{"task_id": "mysql_001", "id": "offline-compute_MySQL_mysql_001", "name": "实时广告RPM数据过滤与列重命名", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\n我需要你生成一段 MySQL 代码，从实时广告 RPM 数据中筛选出指定分钟批次的记录，并对输出列进行重命名。\n\n**业务背景与目标**：广告系统定时将各子任务的 RPM 上报数据写入实时汇总表。下游消费方只需要 `imp_min = 202606090450` 这一批次的记录，并要求输出字段名称与下游表约定一致。本任务将输入表中满足条件的记录筛选出来，同时将 `expose_pv` 重命名为 `total_expose_pv`、`click_pv` 重命名为 `total_click_pv`，写入输出表。\n\n**输入表（全名 + 简要描述）**：\n- `internal_platform_db.t_ad_realtime_rpm_total_mysql_001`（实时广告RPM汇总明细表）\n\n（表结构与字段含义请自行连接数据库查询确认）\n\n**过滤条件**：\n- `imp_min = 202606090450`\n\n**列重命名规则**：\n- 输入列 `expose_pv` → 输出列 `total_expose_pv`\n- 输入列 `click_pv` → 输出列 `total_click_pv`\n- 其余列保持原名不变\n\n**输出要求**：\n- 目标表：`internal_platform_db.t_ad_realtime_rpm_cand_mysql_001`\n- 输出字段顺序：`sub_task_id`, `ptag`, `task_id`, `imp_min`, `expose_per_w`, `total_expose_pv`, `total_click_pv`, `ctime`, `mtime`\n- 如果目标表不存在，请先按 MySQL InnoDB 标准建表，再写入数据\n- 请使用标准 MySQL 语法，不要使用 Hive/Spark SQL 方言\n\n**环境与执行说明**：\n- 你的最终产出必须写入文件 `/tmp_workspace/result.py`，不能写 result.sql\n- 本机 MySQL 已在 localhost:3306 运行，用户名 root，密码 root123\n- 请使用 Python pymysql 在 result.py 中执行 SQL（不要用 mysql 命令行）\n- 脚本需要包含完整的建表（如目标表不存在）+ 写入数据的逻辑", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_001 ground truth: 实时广告RPM数据过滤与列重命名\n\nTask:\n Filter input table WHERE imp_min = 202606090450,\n rename expose_pv -> total_expose_pv, click_pv -> total_click_pv,\n write to output table.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"t_ad_realtime_rpm_total_mysql_001\"\nOUTPUT_TABLE = \"t_ad_realtime_rpm_cand_mysql_001\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (sub_task_id, ptag, task_id, imp_min, expose_per_w, total_expose_pv, total_click_pv, ctime, mtime)\nSELECT\n sub_task_id,\n ptag,\n task_id,\n imp_min,\n expose_per_w,\n expose_pv AS total_expose_pv,\n click_pv AS total_click_pv,\n ctime,\n mtime\nFROM {DB_NAME}.{INPUT_TABLE}\nWHERE imp_min = 202606090450\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n sub_task_id VARCHAR(64) NOT NULL,\n ptag VARCHAR(64) NOT NULL,\n task_id VARCHAR(64) NOT NULL,\n imp_min BIGINT NOT NULL,\n expose_per_w BIGINT NOT NULL,\n total_expose_pv BIGINT NOT NULL,\n total_click_pv BIGINT NOT NULL,\n ctime DATETIME NOT NULL,\n mtime DATETIME NOT NULL,\n PRIMARY KEY (sub_task_id)\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_001 ground_truth done: 7 rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_001"} {"task_id": "mysql_002_en", "id": "offline-compute_MySQL_mysql_002", "name": "Inference Service Replica Forecast and Bias Coefficient Computation", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "en", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\nI need you to generate a MySQL script that, for inference services with autoscaling enabled, expands hourly expected pod counts to 10-minute granularity, clips them using configured upper and lower bounds, computes a bias correction coefficient based on the actual pod count over the past 24 hours, and labels the AHPA recommended status.\n\n**Business Objective**: For all inference services with autoscaling enabled (HPA or AHPA), expand the existing hourly expected pod counts to 10-minute intervals, and clip them using the service's configured replica upper and lower bounds (which must be multiplied by the machine count to convert to pod counts). Combined with the actual running pod count over the past 24 hours, compute a bias correction coefficient (actual pod count / clipped P90 expected value). Additionally, determine the AHPA service recommendation status (normal, degraded, no request). Write the final results to the target table for the partition `dt='2026050700'`.\n\n**Input Tables (full name + brief description)**:\n- `internal_platform_db.dwd_tj_model_service_hpa_mysql_002` (HPA configuration dimension table)\n- `internal_platform_db.nextgen_platform_dsl_autotune_rec_gpu_config_fht0_mysql_002` (AHPA recommendation fact table)\n- `internal_platform_db.dwd_aide_inferencev2_done_service_info_h_mysql_002` (service information dimension table)\n- `internal_platform_db.dwm_gputj_platform_hourly_expected_pod_mysql_002` (hourly expected pod count intermediate table)\n- `internal_platform_db.dwm_gputj_platform_gpu_base_feature_agg_v2_mysql_002` (actual pod count fact table)\n\n(Please connect to the database and query to confirm the table structures and field semantics.)\n\n**Key Filters and Joins**:\n- **Filters**: The service information table must select services where `status='done'`, not deleted (`deleted <> '1'`), and with HPA or AHPA enabled. The HPA configuration table takes the latest visible configuration (first row by `visible desc, id desc`). The AHPA recommendation table takes data from the last 10 minutes (current max timestamp - 600000ms). The hourly expected table takes the latest partition within the past 5 days. The actual pod count takes data from the past 24 hours (`dt >= '2026050600' and dt <= '2026050700'`) with `agg_type=2` and `avg_pod_count > 0`.\n- **Joins**: Join all tables primarily by `service_name`. The service information table is inner-joined with the hourly expected table, then left-joined with the HPA configuration table (via `service_id = serving_id`) and the AHPA status table. The hourly expected table must be cross-joined with a set of 6 time offsets (0, 10, 20, 30, 40, 50 minutes) to expand to 10-minute granularity.\n\n**Derived Logic and Business Semantics**:\n1. **Scale type**: If `ahpa_enable='true'`, then `'ahpa'`; if `hpa_enable='true'`, then `'hpa'`.\n2. **AHPA recommendation status**: For AHPA services, if there are no records in the last 10 minutes, the status is `'no_request'`; if there are records but all have `response_code` other than `'200'`, the status is `'degraded'`; otherwise, `'normal'`.