug_id", "brand", "post_date", "certification_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"pollution_incidents\").executeInsert(\"hybrid_sales\");\n", "labels": {"reads": [{"table": "pollution_incidents", "columns": null}], "writes": [{"table": "hybrid_sales", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"mart.cart_item_daily\").select(\"id\", \"amt\").write.insertInto(\"stg.stg_orders_df\")\n", "labels": {"reads": [{"table": "mart.cart_item_daily", "columns": null}], "writes": [{"table": "stg.stg_orders_df", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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 habitat1 SELECT a.major, b.directed_by FROM newssource a JOIN ocean_temperature b ON a.resource = b.resource\"\n", "labels": {"reads": [{"table": "newssource", "columns": null}, {"table": "ocean_temperature", "columns": null}], "writes": [{"table": "habitat1", "columns": null}]}, "meta": {"template_id": "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": "Repo.of(\"london.lines\").select([\"id\", \"amt\"]).copy_into(\"memberships\").commit()\n", "labels": {"reads": [{"table": "london.lines", "columns": null}], "writes": [{"table": "memberships", "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 african_region_table SELECT bikes_available, date_claim_settled, watch_time FROM soccer_players WHERE bikes_available > 480\"\n", "labels": {"reads": [{"table": "soccer_players", "columns": ["bikes_available", "date_claim_settled", "watch_time"]}], "writes": [{"table": "african_region_table", "columns": ["bikes_available", "date_claim_settled", "watch_time"]}]}, "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": "SCALA", "content": "spark.read.table(\"incident_regions\").select(\"id\", \"amt\").write.insertInto(\"skincare_ingredients\")\n", "labels": {"reads": [{"table": "incident_regions", "columns": null}], "writes": [{"table": "skincare_ingredients", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"music_concerts\").executeInsert(\"flight\");\n", "labels": {"reads": [{"table": "music_concerts", "columns": null}], "writes": [{"table": "flight", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 ads.ads_campaigns_delta SELECT acidity, left_office FROM concertinfo WHERE acidity > 334\"\n", "labels": {"reads": [{"table": "concertinfo", "columns": ["acidity", "left_office"]}], "writes": [{"table": "ads.ads_campaigns_delta", "columns": ["acidity", "left_office"]}]}, "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 attractions depends on aquaculture_sites\ndbt run --select attractions --vars '{\"src\":\"aquaculture_sites\"}'\n", "labels": {"reads": [{"table": "aquaculture_sites", "columns": null}], "writes": [{"table": "attractions", "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": "SCALA", "content": "spark.read.table(\"documents_mailed\").select(\"id\", \"amt\").write.insertInto(\"dysprosium_production\")\n", "labels": {"reads": [{"table": "documents_mailed", "columns": null}], "writes": [{"table": "dysprosium_production", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"salaries\").where(\"dt = current_date()\")\nevents.writeTo(\"mart.sessions_hourly\").append()\n", "labels": {"reads": [{"table": "salaries", "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": "PYTHON", "content": "events = spark.table(\"job_history\").where(\"dt = current_date()\")\nevents.writeTo(\"privacy_settings\").append()\n", "labels": {"reads": [{"table": "job_history", "columns": null}], "writes": [{"table": "privacy_settings", "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\nexport TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO personfriend SELECT customer_number, festival_name, oct FROM recyclingprograms WHERE customer_number > 200\"\n", "labels": {"reads": [{"table": "recyclingprograms", "columns": ["customer_number", "festival_name", "oct"]}], "writes": [{"table": "personfriend", "columns": ["customer_number", "festival_name", "oct"]}]}, "meta": {"template_id": "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": "src = spark.read.table(\"student_records\")\nsrc.write.insertInto(\"ads.ads_member_point_delta\", overwrite=True)\n", "labels": {"reads": [{"table": "student_records", "columns": null}], "writes": [{"table": "ads.ads_member_point_delta", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO bi.bi_coupon_use_di SELECT job_title, materialname, members FROM attractions WHERE job_title > 408\"))\n", "labels": {"reads": [{"table": "attractions", "columns": ["job_title", "materialname", "members"]}], "writes": [{"table": "bi.bi_coupon_use_di", "columns": ["job_title", "materialname", "members"]}]}, "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(\"dws_refunds_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"user_data\")\n", "labels": {"reads": [{"table": "dws_refunds_daily", "columns": null}], "writes": [{"table": "user_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table inspections_tx --target-dir /tmp/land\n", "labels": {"reads": [{"table": "inspections_tx", "columns": null}], "writes": []}, "meta": {"template_id": "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": "impala-shell -i impalad01 -q \"INSERT INTO concertinfo SELECT treatment_id, consultations FROM facilities WHERE treatment_id > 173\"\n", "labels": {"reads": [{"table": "facilities", "columns": ["treatment_id", "consultations"]}], "writes": [{"table": "concertinfo", "columns": ["treatment_id", "consultations"]}]}, "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": "JAVA", "content": "Dataset<Row> df = spark.table(\"farmwatertemp\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "farmwatertemp", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"london.lines\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "london.lines", "columns": null}], "writes": []}, "meta": {"template_id": "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": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO urban.buildings SELECT permit_number, business_zone FROM recyclingamount WHERE permit_number > 46\");\n", "labels": {"reads": [{"table": "recyclingamount", "columns": ["permit_number", "business_zone"]}], "writes": [{"table": "urban.buildings", "columns": ["permit_number", "business_zone"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "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)\nretries = int(os.environ.get('RETRIES', '3'))\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": "@pipeline(reads=\"climate_change_impacts_arctic_ocean\", writes=\"playlists\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "climate_change_impacts_arctic_ocean", "columns": null}], "writes": [{"table": "playlists", "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=\"worker\", writes=\"client\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "worker", "columns": null}], "writes": [{"table": "client", "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": "rows = session.query(Src).filter(Src.airline > 427).all()\n# src table: autonomous_taxis\nengine.execute(\"INSERT INTO vehicle_safety SELECT * FROM autonomous_taxis\")\n", "labels": {"reads": [{"table": "autonomous_taxis", "columns": null}], "writes": [{"table": "vehicle_safety", "columns": null}]}, "meta": {"template_id": "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": "JAVA", "content": "tableEnv.from(\"healthcare_workers\").executeInsert(\"heritage_tours\");\n", "labels": {"reads": [{"table": "healthcare_workers", "columns": null}], "writes": [{"table": "heritage_tours", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 dws_refunds_daily SELECT 1\"\nset -euo pipefail\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": "frame = warehouse_client.fetch(table=\"stg.stg_exposure_df\", limit=5000)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "stg.stg_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": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO claim_3 SELECT item_size, cid FROM manufacturer_sales WHERE item_size > 294\"))\n", "labels": {"reads": [{"table": "manufacturer_sales", "columns": ["item_size", "cid"]}], "writes": [{"table": "claim_3", "columns": ["item_size", "cid"]}]}, "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": "SCALA", "content": "spark.read.table(\"medication\").select(\"id\", \"amt\").write.insertInto(\"aquaculture_zones\")\n", "labels": {"reads": [{"table": "medication", "columns": null}], "writes": [{"table": "aquaculture_zones", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "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": "# model claim_3 depends on lanthanumshipments\ndbt build -s claim_3 --vars '{\"source_table\":\"lanthanumshipments\"}'\n", "labels": {"reads": [{"table": "lanthanumshipments", "columns": null}], "writes": [{"table": "claim_3", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "val df = spark.table(\"sculpture_sales\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"healthcare_providers\")\n", "labels": {"reads": [{"table": "sculpture_sales", "columns": null}], "writes": [{"table": "healthcare_providers", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model airports depends on resourcemanagement\ndbt build --select airports --vars '{\"src\":\"resourcemanagement\"}'\n", "labels": {"reads": [{"table": "resourcemanagement", "columns": null}], "writes": [{"table": "airports", "columns": null}]}, "meta": {"template_id": "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": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bi_exposure_daily SELECT case_number, well_id, partner FROM traveladvisoryreasons WHERE case_number > 197\"\n", "labels": {"reads": [{"table": "traveladvisoryreasons", "columns": ["case_number", "well_id", "partner"]}], "writes": [{"table": "bi_exposure_daily", "columns": ["case_number", "well_id", "partner"]}]}, "meta": {"template_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": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO salaries (church_id, investor_details) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "salaries", "columns": ["church_id", "investor_details"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model customer_complaints depends on stg_exposure_delta\ndbt run --select customer_complaints --vars '{\"src\":\"stg_exposure_delta\"}'\n", "labels": {"reads": [{"table": "stg_exposure_delta", "columns": null}], "writes": [{"table": "customer_complaints", "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(\"ref_company_types\").select([\"id\", \"amt\"]).copy_into(\"property_sales\").commit()\n", "labels": {"reads": [{"table": "ref_company_types", "columns": null}], "writes": [{"table": "property_sales", "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": "SCALA", "content": "spark.table(\"union_finance\").where(\"dt = current_date()\").writeTo(\"stg_campaigns_di\").append()\n", "labels": {"reads": [{"table": "union_finance", "columns": null}], "writes": [{"table": "stg_campaigns_di", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT artworkid, individual_name FROM oil_spills LIMIT 61\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "oil_spills", "columns": ["artworkid", "individual_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"attorneys\").executeInsert(\"sales_data\");\n", "labels": {"reads": [{"table": "attorneys", "columns": null}], "writes": [{"table": "sales_data", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 job_history SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "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": "JAVA", "content": "tableEnv.from(\"vesseldocking\").executeInsert(\"club_rank\");\n", "labels": {"reads": [{"table": "vesseldocking", "columns": null}], "writes": [{"table": "club_rank", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 stg_exposure_delta (culture, foreign) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stg_exposure_delta", "columns": ["culture", "foreign"]}]}, "meta": {"template_id": "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=\"affected_region\", limit=5000)\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "affected_region", "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 pallets depends on privacy_settings\ndbt run --select pallets --vars '{\"src\":\"privacy_settings\"}'\n", "labels": {"reads": [{"table": "privacy_settings", "columns": null}], "writes": [{"table": "pallets", "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=\"destinations\", writes=\"images\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "destinations", "columns": null}], "writes": [{"table": "images", "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": "SCALA", "content": "spark.read.table(\"farm_soil_moisture\").select(\"id\", \"amt\").write.insertInto(\"species\")\n", "labels": {"reads": [{"table": "farm_soil_moisture", "columns": null}], "writes": [{"table": "species", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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.paper_id > 177).all()\n# src table: graduatestudents\nengine.execute(\"INSERT INTO retail SELECT * FROM graduatestudents\")\n", "labels": {"reads": [{"table": "graduatestudents", "columns": null}], "writes": [{"table": "retail", "columns": null}]}, "meta": {"template_id": "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": "@pipeline(reads=\"stg.stg_exposure_df\", writes=\"stg_sessions_daily\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "stg.stg_exposure_df", "columns": null}], "writes": [{"table": "stg_sessions_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": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"taxi_occupancy\", limit=5000)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "taxi_occupancy", "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(\"dw.campaigns\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"public_buildings\")\n", "labels": {"reads": [{"table": "dw.campaigns", "columns": null}], "writes": [{"table": "public_buildings", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"dw.campaigns\", limit=5000)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\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": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO climate SELECT sample_date, restypedescription, card_id FROM fairtradeorders WHERE sample_date > 369\"\n", "labels": {"reads": [{"table": "fairtradeorders", "columns": ["sample_date", "restypedescription", "card_id"]}], "writes": [{"table": "climate", "columns": ["sample_date", "restypedescription", "card_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM healthcare_access\"\n", "labels": {"reads": [{"table": "healthcare_access", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO project_info SELECT num_schools, fuelid FROM content WHERE num_schools > 386\"\n", "labels": {"reads": [{"table": "content", "columns": ["num_schools", "fuelid"]}], "writes": [{"table": "project_info", "columns": ["num_schools", "fuelid"]}]}, "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 marine_protected_areas SELECT resolution, birth_date FROM supplier_products WHERE resolution > 490\"\n", "labels": {"reads": [{"table": "supplier_products", "columns": ["resolution", "birth_date"]}], "writes": [{"table": "marine_protected_areas", "columns": ["resolution", "birth_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": "Repo.of(\"eco_accommodations\").select([\"id\", \"amt\"]).copy_into(\"ads.ads_campaigns_delta\").commit()\n", "labels": {"reads": [{"table": "eco_accommodations", "columns": null}], "writes": [{"table": "ads.ads_campaigns_delta", "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 sessions depends on likes\ndbt run --select sessions --vars '{\"src\":\"likes\"}'\n", "labels": {"reads": [{"table": "likes", "columns": null}], "writes": [{"table": "sessions", "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 sustainablepractices SELECT transaction_id, visit_details, num_solo_exhibitions FROM non_profit_employees WHERE transaction_id > 252\"], check=True)\n", "labels": {"reads": [{"table": "non_profit_employees", "columns": ["transaction_id", "visit_details", "num_solo_exhibitions"]}], "writes": [{"table": "sustainablepractices", "columns": ["transaction_id", "visit_details", "num_solo_exhibitions"]}]}, "meta": {"template_id": "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": "SCALA", "content": "val df = spark.table(\"tour_types\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dws_shipments\")\n", "labels": {"reads": [{"table": "tour_types", "columns": null}], "writes": [{"table": "dws_shipments", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "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 stg_sessions_daily SELECT pettype, ycard, claim_id, ironid FROM dwd.device_log_daily WHERE pettype > 404\"))\n", "labels": {"reads": [{"table": "dwd.device_log_daily", "columns": ["pettype", "ycard", "claim_id", "ironid"]}], "writes": [{"table": "stg_sessions_daily", "columns": ["pettype", "ycard", "claim_id", "ironid"]}]}, "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=\"mart.sessions_hourly\", writes=\"buses\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "mart.sessions_hourly", "columns": null}], "writes": [{"table": "buses", "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(\"game\").toPandas()\ndf[[\"sid\", \"plantlocation\"]].to_sql(\"dws_refunds_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "game", "columns": null}], "writes": [{"table": "dws_refunds_daily", "columns": ["sid", "plantlocation"]}]}, "meta": {"template_id": "py-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": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM manufacturer_sales\"\n", "labels": {"reads": [{"table": "manufacturer_sales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO non_profit_employees SELECT sodium, votes, application_date FROM museum_artists WHERE sodium > 21\"))\n", "labels": {"reads": [{"table": "museum_artists", "columns": ["sodium", "votes", "application_date"]}], "writes": [{"table": "non_profit_employees", "columns": ["sodium", "votes", "application_date"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "val df = spark.table(\"instruments\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "instruments", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "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')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO mobile_subscribers SELECT a.personnelid, b.fan_name FROM playlists a JOIN climate_change_impacts_arctic_ocean b ON a.satelliteid = b.satelliteid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "playlists", "columns": null}, {"table": "climate_change_impacts_arctic_ocean", "columns": null}], "writes": [{"table": "mobile_subscribers", "columns": null}]}, "meta": {"template_id": "py-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": "psql \"$DB_URL\" <<SQL\nSELECT transit_passengers, supplierid FROM language_projects LIMIT 39;\nINSERT INTO europe_org SELECT data_usage, service_type_code FROM dws_refunds_daily WHERE data_usage > 195;\nSQL\n", "labels": {"reads": [{"table": "language_projects", "columns": ["transit_passengers", "supplierid"]}, {"table": "dws_refunds_daily", "columns": ["data_usage", "service_type_code"]}], "writes": [{"table": "europe_org", "columns": ["data_usage", "service_type_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO claim_headers SELECT fanid, shares, cust_id FROM furniture_manufacte WHERE fanid > 376\"\n", "labels": {"reads": [{"table": "furniture_manufacte", "columns": ["fanid", "shares", "cust_id"]}], "writes": [{"table": "claim_headers", "columns": ["fanid", "shares", "cust_id"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"gcc_shariah_financing\", writes=\"heritage_tours\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "gcc_shariah_financing", "columns": null}], "writes": [{"table": "heritage_tours", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT workout_date, budgetid FROM stg_sessions_daily LIMIT 265;\nINSERT INTO gas_production SELECT workeridentity, eco_friendly FROM ads.ads_member_point_delta WHERE workeridentity > 172;\nSQL\n", "labels": {"reads": [{"table": "stg_sessions_daily", "columns": ["workout_date", "budgetid"]}, {"table": "ads.ads_member_point_delta", "columns": ["workeridentity", "eco_friendly"]}], "writes": [{"table": "gas_production", "columns": ["workeridentity", "eco_friendly"]}]}, "meta": {"template_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": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.stg_products_hourly SELECT a.customer_address, b.sale_country FROM smart_cities.ev_charging_stations a JOIN unique_donors b ON a.sea = b.sea\"\n", "labels": {"reads": [{"table": "smart_cities.ev_charging_stations", "columns": null}, {"table": "unique_donors", "columns": null}], "writes": [{"table": "stg.stg_products_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO aircraft_manufacturer SELECT inspection_time, animal, funding_round_id FROM satellite_launches WHERE inspection_time > 120\");\n", "labels": {"reads": [{"table": "satellite_launches", "columns": ["inspection_time", "animal", "funding_round_id"]}], "writes": [{"table": "aircraft_manufacturer", "columns": ["inspection_time", "animal", "funding_round_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO lanthanummines SELECT deliveryid, volunteer_hours FROM country_production WHERE deliveryid > 46\"\n", "labels": {"reads": [{"table": "country_production", "columns": ["deliveryid", "volunteer_hours"]}], "writes": [{"table": "lanthanummines", "columns": ["deliveryid", "volunteer_hours"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table tour_types --columns reported,request_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "tour_types", "columns": ["reported", "request_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": "frame = warehouse_client.fetch(table=\"company_data\", limit=5000)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "company_data", "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(\"recyclingamount\").toPandas()\ndf[[\"market_value_in_billion\", \"complaintid\"]].to_sql(\"document_sections\", engine, index=False)\n", "labels": {"reads": [{"table": "recyclingamount", "columns": null}], "writes": [{"table": "document_sections", "columns": ["market_value_in_billion", "complaintid"]}]}, "meta": {"template_id": "py-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": "clickhouse-client --host ch01 --query \"INSERT INTO artist_genre SELECT chair_name, section_id FROM arctic_vessels WHERE chair_name > 336\"\n", "labels": {"reads": [{"table": "arctic_vessels", "columns": ["chair_name", "section_id"]}], "writes": [{"table": "artist_genre", "columns": ["chair_name", "section_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": "JAVA", "content": "tableEnv.from(\"funds\").executeInsert(\"satellite_images\");\n", "labels": {"reads": [{"table": "funds", "columns": null}], "writes": [{"table": "satellite_images", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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=\"lifelong_learning\", limit=5000)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "lifelong_learning", "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": "msg = \"would run: INSERT INTO tourism_impact SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\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": "cur.execute(\"SELECT pieces, season FROM fleet_vessels LIMIT 299\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "fleet_vessels", "columns": ["pieces", "season"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO services SELECT bioprocess_name, incident_name, code, product_id FROM inspections_tx WHERE bioprocess_name > 324\"))\n", "labels": {"reads": [{"table": "inspections_tx", "columns": ["bioprocess_name", "incident_name", "code", "product_id"]}], "writes": [{"table": "services", "columns": ["bioprocess_name", "incident_name", "code", "product_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": "clickhouse-client --host ch01 --query \"INSERT INTO products_for_hire SELECT museumid, apt_type_code, contract_start_date, max_dissolved_oxygen FROM students_in_detention WHERE museumid > 342\"\n", "labels": {"reads": [{"table": "students_in_detention", "columns": ["museumid", "apt_type_code", "contract_start_date", "max_dissolved_oxygen"]}], "writes": [{"table": "products_for_hire", "columns": ["museumid", "apt_type_code", "contract_start_date", "max_dissolved_oxygen"]}]}, "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_sessions_df\").toPandas()\ndf[[\"post_category\", \"class_code\"]].to_sql(\"crane\", engine, index=False)\n", "labels": {"reads": [{"table": "dws.dws_sessions_df", "columns": null}], "writes": [{"table": "crane", "columns": ["post_category", "class_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": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO launches SELECT wage, dates_active, wifi FROM residents WHERE wage > 173;\nEOF\n", "labels": {"reads": [{"table": "residents", "columns": ["wage", "dates_active", "wifi"]}], "writes": [{"table": "launches", "columns": ["wage", "dates_active", "wifi"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO cultural_sites SELECT accessible, swimmer_id, hosts, treatment_name FROM community_development WHERE accessible > 403\"\n", "labels": {"reads": [{"table": "community_development", "columns": ["accessible", "swimmer_id", "hosts", "treatment_name"]}], "writes": [{"table": "cultural_sites", "columns": ["accessible", "swimmer_id", "hosts", "treatment_name"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table claim_3 --columns budget_type_description,park_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "claim_3", "columns": ["budget_type_description", "park_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"ai_adoption\", limit=5000)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "ai_adoption", "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": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO energy_efficiency_stats SELECT feb, users_engaged FROM threat_actors WHERE feb > 233\");\n", "labels": {"reads": [{"table": "threat_actors", "columns": ["feb", "users_engaged"]}], "writes": [{"table": "energy_efficiency_stats", "columns": ["feb", "users_engaged"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"funding\").select([\"id\", \"amt\"]).copy_into(\"stg.stg_refunds\").commit()\n", "labels": {"reads": [{"table": "funding", "columns": null}], "writes": [{"table": "stg.stg_refunds", "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 graduatestudents SELECT itemname, digital FROM jeans_sales WHERE itemname > 344\"\n", "labels": {"reads": [{"table": "jeans_sales", "columns": ["itemname", "digital"]}], "writes": [{"table": "graduatestudents", "columns": ["itemname", "digital"]}]}, "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": "cur.execute(\"SELECT daily_visitors, document_date FROM job_history LIMIT 313\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "job_history", "columns": ["daily_visitors", "document_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"bi.cart_item\", limit=5000)\nimport logging\nresult = value * ratio + offset\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": "clickhouse-client --host ch01 --query \"INSERT INTO hospital_beds SELECT campaign_name, memory_in_g, item_price, post_date FROM launches WHERE campaign_name > 238\"\n", "labels": {"reads": [{"table": "launches", "columns": ["campaign_name", "memory_in_g", "item_price", "post_date"]}], "writes": [{"table": "hospital_beds", "columns": ["campaign_name", "memory_in_g", "item_price", "post_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": "SCALA", "content": "spark.read.table(\"launches\").select(\"id\", \"amt\").write.insertInto(\"trip_segments\")\n", "labels": {"reads": [{"table": "launches", "columns": null}], "writes": [{"table": "trip_segments", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO stg.stg_refunds SELECT a.servicename, b.incident_count FROM sustainable_urbanism a JOIN social_impact_scores b ON a.fuelid = b.fuelid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "sustainable_urbanism", "columns": null}, {"table": "social_impact_scores", "columns": null}], "writes": [{"table": "stg.stg_refunds", "columns": null}]}, "meta": {"template_id": "py-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 user_check_ins SELECT serving_size, session_date FROM category WHERE serving_size > 306\"\n", "labels": {"reads": [{"table": "category", "columns": ["serving_size", "session_date"]}], "writes": [{"table": "user_check_ins", "columns": ["serving_size", "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": "impala-shell -i impalad01 -q \"INSERT INTO club_rank SELECT end_time, product_price, network, statement_details FROM ods.shipments_daily WHERE end_time > 320\"\n", "labels": {"reads": [{"table": "ods.shipments_daily", "columns": ["end_time", "product_price", "network", "statement_details"]}], "writes": [{"table": "club_rank", "columns": ["end_time", "product_price", "network", "statement_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": "SCALA", "content": "spark.table(\"classes\").where(\"dt = current_date()\").writeTo(\"citydata\").append()\n", "labels": {"reads": [{"table": "classes", "columns": null}], "writes": [{"table": "citydata", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"dws_refunds_daily\").executeInsert(\"claims_processing\");\n", "labels": {"reads": [{"table": "dws_refunds_daily", "columns": null}], "writes": [{"table": "claims_processing", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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_intelligence_data\", limit=5000)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "threat_intelligence_data", "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 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-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": "JAVA", "content": "tableEnv.from(\"sales_data\").executeInsert(\"ethical_ai_courses_year\");\n", "labels": {"reads": [{"table": "sales_data", "columns": null}], "writes": [{"table": "ethical_ai_courses_year", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 addresses SELECT a.stu_fname, b.incorporated_in FROM donors a JOIN salesrevenue b ON a.join_date = b.join_date\"\n", "labels": {"reads": [{"table": "donors", "columns": null}, {"table": "salesrevenue", "columns": null}], "writes": [{"table": "addresses", "columns": null}]}, "meta": {"template_id": "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(\"dws.dws_sessions_df\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws.dws_sessions_df", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model ads_exposure_daily depends on mobile_subscribers_roaming\ndbt run --select ads_exposure_daily --vars '{\"src\":\"mobile_subscribers_roaming\"}'\n", "labels": {"reads": [{"table": "mobile_subscribers_roaming", "columns": null}], "writes": [{"table": "ads_exposure_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": "@pipeline(reads=\"document_drafts\", writes=\"threat_intelligence_data\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "document_drafts", "columns": null}], "writes": [{"table": "threat_intelligence_data", "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": "SCALA", "content": "spark.read.table(\"ads.exposure_daily\").select(\"id\", \"amt\").write.insertInto(\"donationdates\")\n", "labels": {"reads": [{"table": "ads.exposure_daily", "columns": null}], "writes": [{"table": "donationdates", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"bi.bi_orders\", writes=\"meetings\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "bi.bi_orders", "columns": null}], "writes": [{"table": "meetings", "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 menus SELECT route_short_name, fault_log_entry_datetime FROM micro_mobility WHERE route_short_name > 