\n3. **Pod count upper and lower bounds**: The `min_replicas` and `max_replicas` from the HPA/AHPA configuration must be multiplied by `host_num` (machine count) from the service information to obtain `hpa_min_pods`, `hpa_max_pods`, `ahpa_min_pods`, and `ahpa_max_pods`.\n4. **Expected value clipping**: All 8 expected pod count fields (e.g., `expected_pod_avg_qpm_p90`) must be clipped using the computed upper and lower bounds (`GREATEST(LEAST(original_value, upper_bound), lower_bound)`).\n5. **Bias coefficient**: Bias coefficient = `actual_pod_count` / clipped `expected_pod_avg_qpm_p90` value (use 1.0 when the denominator is 0).\n6. **Time expansion**: Expand the hourly prediction time to 6 time points at 0, 10, 20, 30, 40, and 50 minutes of each hour (constructed using `CONCAT(SUBSTRING(agg_time,1,14), LPAD(offset,2,'0'), ':00')`).\n\n**Output Requirements**:\n- Target table: `internal_platform_db.dwm_gputj_platform_replica_forecast_with_bias_cand_mysql_002`\n- Output field order: `service_name`, `instance_uuid`, `workload_name`, `namespace`, `agg_time`, `hour_of_day`, `scale_type`, `host_num`, `gpu_name`, `queue_name`, `hpa_min_pods`, `hpa_max_pods`, `ahpa_min_pods`, `ahpa_max_pods`, `ahpa_status`, `ahpa_response_code`, `expected_pod_avg_qpm_avg`, `expected_pod_avg_qpm_p50`, `expected_pod_avg_qpm_p90`, `expected_pod_max_qpm_avg`, `expected_pod_max_qpm_p50`, `expected_pod_max_qpm_p90`, `expected_pod_latest_avg_qpm`, `expected_pod_latest_max_qpm`, `actual_pod_count`, `bias_coefficient`, `dt`, `host_gpu_num`, `model_req_count`\n- `agg_time` format: `yyyy-MM-dd HH:mm:00`, at 10-minute granularity\n- `hour_of_day` format: `HH:00:00`\n- Write partition: `dt='2026050700'`\n- If the target table does not exist, first create it using standard MySQL InnoDB format, then write the data\n- Use standard MySQL syntax; do not use Hive/Spark SQL dialects (do not use `INSERT OVERWRITE`; use `INSERT INTO ... SELECT`)\n\n**Environment and Execution Notes**:\n- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`\n- The local MySQL is running at localhost:3306, username `root`, password `root123`\n- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)\n- The script must include complete table creation (if the target table does not exist) and data writing logic\n- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_002 ground truth: 推理服务副本数预估与偏差系数计算\n\nTask:\n For services with HPA/AHPA enabled, expand hourly expected pod counts to\n 10-minute granularity, clamp with HPA/AHPA replica limits (× host_num),\n compute bias coefficient (actual / clamped p90), and mark AHPA status.\n Write to output table with dt='2026050700'.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\n\n# Input tables\nT_SVC_INFO = \"dwd_aide_inferencev2_done_service_info_h_mysql_002\"\nT_HPA = \"dwd_tj_model_service_hpa_mysql_002\"\nT_AHPA = \"nextgen_platform_dsl_autotune_rec_gpu_config_fht0_mysql_002\"\nT_EXPECTED = \"dwm_gputj_platform_hourly_expected_pod_mysql_002\"\nT_ACTUAL = \"dwm_gputj_platform_gpu_base_feature_agg_v2_mysql_002\"\n\n# Output table\nOUTPUT_TABLE = \"dwm_gputj_platform_replica_forecast_with_bias_cand_mysql_002\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE} (\n service_name, instance_uuid, workload_name, namespace,\n agg_time, hour_of_day, scale_type, host_num, gpu_name, queue_name,\n hpa_min_pods, hpa_max_pods, ahpa_min_pods, ahpa_max_pods,\n ahpa_status, ahpa_response_code,\n expected_pod_avg_qpm_avg, expected_pod_avg_qpm_p50, expected_pod_avg_qpm_p90,\n expected_pod_max_qpm_avg, expected_pod_max_qpm_p50, expected_pod_max_qpm_p90,\n expected_pod_latest_avg_qpm, expected_pod_latest_max_qpm,\n actual_pod_count, bias_coefficient, dt, host_gpu_num, model_req_count\n)\nWITH\n-- 1. 从服务信息表获取 hpa/ahpa 类型、host_num 和 service_id\nservice_info AS (\n SELECT\n name AS service_name,\n instance_uuid,\n service_id,\n CAST(host_num AS SIGNED) AS host_num,\n CAST(host_gpu_num AS SIGNED) AS host_gpu_num,\n gpu_name,\n queue_name,\n hpa_enable,\n ahpa_enable,\n CASE\n WHEN ahpa_enable = 'true' THEN 'ahpa'\n WHEN hpa_enable = 'true' THEN 'hpa'\n ELSE NULL\n END AS scale_type\n FROM {DB_NAME}.{T_SVC_INFO}\n WHERE dt = '2026050700'\n AND status = 'done'\n AND deleted <> '1'\n AND (hpa_enable = 'true' OR ahpa_enable = 'true')\n),\n\n-- 2. 从HPA表获取副本数上下限（按 serving_id 分组，取 visible desc, id desc 第一条）\nhpa_limits AS (\n SELECT\n serving_id,\n min_replicas,\n max_replicas,\n status AS hpa_status,\n visible\n FROM (\n SELECT\n id,\n serving_id,\n min_replicas,\n max_replicas,\n status,\n visible,\n ROW_NUMBER() OVER (PARTITION BY serving_id ORDER BY visible DESC, id DESC) AS rn\n FROM {DB_NAME}.{T_HPA}\n WHERE dt = '2026050700'\n ) t\n WHERE rn = 1\n),\n\n-- 3. 从AHPA推荐状态表判断最近连续10分钟的推荐状态\n-- 先用窗口函数取每个 service_name 最近一条的 response_code\nahpa_latest AS (\n SELECT\n service_name,\n cluster_id,\n namespace,\n workload_name,\n response_code,\n CAST(`timestamp` AS SIGNED) AS ts\n FROM (\n SELECT\n service_name,\n cluster_id,\n namespace,\n workload_name,\n response_code,\n `timestamp`,\n ROW_NUMBER() OVER (\n PARTITION BY service_name\n ORDER BY CAST(`timestamp` AS SIGNED) DESC\n ) AS rn\n FROM {DB_NAME}.{T_AHPA}\n WHERE databus_imp_date = '2026050700'\n AND CAST(`timestamp` AS SIGNED) >= (\n SELECT MAX(CAST(`timestamp` AS SIGNED)) - 600000\n FROM {DB_NAME}.{T_AHPA}\n WHERE databus_imp_date = '2026050700'\n )\n ) ranked\n WHERE rn = 1\n),\n\nahpa_rec_raw AS (\n SELECT\n service_name,\n MAX(cluster_id) AS cluster_id,\n MAX(namespace) AS namespace,\n MAX(workload_name) AS workload_name,\n COUNT(1) AS total_count,\n SUM(CASE WHEN response_code = '200' THEN 1 ELSE 0 END) AS success_count,\n MAX(CASE WHEN response_code IS NOT NULL THEN response_code ELSE NULL END) AS any_response_code\n FROM {DB_NAME}.{T_AHPA}\n WHERE databus_imp_date = '2026050700'\n AND CAST(`timestamp` AS SIGNED) >= (\n SELECT MAX(CAST(`timestamp` AS SIGNED)) - 600000\n FROM {DB_NAME}.{T_AHPA}\n WHERE databus_imp_date = '2026050700'\n )\n GROUP BY service_name\n),\n\n-- 以 service_info 中 ahpa 服务为基准，LEFT JOIN 推荐状态\nahpa_status AS (\n SELECT\n si.service_name,\n r.cluster_id,\n r.namespace,\n r.workload_name,\n al.response_code,\n CASE\n WHEN r.service_name IS NULL THEN 'no_request'\n WHEN r.success_count = 0 THEN 'degraded'\n ELSE 'normal'\n END AS ahpa_rec_status\n FROM (\n SELECT DISTINCT service_name\n FROM service_info\n WHERE scale_type = 'ahpa'\n ) si\n LEFT JOIN ahpa_rec_raw r ON si.service_name = r.service_name\n LEFT JOIN ahpa_latest al ON si.service_name = al.service_name\n),\n\n-- 4. 从步骤三获取预期pod数（取近5天内最近有产出的天级分区）\nlatest_expected_dt AS (\n SELECT MAX(dt) AS latest_dt\n FROM {DB_NAME}.{T_EXPECTED}\n WHERE dt >= '20260502' AND dt <= '20260507'\n),\n\nexpected_pod AS (\n SELECT\n ep.service_name,\n ep.instance_uuid,\n ep.workload_name,\n ep.namespace,\n ep.agg_time,\n ep.hour_of_day,\n ep.expected_pod_avg_qpm_avg,\n ep.expected_pod_avg_qpm_p50,\n ep.expected_pod_avg_qpm_p90,\n ep.expected_pod_max_qpm_avg,\n ep.expected_pod_max_qpm_p50,\n ep.expected_pod_max_qpm_p90,\n ep.expected_pod_latest_avg_qpm,\n ep.expected_pod_latest_max_qpm,\n ep.model_req_count\n FROM {DB_NAME}.{T_EXPECTED} ep\n JOIN latest_expected_dt led ON ep.dt = led.latest_dt\n),\n\n-- 4.5 构造10分钟间隔的偏移量(0,10,20,30,40,50)\ntime_slots AS (\n SELECT 0 AS minute_offset UNION ALL\n SELECT 10 UNION ALL\n SELECT 20 UNION ALL\n SELECT 30 UNION ALL\n SELECT 40 UNION ALL\n SELECT 50\n),\n\n-- 4.6 将小时粒度的预期pod数按10分钟展开\nexpected_pod_10min AS (\n SELECT\n ep.service_name,\n ep.instance_uuid,\n ep.workload_name,\n ep.namespace,\n CONCAT(\n SUBSTRING(ep.agg_time, 1, 14),\n LPAD(CAST(ts.minute_offset AS CHAR), 2, '0'),\n ':00'\n ) AS agg_time,\n ep.hour_of_day,\n ep.expected_pod_avg_qpm_avg,\n ep.expected_pod_avg_qpm_p50,\n ep.expected_pod_avg_qpm_p90,\n ep.expected_pod_max_qpm_avg,\n ep.expected_pod_max_qpm_p50,\n ep.expected_pod_max_qpm_p90,\n ep.expected_pod_latest_avg_qpm,\n ep.expected_pod_latest_max_qpm,\n ep.model_req_count\n FROM expected_pod ep\n CROSS JOIN time_slots ts\n),\n\n-- 5. 从特征表取近24小时的实际pod数（按service+小时维度）\nactual_pod AS (\n SELECT\n service_name,\n SUBSTRING_INDEX(agg_time, ' ', -1) AS hour_of_day,\n AVG(avg_pod_count) AS actual_pod_count\n FROM {DB_NAME}.{T_ACTUAL}\n WHERE dt >= '2026050600'\n AND dt <= '2026050700'\n AND agg_type = 2\n AND avg_pod_count > 0\n GROUP BY service_name, SUBSTRING_INDEX(agg_time, ' ', -1)\n),\n\n-- 6. 计算偏差系数\nbias AS (\n SELECT\n a.service_name,\n a.hour_of_day,\n a.actual_pod_count,\n e.clamped_pod_p90,\n CASE\n WHEN e.clamped_pod_p90 > 0 THEN a.actual_pod_count / e.clamped_pod_p90\n ELSE 1.0\n END AS bias_coefficient\n FROM actual_pod a\n LEFT JOIN (\n SELECT\n ep.service_name,\n ep.hour_of_day,\n AVG(\n GREATEST(\n LEAST(\n ep.expected_pod_avg_qpm_p90,\n CAST(hl.max_replicas AS DECIMAL(20,4)) * si.host_num\n ),\n CAST(hl.min_replicas AS DECIMAL(20,4)) * si.host_num\n )\n ) AS clamped_pod_p90\n FROM expected_pod_10min ep\n JOIN service_info si ON ep.service_name = si.service_name\n LEFT JOIN hpa_limits hl ON si.service_id = hl.serving_id\n GROUP BY ep.service_name, ep.hour_of_day\n ) e ON a.service_name = e.service_name AND a.hour_of_day = e.hour_of_day\n)\n\n-- 7. 最终结果\nSELECT\n si.service_name,\n si.instance_uuid,\n COALESCE(ep.workload_name, ast.workload_name) AS workload_name,\n COALESCE(ep.namespace, ast.namespace) AS namespace,\n ep.agg_time,\n ep.hour_of_day,\n si.scale_type,\n si.host_num,\n si.gpu_name,\n si.queue_name,\n CAST(hl.min_replicas AS SIGNED) * si.host_num AS hpa_min_pods,\n CAST(hl.max_replicas AS SIGNED) * si.host_num AS hpa_max_pods,\n CAST(hl.min_replicas AS SIGNED) * si.host_num AS ahpa_min_pods,\n CAST(hl.max_replicas AS SIGNED) * si.host_num AS ahpa_max_pods,\n COALESCE(ast.ahpa_rec_status, 'no_request') AS ahpa_status,\n ast.response_code AS ahpa_response_code,\n GREATEST(LEAST(ep.expected_pod_avg_qpm_avg, CAST(hl.max_replicas AS DECIMAL(20,4)) * si.host_num), CAST(hl.min_replicas AS DECIMAL(20,4)) * si.host_num) AS expected_pod_avg_qpm_avg,\n GREATEST(LEAST(ep.expected_pod_avg_qpm_p50, CAST(hl.max_replicas AS DECIMAL(20,4)) * si.host_num), CAST(hl.min_replicas AS DECIMAL(20,4)) * si.host_num) AS expected_pod_avg_qpm_p50,\n GREATEST(LEAST(ep.expected_pod_avg_qpm_p90, CAST(hl.max_replicas AS DECIMAL(20,4)) * si.host_num), CAST(hl.min_replicas AS DECIMAL(20,4)) * si.host_num) AS expected_pod_avg_qpm_p90,\n GREATEST(LEAST(ep.expected_pod_max_qpm_avg, CAST(hl.max_replicas AS DECIMAL(20,4)) * si.host_num), CAST(hl.min_replicas AS DECIMAL(20,4)) * si.host_num) AS expected_pod_max_qpm_avg,\n GREATEST(LEAST(ep.expected_pod_max_qpm_p50, CAST(hl.max_replicas AS DECIMAL(20,4)) * si.host_num), CAST(hl.min_replicas AS DECIMAL(20,4)) * si.host_num) AS expected_pod_max_qpm_p50,\n GREATEST(LEAST(ep.expected_pod_max_qpm_p90, CAST(hl.max_replicas AS DECIMAL(20,4)) * si.host_num), CAST(hl.min_replicas AS DECIMAL(20,4)) * si.host_num) AS expected_pod_max_qpm_p90,\n GREATEST(LEAST(ep.expected_pod_latest_avg_qpm, CAST(hl.max_replicas AS DECIMAL(20,4)) * si.host_num), CAST(hl.min_replicas AS DECIMAL(20,4)) * si.host_num) AS expected_pod_latest_avg_qpm,\n GREATEST(LEAST(ep.expected_pod_latest_max_qpm, CAST(hl.max_replicas AS DECIMAL(20,4)) * si.host_num), CAST(hl.min_replicas AS DECIMAL(20,4)) * si.host_num) AS expected_pod_latest_max_qpm,\n b.actual_pod_count,\n b.bias_coefficient,\n '2026050700' AS dt,\n si.host_gpu_num,\n ep.model_req_count\nFROM service_info si\nJOIN expected_pod_10min ep ON si.service_name = ep.service_name\nLEFT JOIN hpa_limits hl ON si.service_id = hl.serving_id\nLEFT JOIN ahpa_status ast ON si.service_name = ast.service_name\nLEFT JOIN bias b ON si.service_name = b.service_name AND ep.hour_of_day = b.hour_of_day\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n