492\"\n", "labels": {"reads": [{"table": "micro_mobility", "columns": ["route_short_name", "fault_log_entry_datetime"]}], "writes": [{"table": "menus", "columns": ["route_short_name", "fault_log_entry_datetime"]}]}, "meta": {"template_id": "sh-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": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO elections SELECT games, fare, cultural_competency_score FROM ticket_prices WHERE games > 188\")\n", "labels": {"reads": [{"table": "ticket_prices", "columns": ["games", "fare", "cultural_competency_score"]}], "writes": [{"table": "elections", "columns": ["games", "fare", "cultural_competency_score"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"customer_orders\").where(\"dt = current_date()\")\nevents.writeTo(\"farm_soil_moisture\").append()\n", "labels": {"reads": [{"table": "customer_orders", "columns": null}], "writes": [{"table": "farm_soil_moisture", "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": "JAVA", "content": "tableEnv.from(\"regionwildlifehabitats\").executeInsert(\"musicevents\");\n", "labels": {"reads": [{"table": "regionwildlifehabitats", "columns": null}], "writes": [{"table": "musicevents", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"mobile_subscribers\").select([\"id\", \"amt\"]).copy_into(\"startups\").commit()\n", "labels": {"reads": [{"table": "mobile_subscribers", "columns": null}], "writes": [{"table": "startups", "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 things SELECT card_type_code, image_name FROM renewable.projects WHERE card_type_code > 443\"\n", "labels": {"reads": [{"table": "renewable.projects", "columns": ["card_type_code", "image_name"]}], "writes": [{"table": "things", "columns": ["card_type_code", "image_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": "@pipeline(reads=\"affected_region\", writes=\"lawprograms\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "affected_region", "columns": null}], "writes": [{"table": "lawprograms", "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 vesseldocking SELECT is_eco_friendly, astronaut_id FROM mental_health_providers WHERE is_eco_friendly > 323\"\n", "labels": {"reads": [{"table": "mental_health_providers", "columns": ["is_eco_friendly", "astronaut_id"]}], "writes": [{"table": "vesseldocking", "columns": ["is_eco_friendly", "astronaut_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": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO safetyrecord SELECT a.marketing_region_name, b.product_type FROM member_workout_date a JOIN mentalhealthparity b ON a.host_id = b.host_id\"\n", "labels": {"reads": [{"table": "member_workout_date", "columns": null}, {"table": "mentalhealthparity", "columns": null}], "writes": [{"table": "safetyrecord", "columns": null}]}, "meta": {"template_id": "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": "Repo.of(\"endangered_species\").select([\"id\", \"amt\"]).copy_into(\"autonomous_taxis\").commit()\n", "labels": {"reads": [{"table": "endangered_species", "columns": null}], "writes": [{"table": "autonomous_taxis", "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 playlists SELECT stocking_density, park, author_community FROM transactions_lots WHERE stocking_density > 43\"\n", "labels": {"reads": [{"table": "transactions_lots", "columns": ["stocking_density", "park", "author_community"]}], "writes": [{"table": "playlists", "columns": ["stocking_density", "park", "author_community"]}]}, "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": "spark-sql --master yarn -e \"INSERT INTO artist_genre SELECT opened_date, products_last_year, retailer_name, num_employees FROM has_pet WHERE opened_date > 196\"\n", "labels": {"reads": [{"table": "has_pet", "columns": ["opened_date", "products_last_year", "retailer_name", "num_employees"]}], "writes": [{"table": "artist_genre", "columns": ["opened_date", "products_last_year", "retailer_name", "num_employees"]}]}, "meta": {"template_id": "sh-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": "SCALA", "content": "spark.read.table(\"lifelong_learning\").select(\"id\", \"amt\").write.insertInto(\"smartcitysavings\")\n", "labels": {"reads": [{"table": "lifelong_learning", "columns": null}], "writes": [{"table": "smartcitysavings", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"vendor\").executeInsert(\"dws.dws_exposure_df\");\n", "labels": {"reads": [{"table": "vendor", "columns": null}], "writes": [{"table": "dws.dws_exposure_df", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 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-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": "JAVA", "content": "tableEnv.from(\"billing\").executeInsert(\"sleep\");\n", "labels": {"reads": [{"table": "billing", "columns": null}], "writes": [{"table": "sleep", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 product_ingredients SELECT 1\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"lanthanummines\").select([\"id\", \"amt\"]).copy_into(\"graduatestudents\").commit()\n", "labels": {"reads": [{"table": "lanthanummines", "columns": null}], "writes": [{"table": "graduatestudents", "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": "SCALA", "content": "spark.read.table(\"ods.ods_shipments\").select(\"id\", \"amt\").write.insertInto(\"heritage_tours\")\n", "labels": {"reads": [{"table": "ods.ods_shipments", "columns": null}], "writes": [{"table": "heritage_tours", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"incident_regions\").where(\"dt = current_date()\")\nevents.writeTo(\"users\").append()\n", "labels": {"reads": [{"table": "incident_regions", "columns": null}], "writes": [{"table": "users", "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 fairness_reports SELECT sodium, sportname, audienceid FROM ferry_routes WHERE sodium > 317\"\n", "labels": {"reads": [{"table": "ferry_routes", "columns": ["sodium", "sportname", "audienceid"]}], "writes": [{"table": "fairness_reports", "columns": ["sodium", "sportname", "audienceid"]}]}, "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 invoice_id, restaurant_id FROM tourists LIMIT 395\")\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO donationdates SELECT payment_id, min_temperature_f, plan_id FROM daily_transactions WHERE payment_id > 115\")\n", "labels": {"reads": [{"table": "tourists", "columns": ["invoice_id", "restaurant_id"]}, {"table": "daily_transactions", "columns": ["payment_id", "min_temperature_f", "plan_id"]}], "writes": [{"table": "donationdates", "columns": ["payment_id", "min_temperature_f", "plan_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": "SHELL", "content": "# model graduatestudents depends on news_articles\ndbt run --select graduatestudents --vars '{\"src\":\"news_articles\"}'\n", "labels": {"reads": [{"table": "news_articles", "columns": null}], "writes": [{"table": "graduatestudents", "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": "SCALA", "content": "// legacy: INSERT INTO teacher_development SELECT * FROM legacy\nspark.sql(\"INSERT INTO artist_genre SELECT date_incident_start, attack_count, courtid FROM cargoships WHERE date_incident_start > 97\")\n", "labels": {"reads": [{"table": "cargoships", "columns": ["date_incident_start", "attack_count", "courtid"]}], "writes": [{"table": "artist_genre", "columns": ["date_incident_start", "attack_count", "courtid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT u_id, sculpture_name FROM billing LIMIT 364\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "billing", "columns": ["u_id", "sculpture_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO stadium SELECT calories, kids, coupon_amount, menuid FROM wastewaterplant WHERE calories > 108\")\n", "labels": {"reads": [{"table": "wastewaterplant", "columns": ["calories", "kids", "coupon_amount", "menuid"]}], "writes": [{"table": "stadium", "columns": ["calories", "kids", "coupon_amount", "menuid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "val df = spark.table(\"mental_health_facilities\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mental_health_facilities", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "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 language_preservation SELECT crime_type, resource_type, activity_type, media_outlet FROM airbus.flightsafetyrecords WHERE crime_type > 159\"], check=True)\n", "labels": {"reads": [{"table": "airbus.flightsafetyrecords", "columns": ["crime_type", "resource_type", "activity_type", "media_outlet"]}], "writes": [{"table": "language_preservation", "columns": ["crime_type", "resource_type", "activity_type", "media_outlet"]}]}, "meta": {"template_id": "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": "src = spark.read.table(\"worker_salaries\")\nsrc.write.insertInto(\"transactions\", overwrite=True)\n", "labels": {"reads": [{"table": "worker_salaries", "columns": null}], "writes": [{"table": "transactions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"dws_shipments\").where(\"dt = current_date()\")\nevents.writeTo(\"document_sections\").append()\n", "labels": {"reads": [{"table": "dws_shipments", "columns": null}], "writes": [{"table": "document_sections", "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(\"ads.ads_campaigns_delta\").select([\"id\", \"amt\"]).copy_into(\"dws.dws_orders_df\").commit()\n", "labels": {"reads": [{"table": "ads.ads_campaigns_delta", "columns": null}], "writes": [{"table": "dws.dws_orders_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 = pull_table(ctx, \"sleep\")\nsink_to_store(df, \"treatment_type\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "sleep", "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": "JAVA", "content": "tableEnv.from(\"intelligence_operations\").executeInsert(\"dw.dw_campaigns_df\");\n", "labels": {"reads": [{"table": "intelligence_operations", "columns": null}], "writes": [{"table": "dw.dw_campaigns_df", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"artdistribution\").select([\"id\", \"amt\"]).copy_into(\"organiccottongarments\").commit()\n", "labels": {"reads": [{"table": "artdistribution", "columns": null}], "writes": [{"table": "organiccottongarments", "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 treatment SELECT organisation_id, destination_state, location_code, manufacturer_name FROM production_quebec WHERE organisation_id > 453\"))\n", "labels": {"reads": [{"table": "production_quebec", "columns": ["organisation_id", "destination_state", "location_code", "manufacturer_name"]}], "writes": [{"table": "treatment", "columns": ["organisation_id", "destination_state", "location_code", "manufacturer_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": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO playlist_tracks SELECT project_type, incident_date FROM airports WHERE