service_name VARCHAR(256),\n instance_uuid VARCHAR(256),\n workload_name VARCHAR(256),\n namespace VARCHAR(256),\n agg_time VARCHAR(256),\n hour_of_day VARCHAR(256),\n scale_type VARCHAR(256),\n host_num BIGINT,\n gpu_name VARCHAR(256),\n queue_name VARCHAR(256),\n hpa_min_pods BIGINT,\n hpa_max_pods BIGINT,\n ahpa_min_pods BIGINT,\n ahpa_max_pods BIGINT,\n ahpa_status VARCHAR(256),\n ahpa_response_code VARCHAR(256),\n expected_pod_avg_qpm_avg DOUBLE,\n expected_pod_avg_qpm_p50 DOUBLE,\n expected_pod_avg_qpm_p90 DOUBLE,\n expected_pod_max_qpm_avg DOUBLE,\n expected_pod_max_qpm_p50 DOUBLE,\n expected_pod_max_qpm_p90 DOUBLE,\n expected_pod_latest_avg_qpm DOUBLE,\n expected_pod_latest_max_qpm DOUBLE,\n actual_pod_count DOUBLE,\n bias_coefficient DOUBLE,\n dt VARCHAR(32),\n host_gpu_num BIGINT,\n model_req_count DOUBLE\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_002 ground_truth done: rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_002_en"} {"task_id": "mysql_003", "id": "offline-compute_MySQL_mysql_003", "name": "离线推理任务特征宽表构建", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\n我需要你生成一段 MySQL 代码，创建离线推理任务特征宽表，汇总dt='2026050700'分区内非终态或近期结束的静态模型JOB类型推理任务的特征信息，包含任务基本信息、各阶段耗时、数据集处理情况、实例排队等待时长、提交人和镜像版本。\n\n**输入**：\n1. `internal_platform_db.aide_offline_inference_info_fixed_mysql_003`（主表，按dt字段分区过滤）\n2. `internal_platform_db.aide_offline_inference_dataset_info_h_mysql_003`\n3. `internal_platform_db.aide_offline_inference_stage_info_h_mysql_003`\n4. `internal_platform_db.aide_offline_inference_pipeline_time_mysql_003`\n5. `internal_platform_db.aide_offline_inference_file_info_h_mysql_003`（按dt字段过滤当前小时分区）\n6. `internal_platform_db.aide_mould_h_mysql_003`\n7. `internal_platform_db.task_instance_wait_time_stats_mysql_003`（按dt字段取最新分区数据）\n\n**处理规则**：\n1. 主表过滤条件：处理dt='2026050700'分区数据；base='JOB'；model_type='STATIC_MODEL'；create_time早于dt解析后的下小时开始时间（即'2026-05-07 01:00:00'）；status为非终态（NOT IN ('FINISH','KILL','FAILED')）或update_time在dt解析前1小时内（即>='2026-05-06 23:00:00'）\n2. 表关联方式：所有表通过任务ID（id、offline_inference_id、task_id、file_id）与主表LEFT JOIN，注意：stage_info_h和dataset_info_h与主表为一对多关系，LEFT JOIN不做聚合，同一主表行会产生多行输出（笛卡尔膨胀）\n3. 特殊关联规则：\n - 等待时长表：先用任务名和阶段ID拼接服务名（格式：任务名_阶段ID_OFFLINE），再与服务名关联\n - 模型表：从主表model_ids字段用JSON提取mould_id（JSON_EXTRACT(model_ids, '$.mould_id')）进行关联\n4. 聚合计算规则：\n - 流水线耗时按task_id分组：总耗时=SUM(time_cost_in_second)；任务下发耗时=SUM(CASE WHEN step_desc='任务下发' THEN time_cost_in_second ELSE 0 END)；离线推理耗时=SUM(CASE WHEN step_desc='离线推理' THEN time_cost_in_second ELSE 0 END)；仅统计已完成步骤（end_time IS NOT NULL）\n - 实例等待时长按service_name分组：取最新分区数据；最大首次等待时长=MAX(first_wait_time)；平均首次等待时长=AVG(first_wait_time)\n - 数据集统计：激活数、完成数、错误数按offline_inference_id分组计数\n - 文件信息：取当前小时分区下每个文件的最新一条记录（ROW_NUMBER按update_time降序）\n - 模型信息：从model_ids用JSON提取mould_id和mould_name\n - 服务名派生：CONCAT(任务名, '_', 阶段ID, '_OFFLINE')\n - 计数标识ct：固定值1\n5. 输出字段顺序：dt, create_time, ct, id, name, servingName, base, status, submit_type, mould_id, mould_name, time_cost_in_second, dataset_act_num, dataset_fin_num, dataset_err_num, max_instance_first_wait_time, avg_instance_first_wait_time, task_dispatch_time, offline_inference_time, inference_config, submit_operator, submit_time, image_tag\n\n**输出要求**：\n- 目标表：`internal_platform_db.dwd_aide_offline_inference_feature_cand_mysql_003`\n- 输出23个字段，字段类型根据源表推断\n- 如果目标表不存在，请先按MySQL InnoDB标准建表，再写入数据\n- 请使用标准MySQL语法，不要使用Hive/Spark SQL方言\n\n**环境与执行说明**：\n- 你的最终产出必须写入文件 `/tmp_workspace/result.py`，不能写 result.sql\n- 本机 MySQL 已在 localhost:3306 运行，用户名 root，密码 root123\n- 请使用 Python pymysql 在 result.py 中执行 SQL（不要用 mysql 命令行）\n- 脚本需要包含完整的建表（如目标表不存在）+ 写入数据的逻辑\n- 写出 result.py 后，你必须自己执行 `python3 /tmp_workspace/result.py` 验证它能成功运行并产出正确数据", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_003 ground truth: 离线推理任务特征宽表构建\n\nTask:\n Build an offline inference task feature wide table by joining 7 input tables,\n aggregating pipeline timing and wait time stats, extracting model info via JSON,\n and writing to the output table.\n\n WHERE conditions match the original Hive SQL:\n - a.dt = '2026050700'\n - a.base = 'JOB' AND a.model_type = 'STATIC_MODEL'\n - a.create_time < '2026-05-07 01:00:00' (dt parsed + 1 hour)\n - status NOT IN ('FINISH','KILL','FAILED') OR update_time >= '2026-05-06 23:00:00' (dt parsed - 1 hour)\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nOUTPUT_TABLE = \"dwd_aide_offline_inference_feature_cand_mysql_003\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (dt, create_time, ct, id, name, servingName, base, status, submit_type,\n mould_id, mould_name, time_cost_in_second, dataset_act_num, dataset_fin_num,\n dataset_err_num, max_instance_first_wait_time, avg_instance_first_wait_time,\n task_dispatch_time, offline_inference_time, inference_config,\n submit_operator, submit_time, image_tag)\n\nWITH\n-- Step 1: Aggregate pipeline timing by task_id\npipeline_time_agg AS (\n SELECT\n task_id,\n SUM(time_cost_in_second) AS time_cost_in_second,\n CAST(SUM(CASE WHEN step_desc = '任务下发' THEN time_cost_in_second ELSE 0 END) AS SIGNED) AS task_dispatch_time,\n CAST(SUM(CASE WHEN step_desc = '离线推理' THEN time_cost_in_second ELSE 0 END) AS SIGNED) AS offline_inference_time\n FROM {DB_NAME}.aide_offline_inference_pipeline_time_mysql_003\n WHERE end_time IS NOT NULL\n GROUP BY task_id\n),\n\n-- Step 2: Aggregate wait time stats by service_name (latest partition)\nservice_wait_time_stats AS (\n SELECT\n service_name,\n CAST(MAX(first_wait_time) AS DOUBLE) AS max_instance_first_wait_time,\n CAST(AVG(first_wait_time) AS DOUBLE) AS avg_instance_first_wait_time\n FROM {DB_NAME}.task_instance_wait_time_stats_mysql_003\n WHERE dt = (SELECT MAX(dt) FROM {DB_NAME}.task_instance_wait_time_stats_mysql_003)\n AND first_wait_time IS NOT NULL\n GROUP BY service_name\n)\n\n-- Step 3: Join all tables and extract features\nSELECT\n '2026050700' AS dt,\n a.create_time,\n 1 AS ct,\n a.id,\n a.name AS `name`,\n CONCAT(a.name, '_', c.id, '_OFFLINE') AS servingName,\n a.base,\n a.status,\n a.submit_type,\n JSON_UNQUOTE(JSON_EXTRACT(a.model_ids, '$.mould_id')) AS mould_id,\n JSON_UNQUOTE(JSON_EXTRACT(a.model_ids, '$.mould_name')) AS mould_name,\n d.time_cost_in_second,\n b.dataset_act_num,\n b.dataset_fin_num,\n b.dataset_err_num,\n w.max_instance_first_wait_time,\n w.avg_instance_first_wait_time,\n d.task_dispatch_time,\n d.offline_inference_time,\n a.inference_config,\n e.operator AS submit_operator,\n e.create_time AS submit_time,\n f.image_tag\nFROM {DB_NAME}.aide_offline_inference_info_fixed_mysql_003 a\nLEFT JOIN {DB_NAME}.aide_offline_inference_dataset_info_h_mysql_003 b\n ON a.id = b.offline_inference_id\nLEFT JOIN {DB_NAME}.aide_offline_inference_stage_info_h_mysql_003 c\n ON a.id = c.offline_inference_id\nLEFT JOIN pipeline_time_agg d\n ON a.id = d.task_id\nLEFT JOIN service_wait_time_stats w\n ON CONCAT(a.name, '_', c.id, '_OFFLINE') = w.service_name\nLEFT JOIN (\n SELECT\n id,\n operator,\n create_time,\n ROW_NUMBER() OVER(PARTITION BY id ORDER BY update_time DESC) AS rn\n FROM {DB_NAME}.aide_offline_inference_file_info_h_mysql_003\n WHERE dt = '2026050700'\n) e ON a.file_id = e.id AND e.rn = 1\nLEFT JOIN {DB_NAME}.aide_mould_h_mysql_003 f\n ON CAST(JSON_UNQUOTE(JSON_EXTRACT(a.model_ids, '$.mould_id')) AS SIGNED) = f.id\nWHERE a.dt = '2026050700'\n AND a.base = 'JOB'\n AND a.model_type = 'STATIC_MODEL'\n AND a.create_time < '2026-05-07 01:00:00'\n AND (\n a.status NOT IN ('FINISH', 'KILL', 'FAILED')\n OR\n a.update_time >= '2026-05-06 23:00:00'\n )\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n dt VARCHAR(256) COMMENT '分区字段',\n create_time VARCHAR(256) COMMENT '创建时间',\n ct INT COMMENT '计数标识',\n id BIGINT COMMENT '离线推理任务ID',\n name VARCHAR(256) COMMENT '任务名称',\n servingName VARCHAR(256) COMMENT '服务名称',\n base VARCHAR(256) COMMENT '基础类型',\n status VARCHAR(256) COMMENT '状态',\n submit_type VARCHAR(256) COMMENT '提交类型',\n mould_id VARCHAR(256) COMMENT '模型ID',\n mould_name VARCHAR(256) COMMENT '模型名称',\n time_cost_in_second BIGINT COMMENT '总耗时(秒)',\n dataset_act_num BIGINT COMMENT '数据集激活数量',\n dataset_fin_num BIGINT COMMENT '数据集完成数量',\n dataset_err_num BIGINT COMMENT '数据集错误数量',\n max_instance_first_wait_time DOUBLE COMMENT '最大首次等待时长(秒)',\n avg_instance_first_wait_time DOUBLE COMMENT '平均首次等待时长(秒)',\n task_dispatch_time BIGINT COMMENT '任务下发耗时(秒)',\n offline_inference_time BIGINT COMMENT '离线推理耗时(秒)',\n inference_config VARCHAR(256) COMMENT '推理配置',\n submit_operator VARCHAR(256) COMMENT '提交人',\n submit_time VARCHAR(256) COMMENT '提交时间',\n image_tag VARCHAR(256) COMMENT '镜像标签'\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_003 ground_truth done: rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_003"} {"task_id": "mysql_004_en", "id": "offline-compute_MySQL_mysql_004", "name": "Ceph Path Coldness Score Cluster-Level Statistics", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "en", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\nPlease generate MySQL code based on the following requirements.\n\n1. Task Objective\nPerform cluster-level statistics on Ceph path coldness score data using three aggregation methods, and write the results to the target table.\n\n2. Input\n- Input table: `internal_platform_db.t_ceph_path_coldness_score_v2_mysql_004`\n- Filter condition: `dt='20260507'`\n\n3. Processing Rules\n3.1 Three path selection method definitions:\n- `level2_only`: Select only records where `path_level=2`\n- `max_available`: For each `cluster_name`, group by the first-level directory of `path_level2` (group key = the first segment obtained by removing the leading `/` from `path_level2` and splitting by `/`; for example, `/data/logs` and `/data/backup` both have the group key `data`). For each group, select the record with the maximum `path_level`.\n- `leaf_only`: Select leaf nodes (i.e., records for which no other record with the same `cluster_name` exists whose `path_level2` equals the parent path obtained by removing the last `/` and everything after it from the current record's `path_level2`; for example, if the current path is `/data/logs/app1`, the parent path is `/data/logs`, and if a record with this parent path exists, the current record is not a leaf).\n\n3.2 After merging the data from all three methods, aggregate by `cluster_name` and `agg_method`:\n- `total_path_count`: Total path count (count)\n- `total_size_tb`: Total capacity in TB (sum)\n- `total_access_count`: Total access count (sum)\n- `avg_coldness_score`: Average coldness score (mean)\n- `cluster_heat_category`: Categorize based on `avg_coldness_score`: >=60 is `'高热集群'` (high-heat cluster), >=40 is `'较热集群'` (warm cluster), >=20 is `'中等集群'` (moderate cluster), otherwise `'较冷集群'` (cool cluster)\n- `p0_count`/`p0_size_tb`: Path count/capacity where `governance_level='P0'`\n- `p1_count`/`p1_size_tb`: Path count/capacity where `governance_level='P1'`\n- `p2_count`/`p2_size_tb`: Path count/capacity where `governance_level='P2'`\n- `p3_count`/`p3_size_tb`: Path count/capacity where `governance_level='P3'`\n- `cold_path_pct`: (P0 path count + P1 path count) / total path count * 100\n- `cold_size_pct`: (P0 capacity + P1 capacity) / total capacity * 100\n\n4. Output Requirements\nOutput field order: `cluster_name` (VARCHAR(256)), `agg_method` (VARCHAR(256)), `total_path_count` (BIGINT), `total_size_tb` (DOUBLE), `total_access_count` (BIGINT), `avg_coldness_score` (DOUBLE), `cluster_heat_category` (VARCHAR(256)), `p0_count` (BIGINT), `p0_size_tb` (DOUBLE), `p1_count` (BIGINT), `p1_size_tb` (DOUBLE), `p2_count` (BIGINT), `p2_size_tb` (DOUBLE), `p3_count` (BIGINT), `p3_size_tb` (DOUBLE), `cold_path_pct` (DOUBLE), `cold_size_pct` (DOUBLE), `dt` (VARCHAR(256))\n\n5. Write Requirements\n- Output table: `internal_platform_db.t_ceph_coldness_cluster_stats_v2_cand_mysql_004`\n- Filter condition: `dt='20260507'`\n- No joins, single-table processing\n- Use standard MySQL syntax; do not use Hive/Spark SQL dialects\n\n**Environment and Execution Notes**:\n- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`\n- The local MySQL is running at localhost:3306, username `root`, password `root123`\n- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)\n- The script must include complete table creation (if the target table does not exist) and data writing logic\n- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_004 ground truth: Ceph路径冷热评分集群级统计\n\nTask:\n 对 t_ceph_path_coldness_score_v2_mysql_004 进行三种聚合方式统计，\n 结果写入 t_ceph_coldness_cluster_stats_v2_cand_mysql_004\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"t_ceph_path_coldness_score_v2_mysql_004\"\nOUTPUT_TABLE = \"t_ceph_coldness_cluster_stats_v2_cand_mysql_004\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (cluster_name, agg_method, total_path_count, total_size_tb, total_access_count,\n avg_coldness_score, cluster_heat_category,\n p0_count, p0_size_tb, p1_count, p1_size_tb,\n p2_count, p2_size_tb, p3_count, p3_size_tb,\n cold_path_pct, cold_size_pct, dt)\n\nWITH all_scores AS (\n SELECT *\n FROM {DB_NAME}.{INPUT_TABLE}\n WHERE dt = '20260507'\n),\n\n-- Method 1: Fixed path_level=2\nlevel2_data AS (\n SELECT *, 'level2_only' AS agg_method\n FROM all_scores\n WHERE path_level = 2\n),\n\n-- Method 2: Maximum available level per path tree\nmax_avail_data AS (\n SELECT a.*, 'max_available' AS agg_method\n FROM all_scores a\n INNER JOIN (\n SELECT\n cluster_name,\n path_level2,\n path_level,\n ROW_NUMBER() OVER (\n PARTITION BY cluster_name,\n SUBSTRING_INDEX(TRIM(LEADING '/' FROM path_level2), '/', 1)\n ORDER BY path_level DESC, path_level2\n ) AS rn\n FROM all_scores\n ) b\n ON a.cluster_name = b.cluster_name\n AND a.path_level2 = b.path_level2\n AND a.path_level = b.path_level\n AND b.rn = 1\n),\n\n-- Method 3: Leaf nodes only (no deeper children)\nparent_paths AS (\n SELECT DISTINCT\n cluster_name,\n path_level - 1 AS parent_level,\n REGEXP_REPLACE(path_level2, '/[^/]+$', '') AS parent_path\n FROM all_scores\n WHERE path_level >= 2\n),\n\nleaf_data AS (\n SELECT a.*, 'leaf_only' AS agg_method\n FROM all_scores a\n LEFT JOIN parent_paths b\n ON a.cluster_name = b.cluster_name\n AND a.path_level = b.parent_level\n AND a.path_level2 = b.parent_path\n WHERE b.parent_path IS NULL\n),\n\n-- Combine three methods\nunion_all AS (\n SELECT * FROM level2_data\n UNION ALL\n SELECT * FROM max_avail_data\n UNION ALL\n SELECT * FROM leaf_data\n)\n\n-- Aggregate by cluster and method\nSELECT\n cluster_name,\n agg_method,\n COUNT(*) AS total_path_count,\n ROUND(SUM(size_tb), 2) AS total_size_tb,\n SUM(access_count) AS total_access_count,\n ROUND(AVG(coldness_score), 2) AS avg_coldness_score,\n\n CASE\n WHEN AVG(coldness_score) >= 60 THEN '高热集群'\n WHEN AVG(coldness_score) >= 40 THEN '较热集群'\n WHEN AVG(coldness_score) >= 20 THEN '中等集群'\n ELSE '较冷集群'\n END AS cluster_heat_category,\n\n SUM(CASE WHEN governance_level = 'P0' THEN 1 ELSE 0 END) AS p0_count,\n ROUND(SUM(CASE WHEN governance_level = 'P0' THEN size_tb ELSE 0 END), 2) AS p0_size_tb,\n SUM(CASE WHEN governance_level = 'P1' THEN 1 ELSE 0 END) AS p1_count,\n ROUND(SUM(CASE WHEN governance_level = 'P1' THEN size_tb ELSE 0 END), 2) AS p1_size_tb,\n SUM(CASE WHEN governance_level = 'P2' THEN 1 ELSE 0 END) AS p2_count,\n ROUND(SUM(CASE WHEN governance_level = 'P2' THEN size_tb ELSE 0 END), 2) AS p2_size_tb,\n SUM(CASE WHEN governance_level = 'P3' THEN 1 ELSE 0 END) AS p3_count,\n ROUND(SUM(CASE WHEN governance_level = 'P3' THEN size_tb ELSE 0 END), 2) AS p3_size_tb,\n\n ROUND(SUM(CASE WHEN governance_level IN ('P0','P1') THEN 1 ELSE 0 END)\n * 100.0 / COUNT(*), 2) AS cold_path_pct,\n ROUND(SUM(CASE WHEN governance_level IN ('P0','P1') THEN size_tb ELSE 0 END)\n * 100.0 / NULLIF(SUM(size_tb), 0), 2) AS cold_size_pct,\n\n '20260507' AS dt\n\nFROM union_all\nGROUP BY cluster_name, agg_method\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n cluster_name VARCHAR(256) COMMENT '集群名',\n agg_method VARCHAR(256) COMMENT '聚合方式',\n total_path_count BIGINT COMMENT '路径总数',\n total_size_tb DOUBLE COMMENT '总容量(TB)',\n total_access_count BIGINT COMMENT '总访问次数',\n avg_coldness_score DOUBLE COMMENT '平均冷热评分',\n cluster_heat_category VARCHAR(256) COMMENT '集群热度分类',\n p0_count BIGINT COMMENT 'P0路径数',\n p0_size_tb DOUBLE COMMENT 'P0容量TB',\n p1_count BIGINT COMMENT 'P1路径数',\n p1_size_tb DOUBLE COMMENT 'P1容量TB',\n p2_count BIGINT COMMENT 'P2路径数',\n p2_size_tb DOUBLE COMMENT 'P2容量TB',\n p3_count BIGINT COMMENT 'P3路径数',\n p3_size_tb DOUBLE COMMENT 'P3容量TB',\n cold_path_pct DOUBLE COMMENT '冷路径占比',\n cold_size_pct DOUBLE COMMENT '冷容量占比',\n dt VARCHAR(256) COMMENT '数据日期'\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_004 ground_truth done: rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_004_en"} {"task_id": "mysql_005_en", "id": "offline-compute_MySQL_mysql_005", "name": "GPU Inference Platform P90 Traffic Forecast (Holiday/Workday Differentiation)", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "en", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\nI need you to generate a MySQL script that produces 14-day traffic and resource usage forecasts (P90 percentile) for each service on the GPU inference platform, differentiating between holidays and workdays, with mutual fallback when historical samples are insufficient, and outputting results at both 10-minute and hourly time granularities.\n\n**Business Background and Objective**: The GPU inference platform needs to forecast traffic and resource usage for each service over the next 14 days. The forecast is based on P90 percentile values from historical data, with differentiation between holidays and workdays. When historical samples for one category are insufficient, the other category is used as a fallback. The output includes forecast results at both 10-minute and hourly time granularities.