project_type > 29\")\n", "labels": {"reads": [{"table": "airports", "columns": ["project_type", "incident_date"]}], "writes": [{"table": "playlist_tracks", "columns": ["project_type", "incident_date"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "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_development.schools SELECT savings, production_quantity FROM ref_transaction_types WHERE savings > 7\"))\n", "labels": {"reads": [{"table": "ref_transaction_types", "columns": ["savings", "production_quantity"]}], "writes": [{"table": "community_development.schools", "columns": ["savings", "production_quantity"]}]}, "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 = pd.read_sql(\"SELECT * FROM medication\", conn)\ndf.to_sql(\"member_workout_date\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "medication", "columns": null}], "writes": [{"table": "member_workout_date", "columns": null}]}, "meta": {"template_id": "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": "JAVA", "content": "Dataset<Row> df = spark.table(\"ods.ods_events_delta\");\ndf.write().mode(\"overwrite\").saveAsTable(\"union_finance\");\n", "labels": {"reads": [{"table": "ods.ods_events_delta", "columns": null}], "writes": [{"table": "union_finance", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "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 launch, well_id FROM recyclingamount\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"eu_ets\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "recyclingamount", "columns": ["launch", "well_id"]}], "writes": [{"table": "eu_ets", "columns": null}]}, "meta": {"template_id": "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": "df = spark.read.table(\"attorneys\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"arctic_vessels\")\n", "labels": {"reads": [{"table": "attorneys", "columns": null}], "writes": [{"table": "arctic_vessels", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM defenseagreements\", conn)\ndf.to_sql(\"enroll\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "defenseagreements", "columns": null}], "writes": [{"table": "enroll", "columns": null}]}, "meta": {"template_id": "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": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO supplierfabric SELECT runtime, countid, no_of_customers FROM cargo WHERE runtime > 406;\nEOF\n", "labels": {"reads": [{"table": "cargo", "columns": ["runtime", "countid", "no_of_customers"]}], "writes": [{"table": "supplierfabric", "columns": ["runtime", "countid", "no_of_customers"]}]}, "meta": {"template_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(\"cargoships\").where(\"dt = current_date()\")\nevents.writeTo(\"autonomous_taxis\").append()\n", "labels": {"reads": [{"table": "cargoships", "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": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO dws.products_daily SELECT guest_id, initiativeid, transit_passengers FROM unique_donors WHERE guest_id > 312\"\n", "labels": {"reads": [{"table": "unique_donors", "columns": ["guest_id", "initiativeid", "transit_passengers"]}], "writes": [{"table": "dws.products_daily", "columns": ["guest_id", "initiativeid", "transit_passengers"]}]}, "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 healthcare_providers depends on lawprograms\ndbt run --select healthcare_providers --vars '{\"src\":\"lawprograms\"}'\n", "labels": {"reads": [{"table": "lawprograms", "columns": null}], "writes": [{"table": "healthcare_providers", "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 military_equipment_sales SELECT hispanic, treatment, attribute_id FROM project_budget WHERE hispanic > 410\"))\n", "labels": {"reads": [{"table": "project_budget", "columns": ["hispanic", "treatment", "attribute_id"]}], "writes": [{"table": "military_equipment_sales", "columns": ["hispanic", "treatment", "attribute_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=\"wastewaterplant\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "wastewaterplant", "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(\"club_rank\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"brand_scores\")\n", "labels": {"reads": [{"table": "club_rank", "columns": null}], "writes": [{"table": "brand_scores", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"delivery_routes\")\nsrc.write.insertInto(\"performance\", overwrite=True)\n", "labels": {"reads": [{"table": "delivery_routes", "columns": null}], "writes": [{"table": "performance", "columns": null}]}, "meta": {"template_id": "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(\"dw.dw_refunds_delta\").select([\"id\", \"amt\"]).copy_into(\"elections\").commit()\n", "labels": {"reads": [{"table": "dw.dw_refunds_delta", "columns": null}], "writes": [{"table": "elections", "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": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO techniques SELECT student_id, port, donorage FROM agroecology WHERE student_id > 257\");\n", "labels": {"reads": [{"table": "agroecology", "columns": ["student_id", "port", "donorage"]}], "writes": [{"table": "techniques", "columns": ["student_id", "port", "donorage"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "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 $TBL SELECT * FROM threat_actors\"\n", "labels": {"reads": [{"table": "threat_actors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"content\").where(\"dt = current_date()\")\nevents.writeTo(\"memberships\").append()\n", "labels": {"reads": [{"table": "content", "columns": null}], "writes": [{"table": "memberships", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT assessmentid, bedtype FROM funds LIMIT 464;\nINSERT INTO claim_3 SELECT is_recycled, vin, fundingagency, brand FROM union_finance WHERE is_recycled > 404;\nSQL\n", "labels": {"reads": [{"table": "funds", "columns": ["assessmentid", "bedtype"]}, {"table": "union_finance", "columns": ["is_recycled", "vin", "fundingagency", "brand"]}], "writes": [{"table": "claim_3", "columns": ["is_recycled", "vin", "fundingagency", "brand"]}]}, "meta": {"template_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(\"games\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dw_clicks_hourly\")\n", "labels": {"reads": [{"table": "games", "columns": null}], "writes": [{"table": "dw_clicks_hourly", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO ods.ods_shipments SELECT ranking, home_team_id, method_id, hispanic FROM green_vehicles WHERE ranking > 369\"\n", "labels": {"reads": [{"table": "green_vehicles", "columns": ["ranking", "home_team_id", "method_id", "hispanic"]}], "writes": [{"table": "ods.ods_shipments", "columns": ["ranking", "home_team_id", "method_id", "hispanic"]}]}, "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(\"bi.products_hourly\").select([\"id\", \"amt\"]).copy_into(\"ingredient_sources\").commit()\n", "labels": {"reads": [{"table": "bi.products_hourly", "columns": null}], "writes": [{"table": "ingredient_sources", "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 therapeutic_areas depends on donationdates\ndbt run --select therapeutic_areas --vars '{\"src\":\"donationdates\"}'\n", "labels": {"reads": [{"table": "donationdates", "columns": null}], "writes": [{"table": "therapeutic_areas", "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": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO medication (contract_name, lot_details) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "medication", "columns": ["contract_name", "lot_details"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"content\").select(\"id\", \"amt\").write.insertInto(\"salesrevenue\")\n", "labels": {"reads": [{"table": "content", "columns": null}], "writes": [{"table": "salesrevenue", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model jobopenings depends on arts_culture.programs\ndbt run --select jobopenings --vars '{\"src\":\"arts_culture.programs\"}'\n", "labels": {"reads": [{"table": "arts_culture.programs", "columns": null}], "writes": [{"table": "jobopenings", "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 marine_trenches SELECT bedroom_count, authid, initiativeid, operationid FROM oregondispensaries WHERE bedroom_count > 337\"))\n", "labels": {"reads": [{"table": "oregondispensaries", "columns": ["bedroom_count", "authid", "initiativeid", "operationid"]}], "writes": [{"table": "marine_trenches", "columns": ["bedroom_count", "authid", "initiativeid", "operationid"]}]}, "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": "set -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table visitors --target-dir /tmp/land\n", "labels": {"reads": [{"table": "visitors", "columns": null}], "writes": []}, "meta": {"template_id": "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": "# model ethereum_contracts depends on csu_fees\ndbt run --select ethereum_contracts --vars '{\"src\":\"csu_fees\"}'\n", "labels": {"reads": [{"table": "csu_fees", "columns": null}], "writes": [{"table": "ethereum_contracts", "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=\"water_treatment_plant_upgrades\", writes=\"users\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "water_treatment_plant_upgrades", "columns": null}], "writes": [{"table": "users", "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 oregondispensaries SELECT gameid, task_details, vulnerability FROM georgia_rural_residents WHERE gameid > 128\"\n", "labels": {"reads": [{"table": "georgia_rural_residents", "columns": ["gameid", "task_details", "vulnerability"]}], "writes": [{"table": "oregondispensaries", "columns": ["gameid", "task_details", "vulnerability"]}]}, "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_company_revenue\", writes=\"worker\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "mining_company_revenue", "columns": null}], "writes": [{"table": "worker", "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": "JAVA", "content": "tableEnv.from(\"visitor_demographics\").executeInsert(\"users\");\n", "labels": {"reads": [{"table": "visitor_demographics", "columns": null}], "writes": [{"table": "users", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.table(\"salaries\").where(\"dt = current_date()\").writeTo(\"gold_mines\").append()\n", "labels": {"reads": [{"table": "salaries", "columns": null}], "writes": [{"table": "gold_mines", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "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 dental_clinics --columns workout_name,deliveryaddress --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dental_clinics", "columns": ["workout_name", "deliveryaddress"]}]}, "meta": {"template_id": "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": "impala-shell -i impalad01 -q \"INSERT INTO military_equipment_sales SELECT ei_value, enable_third_party_ads FROM bi.bi_coupon_use_di WHERE ei_value > 211\"\n", "labels": {"reads": [{"table": "bi.bi_coupon_use_di", "columns": ["ei_value", "enable_third_party_ads"]}], "writes": [{"table": "military_equipment_sales", "columns": ["ei_value", "enable_third_party_ads"]}]}, "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": "events = spark.table(\"marine_protected_areas\").where(\"dt = current_date()\")\nevents.writeTo(\"dw.dw_campaigns_df\").append()\n", "labels": {"reads": [{"table": "marine_protected_areas", "columns": null}], "writes": [{"table": "dw.dw_campaigns_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": "df = spark.read.table(\"dws_shipments\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"member_demographics\")\n", "labels": {"reads": [{"table": "dws_shipments", "columns": null}], "writes": [{"table": "member_demographics", "columns": null}]}, "meta": {"template_id": "py-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": "sqoop export --connect \"$JDBC\" --table taxi_occupancy --columns donortype,dname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "taxi_occupancy", "columns": ["donortype", "dname"]}]}, "meta": {"template_id": "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 dw.dw_campaigns_df SELECT advocate_name, contractid, deliverydate, number_thousands FROM ipl_runs WHERE advocate_name > 63\"\n", "labels": {"reads": [{"table": "ipl_runs", "columns": ["advocate_name", "contractid", "deliverydate", "number_thousands"]}], "writes": [{"table": "dw.dw_campaigns_df", "columns": ["advocate_name", "contractid", "deliverydate", "number_thousands"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO product_ingredients SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO skincare_ingredients SELECT word_count, unsure_rate, budget, show_name FROM skills_required_to_fix WHERE word_count > 385\"\n", "labels": {"reads": [{"table": "skills_required_to_fix", "columns": ["word_count", "unsure_rate", "budget", "show_name"]}], "writes": [{"table": "skincare_ingredients", "columns": ["word_count", "unsure_rate", "budget", "show_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO treatment SELECT a.lastname, b.fairtrade FROM aquaculture_sites a JOIN lanthanumshipments b ON a.tech = b.tech\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "aquaculture_sites", "columns": null}, {"table": "lanthanumshipments", "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": "JAVA", "content": "tableEnv.from(\"mobile_subscribers_roaming\").executeInsert(\"privacy_settings\");\n", "labels": {"reads": [{"table": "mobile_subscribers_roaming", "columns": null}], "writes": [{"table": "privacy_settings", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"therapeutic_areas\").where(\"dt = current_date()\")\nevents.writeTo(\"casesattorneys\").append()\n", "labels": {"reads": [{"table": "therapeutic_areas", "columns": null}], "writes": [{"table": "casesattorneys", "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": "if not rows:\n    logger.warning('empty result')\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO peacekeeping_operations SELECT area_size, end_speed FROM sales.games WHERE area_size > 186\"\n", "labels": {"reads": [{"table": "sales.games", "columns": ["area_size", "end_speed"]}], "writes": [{"table": "peacekeeping_operations", "columns": ["area_size", "end_speed"]}]}, "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": "SCALA", "content": "spark.read.table(\"treatment_type\").select(\"id\", \"amt\").write.insertInto(\"dw.dw_refunds_delta\")\n", "labels": {"reads": [{"table": "treatment_type", "columns": null}], "writes": [{"table": "dw.dw_refunds_delta", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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_transaction_types SELECT individual_id, representative_name, stars FROM studentsmentalhealth WHERE individual_id > 260\"\n", "labels": {"reads": [{"table": "studentsmentalhealth", "columns": ["individual_id", "representative_name", "stars"]}], "writes": [{"table": "ref_transaction_types", "columns": ["individual_id", "representative_name", "stars"]}]}, "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": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"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": "py-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 students_disabilities SELECT hospitalid, rid, watertemp FROM ref_document_types WHERE hospitalid > 151\"\n", "labels": {"reads": [{"table": "ref_document_types", "columns": ["hospitalid", "rid", "watertemp"]}], "writes": [{"table": "students_disabilities", "columns": ["hospitalid", "rid", "watertemp"]}]}, "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=\"company_data\", limit=5000)\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "company_data", "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": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO debate_people SELECT therapy_date, round_number, dependent_name, platform_id FROM claim_headers WHERE therapy_date > 228\");\n", "labels": {"reads": [{"table": "claim_headers", "columns": ["therapy_date", "round_number", "dependent_name", "platform_id"]}], "writes": [{"table": "debate_people", "columns": ["therapy_date", "round_number", "dependent_name", "platform_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "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 detention_type_description, sport FROM tech_companies LIMIT 190;\nINSERT INTO mature_forest SELECT settlement_amount, area_ha FROM church WHERE settlement_amount > 290;\nSQL\n", "labels": {"reads": [{"table": "tech_companies", "columns": ["detention_type_description", "sport"]}, {"table": "church", "columns": ["settlement_amount", "area_ha"]}], "writes": [{"table": "mature_forest", "columns": ["settlement_amount", "area_ha"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT activity_type, fuelid FROM ads.exposure_daily LIMIT 288\")\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO things SELECT farmer_id, restaurant_name FROM mature_forest WHERE farmer_id > 50\")\n", "labels": {"reads": [{"table": "ads.exposure_daily", "columns": ["activity_type", "fuelid"]}, {"table": "mature_forest", "columns": ["farmer_id", "restaurant_name"]}], "writes": [{"table": "things", "columns": ["farmer_id", "restaurant_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model dws_cart_item_di depends on ads.ads_cart_item_di\ndbt run --select dws_cart_item_di --vars '{\"src\":\"ads.ads_cart_item_di\"}'\n", "labels": {"reads": [{"table": "ads.ads_cart_item_di", "columns": null}], "writes": [{"table": "dws_cart_item_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": "df = spark.read.table(\"buildingtypes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"student_course_enrolment\")\n", "labels": {"reads": [{"table": "buildingtypes", "columns": null}], "writes": [{"table": "student_course_enrolment", "columns": null}]}, "meta": {"template_id": "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=\"smartcitysavings\", limit=5000)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "smartcitysavings", "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(\"carriers\").where(\"dt = current_date()\")\nevents.writeTo(\"micro_mobility\").append()\n", "labels": {"reads": [{"table": "carriers", "columns": null}], "writes": [{"table": "micro_mobility", "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": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO micro_mobility (sport, license_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "micro_mobility", "columns": ["sport", "license_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM national_security_agencies\", conn)\ndf.to_sql(\"biotechstartupfunding\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "national_security_agencies", "columns": null}], "writes": [{"table": "biotechstartupfunding", "columns": null}]}, "meta": {"template_id": "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": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO donationcategories SELECT ride_id, sustainability_score FROM heritage_tours WHERE ride_id > 395\"))\n", "labels": {"reads": [{"table": "heritage_tours", "columns": ["ride_id", "sustainability_score"]}], "writes": [{"table": "donationcategories", "columns": ["ride_id", "sustainability_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": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO contractors SELECT exhibit_location, ngo_id, shipping_agent_code, daily_hire_cost FROM threat_intelligence_data WHERE exhibit_location > 168\"\n", "labels": {"reads": [{"table": "threat_intelligence_data", "columns": ["exhibit_location", "ngo_id", "shipping_agent_code", "daily_hire_cost"]}], "writes": [{"table": "contractors", "columns": ["exhibit_location", "ngo_id", "shipping_agent_code", "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": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO stg.stg_refunds SELECT check_in_id, satellite_id, trend FROM debate WHERE check_in_id > 391\")\n", "labels": {"reads": [{"table": "debate", "columns": ["check_in_id", "satellite_id", "trend"]}], "writes": [{"table": "stg.stg_refunds", "columns": ["check_in_id", "satellite_id", "trend"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "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=\"residential_buildings\", limit=5000)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "residential_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": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stg.stg_products_hourly (characteristic_type_code, shipment_year) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_products_hourly", "columns": ["characteristic_type_code", "shipment_year"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model project_info depends on flight\ndbt run --select project_info --vars '{\"src\":\"flight\"}'\n", "labels": {"reads": [{"table": "flight", "columns": null}], "writes": [{"table": "project_info", "columns": null}]}, "meta": {"template_id": "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=\"mature_forest\", writes=\"clientinvestments\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "mature_forest", "columns": null}], "writes": [{"table": "clientinvestments", "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(\"dws.exposure_hourly\").select([\"id\", \"amt\"]).copy_into(\"actor\").commit()\n", "labels": {"reads": [{"table": "dws.exposure_hourly", "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": "PYTHON", "content": "Repo.of(\"digitalexperiences\").select([\"id\", \"amt\"]).copy_into(\"user_check_ins\").commit()\n", "labels": {"reads": [{"table": "digitalexperiences", "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": "PYTHON", "content": "@pipeline(reads=\"population\", writes=\"humanitarianassistance\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "population", "columns": null}], "writes": [{"table": "humanitarianassistance", "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 organic_meals SELECT uses_vr, height_feet, semester, credit_score FROM mart.mart_cart_item_daily WHERE uses_vr > 141\"))\n", "labels": {"reads": [{"table": "mart.mart_cart_item_daily", "columns": ["uses_vr", "height_feet", "semester", "credit_score"]}], "writes": [{"table": "organic_meals", "columns": ["uses_vr", "height_feet", "semester", "credit_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=\"jeans_sales\", writes=\"eu_ets\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "jeans_sales", "columns": null}], "writes": [{"table": "eu_ets", "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(\"ods.ods_member_point\").where(\"dt = current_date()\")\nevents.writeTo(\"pollution_incidents\").append()\n", "labels": {"reads": [{"table": "ods.ods_member_point", "columns": null}], "writes": [{"table": "pollution_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": "PYTHON", "content": "df = spark.read.table(\"trend_popularity\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "trend_popularity", "columns": null}], "writes": []}, "meta": {"template_id": "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": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO studios (artist_gender, employee_address_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "studios", "columns": ["artist_gender", "employee_address_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": "df = pd.read_sql(\"SELECT environmental_impact_score, conservation_status FROM broadcast\", engine)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"gcc_shariah_financing\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "broadcast", "columns": ["environmental_impact_score", "conservation_status"]}], "writes": [{"table": "gcc_shariah_financing", "columns": null}]}, "meta": {"template_id": "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": "# TODO: 旧逻辑 INSERT INTO community_development.schools SELECT * FROM legacy\ncur.execute(\"SELECT change_date, software_platform FROM worker_salaries LIMIT 435\")\n", "labels": {"reads": [{"table": "worker_salaries", "columns": ["change_date", "software_platform"]}], "writes": []}, "meta": {"template_id": "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(\"citydata\").where(\"dt = current_date()\")\nevents.writeTo(\"residential_buildings\").append()\n", "labels": {"reads": [{"table": "citydata", "columns": null}], "writes": [{"table": "residential_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": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"criminal_database\", limit=5000)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "criminal_database", "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 activity, safety_record FROM studentsmentalhealth LIMIT 115\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO models SELECT dance_form, license_plate, schedule, label FROM elements WHERE dance_form > 2\")\n", "labels": {"reads": [{"table": "studentsmentalhealth", "columns": ["activity", "safety_record"]}, {"table": "elements", "columns": ["dance_form", "license_plate", "schedule", "label"]}], "writes": [{"table": "models", "columns": ["dance_form", "license_plate", "schedule", "label"]}]}, "meta": {"template_id": "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": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table product_ingredients --target-dir /tmp/land\n", "labels": {"reads": [{"table": "product_ingredients", "columns": null}], "writes": []}, "meta": {"template_id": "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": "impala-shell -i impalad01 -q \"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": "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=\"debate_people\", writes=\"healthcare_providers\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "debate_people", "columns": null}], "writes": [{"table": "healthcare_providers", "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=\"dwd.device_log_daily\", writes=\"donationdates\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "dwd.device_log_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": "SHELL", "content": "# model hotel_ai depends on well\ndbt run --select hotel_ai --vars '{\"src\":\"well\"}'\n", "labels": {"reads": [{"table": "well", "columns": null}], "writes": [{"table": "hotel_ai", "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 supplychain SELECT has_aloe_vera, investor_details, operation_count, business_zone FROM stg.stg_refunds WHERE has_aloe_vera > 12\"], check=True)\n", "labels": {"reads": [{"table": "stg.stg_refunds", "columns": ["has_aloe_vera", "investor_details", "operation_count", "business_zone"]}], "writes": [{"table": "supplychain", "columns": ["has_aloe_vera", "investor_details", "operation_count", "business_zone"]}]}, "meta": {"template_id": "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 ods.ods_device_log_df SELECT velocity, startup_name, funding_id, site_name FROM vulnerabilityassessments WHERE velocity > 365\"\n", "labels": {"reads": [{"table": "vulnerabilityassessments", "columns": ["velocity", "startup_name", "funding_id", "site_name"]}], "writes": [{"table": "ods.ods_device_log_df", "columns": ["velocity", "startup_name", "funding_id", "site_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": "JAVA", "content": "tableEnv.from(\"carriers\").executeInsert(\"smartcitysavings\");\n", "labels": {"reads": [{"table": "carriers", "columns": null}], "writes": [{"table": "smartcitysavings", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 delivery_routes SELECT vesselid, city_code, animal_name, starting_year FROM clients WHERE vesselid > 274\"\n", "labels": {"reads": [{"table": "clients", "columns": ["vesselid", "city_code", "animal_name", "starting_year"]}], "writes": [{"table": "delivery_routes", "columns": ["vesselid", "city_code", "animal_name", "starting_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"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO gamerevenue SELECT volunteer_id, organization, coach_name FROM lanthanumshipments WHERE volunteer_id > 310\"\n", "labels": {"reads": [{"table": "lanthanumshipments", "columns": ["volunteer_id", "organization", "coach_name"]}], "writes": [{"table": "gamerevenue", "columns": ["volunteer_id", "organization", "coach_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": "events = spark.table(\"funds\").where(\"dt = current_date()\")\nevents.writeTo(\"cultural_sites\").append()\n", "labels": {"reads": [{"table": "funds", "columns": null}], "writes": [{"table": "cultural_sites", "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 renewable.projects SELECT artist_gender, date_in_locaton_to FROM vendor WHERE artist_gender > 352\"\n", "labels": {"reads": [{"table": "vendor", "columns": ["artist_gender", "date_in_locaton_to"]}], "writes": [{"table": "renewable.projects", "columns": ["artist_gender", "date_in_locaton_to"]}]}, "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": "events = spark.table(\"flu_vaccinations\").where(\"dt = current_date()\")\nevents.writeTo(\"media.reporters\").append()\n", "labels": {"reads": [{"table": "flu_vaccinations", "columns": null}], "writes": [{"table": "media.reporters", "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=\"crane\", limit=5000)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "crane", "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(\"dwd.dwd_risk_score\").select([\"id\", \"amt\"]).copy_into(\"policyholder\").commit()\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score", "columns": null}], "writes": [{"table": "policyholder", "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(\"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": "SCALA", "content": "val df = spark.table(\"criminal_database\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "criminal_database", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"dws.dws_refunds_full\").select(\"id\", \"amt\").write.insertInto(\"tour_types\")\n", "labels": {"reads": [{"table": "dws.dws_refunds_full", "columns": null}], "writes": [{"table": "tour_types", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"project_staff\")\nsrc.write.insertInto(\"case_assignments\", overwrite=True)\n", "labels": {"reads": [{"table": "project_staff", "columns": null}], "writes": [{"table": "case_assignments", "columns": null}]}, "meta": {"template_id": "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 wildlife_type_id, author_or_editor FROM debate_people\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"moviebudgets\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "debate_people", "columns": ["wildlife_type_id", "author_or_editor"]}], "writes": [{"table": "moviebudgets", "columns": null}]}, "meta": {"template_id": "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": "# 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": "PYTHON", "content": "Repo.of(\"endangered_species\").select([\"id\", \"amt\"]).copy_into(\"elements\").commit()\n", "labels": {"reads": [{"table": "endangered_species", "columns": null}], "writes": [{"table": "elements", "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(\"projects_pakistan\").select([\"id\", \"amt\"]).copy_into(\"stg.stg_exposure_df\").commit()\n", "labels": {"reads": [{"table": "projects_pakistan", "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": "frame = warehouse_client.fetch(table=\"studentsmentalhealth\", limit=5000)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "studentsmentalhealth", "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": "msg = \"would run: INSERT INTO student_records SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"green_vehicles\").select(\"id\", \"amt\").write.insertInto(\"policyholder\")\n", "labels": {"reads": [{"table": "green_vehicles", "columns": null}], "writes": [{"table": "policyholder", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"renewable_energy\").select(\"id\", \"amt\").write.insertInto(\"ocean_trenches\")\n", "labels": {"reads": [{"table": "renewable_energy", "columns": null}], "writes": [{"table": "ocean_trenches", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "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 healthcare_providers SELECT