\n\n**Input Tables (full name + brief description)**:\n- `internal_platform_db.dwm_gputj_platform_gpu_base_feature_agg_v2_mysql_005` (GPU feature aggregation fact table)\n- `internal_platform_db.dwd_aide_inferencev2_done_service_info_h_mysql_005` (service list dimension table)\n- `internal_platform_db.dim_holiday_list_mysql_005` (holiday dimension table)\n\n(Please connect to the database and query to confirm the table structures and field semantics.)\n\n**Processing Rules**:\n1. **Fact table data range**: Take partitions between `'20260420'` and `'20260507'`, excluding the anomalous period from `'20260504'` to `'20260504'`; traffic value `nv_inference_count_model_avg` >= 0; `agg_type` is 1 or 2.\n2. **Time point template**: Extracted from the fact table partitions `'20260502'` to `'20260507'` (exclusive).\n3. **Join logic**:\n - Cartesian product of the service list with all time points over the next 14 days (at 10-minute and hourly granularities).\n - Join with the historical fact table on join keys: `service_name`, `agg_type`, and the hour-minute portion of the time point.\n - Left join the holiday dimension table twice: once for the prediction date and once for the historical data date.\n4. **Derived fields**:\n - `is_holiday`: Whether the prediction date is a holiday (1/0).\n - `day_of_week`: Day of the week for the prediction date (1–7).\n - `prediction_type`: Fixed as `'request_model_count'`.\n - `statistic_time_count`: Number of historical time points used in the forecast, computed using the holiday/workday mutual fallback logic.\n - P90 forecast fields: Based on the prediction day type (holiday/workday), take the P90 value of each metric from the corresponding historical data, with fallback to the other type when insufficient.\n - `workload_name`, `namespace`, etc.: Take the maximum value per service from the historical fact table.\n\n**Output Requirements**:\n- **Output table**: `internal_platform_db.dws_gputj_platform_model_prediction_long_p90_mysql_005`\n- **Output fields (in order)**: `dt`, `instance_uuid`, `service_name`, `workload_name`, `namespace`, `agg_time`, `agg_type`, `is_holiday`, `day_of_week`, `prediction_type`, `nv_inference_count_model_avg_p90`, `statistic_time_count`, `nv_inference_request_duration_ms_model_avg`, `nv_inference_queue_duration_ms_model_avg`, `num_queued_reqs_model_avg`, `nv_inference_request_success_model_avg`, `nv_inference_request_failure_model_avg`, `nv_inference_request_duration_ms_perreq_avg`, `nv_inference_queue_duration_ms_perreq_avg`, `nv_inference_request_duration_ms_perreq_p95`, `nv_inference_queue_duration_ms_perreq_p95`, `nv_inference_request_success_model_max`, `nv_inference_request_failure_model_max`, `DCGM_FI_DEV_GPU_UTIL_pod_avg`, `k8s_container_bs_rate_cpu_core_used_request_pod_avg`, `k8s_container_rate_mem_working_set_request_pod_avg`, `k8s_dcgm_fi_dev_fb_util_pod_avg`, `k8s_container_vgpu_gpu_util_pod_avg`\n- **Write method**: DELETE + INSERT or TRUNCATE + INSERT INTO ... SELECT\n- If the target table does not exist, first create it using standard MySQL InnoDB format, then write the data\n- Use standard MySQL syntax; do not use Hive/Spark SQL dialects\n\n**Environment and Execution Notes**:\n- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`\n- The local MySQL is running at localhost:3306, username `root`, password `root123`\n- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)\n- The script must include complete table creation (if the target table does not exist) and data writing logic\n- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_005 ground truth: GPU推理平台P90流量预估\n\nTask:\n Generate P90 predictions for GPU inference platform services\n over the next 14 days, distinguishing holidays vs weekdays,\n with fallback logic when historical samples are insufficient.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE_FEATURE = \"dwm_gputj_platform_gpu_base_feature_agg_v2_mysql_005\"\nINPUT_TABLE_SERVICE = \"dwd_aide_inferencev2_done_service_info_h_mysql_005\"\nINPUT_TABLE_HOLIDAY = \"dim_holiday_list_mysql_005\"\nOUTPUT_TABLE = \"dws_gputj_platform_model_prediction_long_p90_mysql_005\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (dt, instance_uuid, service_name, workload_name, namespace, agg_time, agg_type,\n is_holiday, day_of_week, prediction_type,\n nv_inference_count_model_avg_p90, statistic_time_count,\n nv_inference_request_duration_ms_model_avg, nv_inference_queue_duration_ms_model_avg,\n num_queued_reqs_model_avg, nv_inference_request_success_model_avg,\n nv_inference_request_failure_model_avg, nv_inference_request_duration_ms_perreq_avg,\n nv_inference_queue_duration_ms_perreq_avg, nv_inference_request_duration_ms_perreq_p95,\n nv_inference_queue_duration_ms_perreq_p95, nv_inference_request_success_model_max,\n nv_inference_request_failure_model_max, DCGM_FI_DEV_GPU_UTIL_pod_avg,\n k8s_container_bs_rate_cpu_core_used_request_pod_avg,\n k8s_container_rate_mem_working_set_request_pod_avg,\n k8s_dcgm_fi_dev_fb_util_pod_avg, k8s_container_vgpu_gpu_util_pod_avg)\nWITH\n-- Generate 14 future dates starting from 2026-05-07\nfuture_dates AS (\n SELECT DATE_ADD(STR_TO_DATE('20260507', '%Y%m%d'), INTERVAL offset DAY) AS target_date\n FROM (\n SELECT 0 AS offset UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3\n UNION ALL SELECT 4 UNION ALL SELECT 5 UNION ALL SELECT 6 UNION ALL SELECT 7\n UNION ALL SELECT 8 UNION ALL SELECT 9 UNION ALL SELECT 10 UNION ALL SELECT 11\n UNION ALL SELECT 12 UNION ALL SELECT 13\n ) offsets\n),\n-- Extract time template (hour/10-min granularity time points)\ntime_template AS (\n SELECT SUBSTRING_INDEX(agg_time, ' ', -1) AS time_part, agg_type\n FROM {DB_NAME}.