booked_amount, salary FROM funding WHERE booked_amount > 272\"\n", "labels": {"reads": [{"table": "funding", "columns": ["booked_amount", "salary"]}], "writes": [{"table": "healthcare_providers", "columns": ["booked_amount", "salary"]}]}, "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": "SCALA", "content": "spark.read.table(\"dw.campaigns\").select(\"id\", \"amt\").write.insertInto(\"images\")\n", "labels": {"reads": [{"table": "dw.campaigns", "columns": null}], "writes": [{"table": "images", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"project_staff\").where(\"dt = current_date()\")\nevents.writeTo(\"exoplanets\").append()\n", "labels": {"reads": [{"table": "project_staff", "columns": null}], "writes": [{"table": "exoplanets", "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 techniques SELECT customer_id, waste_id, follows_ethical_practices, audienceid FROM public_buses WHERE customer_id > 221\"\n", "labels": {"reads": [{"table": "public_buses", "columns": ["customer_id", "waste_id", "follows_ethical_practices", "audienceid"]}], "writes": [{"table": "techniques", "columns": ["customer_id", "waste_id", "follows_ethical_practices", "audienceid"]}]}, "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=\"resourcemanagement\", writes=\"user_check_ins\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "resourcemanagement", "columns": null}], "writes": [{"table": "user_check_ins", "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=\"project_staff\", limit=5000)\nimport logging\n", "labels": {"reads": [{"table": "project_staff", "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=\"trend_popularity\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "trend_popularity", "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": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ods.shipments_daily SELECT claim_outcome_code, servicename FROM education_aid WHERE claim_outcome_code > 140;\nEOF\n", "labels": {"reads": [{"table": "education_aid", "columns": ["claim_outcome_code", "servicename"]}], "writes": [{"table": "ods.shipments_daily", "columns": ["claim_outcome_code", "servicename"]}]}, "meta": {"template_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": "msg = \"would run: INSERT INTO cultural_tourists 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": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO aircraft_and_flight_hours SELECT mappinglength, founder FROM mental_health_providers WHERE mappinglength > 145\")\n", "labels": {"reads": [{"table": "mental_health_providers", "columns": ["mappinglength", "founder"]}], "writes": [{"table": "aircraft_and_flight_hours", "columns": ["mappinglength", "founder"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "JAVA", "content": "tableEnv.from(\"room\").executeInsert(\"ref_transaction_types\");\n", "labels": {"reads": [{"table": "room", "columns": null}], "writes": [{"table": "ref_transaction_types", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model plant_safety_protocols depends on customer_event_notes\ndbt run --select plant_safety_protocols --vars '{\"src\":\"customer_event_notes\"}'\n", "labels": {"reads": [{"table": "customer_event_notes", "columns": null}], "writes": [{"table": "plant_safety_protocols", "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(\"recipe\").where(\"dt = current_date()\")\nevents.writeTo(\"medical\").append()\n", "labels": {"reads": [{"table": "recipe", "columns": null}], "writes": [{"table": "medical", "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 vehicle_type, method_id FROM elections LIMIT 131\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "elections", "columns": ["vehicle_type", "method_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": "df = pd.read_sql(\"SELECT total_distance, is_vegan FROM mart_device_log_full\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"sessions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart_device_log_full", "columns": ["total_distance", "is_vegan"]}], "writes": [{"table": "sessions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO farm_soil_moisture SELECT access_count, billing_country, watertemp, order_item_id FROM dw.payments_full WHERE access_count > 156\"\n", "labels": {"reads": [{"table": "dw.payments_full", "columns": ["access_count", "billing_country", "watertemp", "order_item_id"]}], "writes": [{"table": "farm_soil_moisture", "columns": ["access_count", "billing_country", "watertemp", "order_item_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": "clickhouse-client --host ch01 --query \"INSERT INTO worker SELECT number_cities, astronaut_name FROM ref_document_types WHERE number_cities > 243\"\n", "labels": {"reads": [{"table": "ref_document_types", "columns": ["number_cities", "astronaut_name"]}], "writes": [{"table": "worker", "columns": ["number_cities", "astronaut_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": "df = spark.read.table(\"lifelong_learning\").toPandas()\ndf[[\"artwork_id\", \"material\"]].to_sql(\"type_of_restaurant\", engine, index=False)\n", "labels": {"reads": [{"table": "lifelong_learning", "columns": null}], "writes": [{"table": "type_of_restaurant", "columns": ["artwork_id", "material"]}]}, "meta": {"template_id": "py-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": "clickhouse-client --host ch01 --query \"INSERT INTO lending_initiatives SELECT staff_first_name, mean_temperature_f, area_name FROM culture_company WHERE staff_first_name > 385\"\n", "labels": {"reads": [{"table": "culture_company", "columns": ["staff_first_name", "mean_temperature_f", "area_name"]}], "writes": [{"table": "lending_initiatives", "columns": ["staff_first_name", "mean_temperature_f", "area_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": "# TODO: 旧逻辑 INSERT INTO worker_salaries SELECT * FROM legacy\ncur.execute(\"SELECT producerid, sustainability_initiative_id FROM dw.dw_vendors_hourly LIMIT 22\")\n", "labels": {"reads": [{"table": "dw.dw_vendors_hourly", "columns": ["producerid", "sustainability_initiative_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": "# model bi_vendors_daily depends on buildingtypes\ndbt run --select bi_vendors_daily --vars '{\"src\":\"buildingtypes\"}'\n", "labels": {"reads": [{"table": "buildingtypes", "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(\"workout_records\").where(\"dt = current_date()\")\nevents.writeTo(\"startup\").append()\n", "labels": {"reads": [{"table": "workout_records", "columns": null}], "writes": [{"table": "startup", "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": "JAVA", "content": "tableEnv.from(\"likes\").executeInsert(\"workout_records\");\n", "labels": {"reads": [{"table": "likes", "columns": null}], "writes": [{"table": "workout_records", "columns": null}]}, "meta": {"template_id": "h-java-flink-from-insert", "rule_covered": false, "form_family": "jvm", "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 delivery_routes (line_1, conferenceid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "delivery_routes", "columns": ["line_1", "conferenceid"]}]}, "meta": {"template_id": "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": "SCALA", "content": "spark.read.table(\"budgetallocation\").select(\"id\", \"amt\").write.insertInto(\"humanitarianassistance\")\n", "labels": {"reads": [{"table": "budgetallocation", "columns": null}], "writes": [{"table": "humanitarianassistance", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SCALA", "content": "spark.read.table(\"newssource\").select(\"id\", \"amt\").write.insertInto(\"non_profit_employees\")\n", "labels": {"reads": [{"table": "newssource", "columns": null}], "writes": [{"table": "non_profit_employees", "columns": null}]}, "meta": {"template_id": "h-scala-dataset-chain", "rule_covered": false, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model visitor_stats depends on property\ndbt run --select visitor_stats --vars '{\"src\":\"property\"}'\n", "labels": {"reads": [{"table": "property", "columns": null}], "writes": [{"table": "visitor_stats", "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=\"case_assignments\", writes=\"debate_people\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "case_assignments", "columns": null}], "writes": [{"table": "debate_people", "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": "SCALA", "content": "val df = spark.table(\"bi.bi_coupon_use_di\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"project_outcomes\")\n", "labels": {"reads": [{"table": "bi.bi_coupon_use_di", "columns": null}], "writes": [{"table": "project_outcomes", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"nurse\")\nexport_to_target(df, \"healthcare_access\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "nurse", "columns": null}], "writes": [{"table": "healthcare_access", "columns": null}]}, "meta": {"template_id": "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(\"public_buses\").select([\"id\", \"amt\"]).copy_into(\"veteranemployees\").commit()\n", "labels": {"reads": [{"table": "public_buses", "columns": null}], "writes": [{"table": "veteranemployees", "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 furniture_manufacte SELECT assets_billion, date_closed, device_id, rig_name FROM crane WHERE assets_billion > 371\"\n", "labels": {"reads": [{"table": "crane", "columns": ["assets_billion", "date_closed", "device_id", "rig_name"]}], "writes": [{"table": "furniture_manufacte", "columns": ["assets_billion", "date_closed", "device_id", "rig_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": "impala-shell -i impalad01 -q \"INSERT INTO urban_agriculture SELECT start, stationid FROM stg.stg_refunds_hourly WHERE start > 376\"\n", "labels": {"reads": [{"table": "stg.stg_refunds_hourly", "columns": ["start", "stationid"]}], "writes": [{"table": "urban_agriculture", "columns": ["start", "stationid"]}]}, "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=\"worker\", limit=5000)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "worker", "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 investigative_journalism (player, founded_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "investigative_journalism", "columns": ["player", "founded_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO ruralinfrastructure SELECT consider_rate, omim, time_month FROM oregondispensaries WHERE consider_rate > 10\")\n", "labels": {"reads": [{"table": "oregondispensaries", "columns": ["consider_rate", "omim", "time_month"]}], "writes": [{"table": "ruralinfrastructure", "columns": ["consider_rate", "omim", "time_month"]}]}, "meta": {"template_id": "py-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"}}