{INPUT_TABLE_FEATURE}\n WHERE dt >= '20260502' AND dt < '20260507' AND agg_time > ''\n GROUP BY SUBSTRING_INDEX(agg_time, ' ', -1), agg_type\n),\n-- Generate 14 days x all time points\ntime_list AS (\n SELECT CONCAT(DATE_FORMAT(fd.target_date, '%Y-%m-%d'), ' ', tt.time_part) AS agg_time,\n tt.agg_type, fd.target_date\n FROM future_dates fd\n CROSS JOIN time_template tt\n),\n-- Get distinct service names\nservice_list AS (\n SELECT DISTINCT name AS service_name\n FROM {DB_NAME}.{INPUT_TABLE_SERVICE}\n WHERE dt = IF('2026050723' >= '2025101512', '2026050723', '2025101512') AND name <> ''\n),\n-- Filter feature data\nfeature AS (\n SELECT service_name, instance_uuid,\n trial_job_name AS workload_name, namespace, agg_time, agg_type,\n nv_inference_count_model_avg,\n nv_inference_request_duration_ms_model_avg,\n nv_inference_queue_duration_ms_model_avg,\n num_queued_reqs_model_avg,\n nv_inference_request_success_model_avg,\n nv_inference_request_failure_model_avg,\n nv_inference_request_duration_ms_perreq_avg,\n nv_inference_queue_duration_ms_perreq_avg,\n nv_inference_request_duration_ms_perreq_p95,\n nv_inference_queue_duration_ms_perreq_p95,\n nv_inference_request_success_model_max,\n nv_inference_request_failure_model_max,\n dcgm_fi_dev_gpu_util_pod_avg,\n k8s_container_bs_rate_cpu_core_used_request_pod_avg,\n k8s_container_rate_mem_working_set_request_pod_avg,\n k8s_dcgm_fi_dev_fb_util_pod_avg,\n k8s_container_vgpu_gpu_util_pod_avg\n FROM {DB_NAME}.{INPUT_TABLE_FEATURE}\n WHERE dt >= '20260420' AND dt <= '20260507'\n AND (dt < '20260504' OR dt > '20260504')\n AND nv_inference_count_model_avg >= 0\n AND agg_type IN (1, 2)\n),\n-- Core join + aggregation\nt1 AS (\n SELECT time_list.agg_time,\n feature.agg_type,\n MAX(feature.instance_uuid) AS instance_uuid,\n feature.service_name,\n MAX(feature.workload_name) AS workload_name,\n MAX(feature.namespace) AS namespace,\n MAX(CASE WHEN holiday_today.holiday_date > '' THEN 1 ELSE 0 END) AS today_holiday_date,\n MAX(feature.nv_inference_count_model_avg) AS nv_inference_count_model_avg,\n MAX(feature.nv_inference_request_duration_ms_model_avg) AS nv_inference_request_duration_ms_model_avg,\n MAX(feature.nv_inference_queue_duration_ms_model_avg) AS nv_inference_queue_duration_ms_model_avg,\n MAX(feature.num_queued_reqs_model_avg) AS num_queued_reqs_model_avg,\n MAX(feature.nv_inference_request_success_model_avg) AS nv_inference_request_success_model_avg,\n MAX(feature.nv_inference_request_failure_model_avg) AS nv_inference_request_failure_model_avg,\n MAX(feature.nv_inference_request_duration_ms_perreq_avg) AS nv_inference_request_duration_ms_perreq_avg,\n MAX(feature.nv_inference_queue_duration_ms_perreq_avg) AS nv_inference_queue_duration_ms_perreq_avg,\n MAX(feature.nv_inference_request_duration_ms_perreq_p95) AS nv_inference_request_duration_ms_perreq_p95,\n MAX(feature.nv_inference_queue_duration_ms_perreq_p95) AS nv_inference_queue_duration_ms_perreq_p95,\n MAX(feature.nv_inference_request_success_model_max) AS nv_inference_request_success_model_max,\n MAX(feature.nv_inference_request_failure_model_max) AS nv_inference_request_failure_model_max,\n MAX(feature.dcgm_fi_dev_gpu_util_pod_avg) AS dcgm_fi_dev_gpu_util_pod_avg,\n MAX(feature.k8s_container_bs_rate_cpu_core_used_request_pod_avg) AS k8s_container_bs_rate_cpu_core_used_request_pod_avg,\n MAX(feature.k8s_container_rate_mem_working_set_request_pod_avg) AS k8s_container_rate_mem_working_set_request_pod_avg,\n MAX(feature.k8s_dcgm_fi_dev_fb_util_pod_avg) AS k8s_dcgm_fi_dev_fb_util_pod_avg,\n MAX(feature.k8s_container_vgpu_gpu_util_pod_avg) AS k8s_container_vgpu_gpu_util_pod_avg,\n MAX(CASE WHEN holiday_feature.holiday_date > '' THEN 1 ELSE 0 END) AS feature_holiday_date\n FROM service_list\n CROSS JOIN time_list\n JOIN feature\n ON service_list.service_name = feature.service_name\n AND time_list.agg_type = feature.agg_type\n AND SUBSTRING_INDEX(time_list.agg_time, ' ', -1) = SUBSTRING_INDEX(feature.agg_time, ' ', -1)\n LEFT JOIN {DB_NAME}.{INPUT_TABLE_HOLIDAY} holiday_today\n ON SUBSTRING_INDEX(time_list.agg_time, ' ', 1) = holiday_today.holiday_date\n LEFT JOIN {DB_NAME}.{INPUT_TABLE_HOLIDAY} holiday_feature\n ON SUBSTRING_INDEX(feature.agg_time, ' ', 1) = holiday_feature.holiday_date\n GROUP BY time_list.agg_time, feature.agg_time, feature.agg_type, feature.service_name\n),\nt2 AS (\n SELECT *,\n ROW_NUMBER() OVER (PARTITION BY service_name, agg_time, agg_type ORDER BY feature_holiday_date ASC, nv_inference_count_model_avg ASC) AS r,\n SUM(1) OVER (PARTITION BY service_name, agg_time, agg_type) AS total,\n SUM(feature_holiday_date) OVER (PARTITION BY service_name, agg_time, agg_type) AS feature_holiday_total\n FROM t1\n),\nt3 AS (\n SELECT *,\n total - feature_holiday_total AS feature_weekday_total,\n CEIL((total - feature_holiday_total) * 0.9) AS feature_weekday_index,\n CEIL(feature_holiday_total * 0.9) + (total - feature_holiday_total) AS feature_holiday_index\n FROM t2\n),\nbase AS (\n SELECT '20260507' AS dt,\n instance_uuid,\n service_name,\n workload_name,\n namespace,\n agg_time,\n agg_type,\n today_holiday_date,\n -- MySQL: DAYOFWEEK returns 1=Sunday..7=Saturday; we need 1=Monday..7=Sunday\n MOD(DATEDIFF(SUBSTRING_INDEX(agg_time, ' ', 1), '2019-12-30'), 7) + 1 AS day_of_week,\n 'request_model_count' AS prediction_type,\n nv_inference_count_model_avg,\n CASE\n WHEN today_holiday_date = 1 AND feature_holiday_total > 0 THEN feature_holiday_total\n WHEN today_holiday_date = 1 AND feature_holiday_total = 0 THEN feature_weekday_total\n WHEN today_holiday_date = 0 AND feature_weekday_total > 0 THEN feature_weekday_total\n WHEN today_holiday_date = 0 AND feature_weekday_total = 0 THEN feature_holiday_total\n END AS statistic_time_count,\n nv_inference_request_duration_ms_model_avg,\n nv_inference_queue_duration_ms_model_avg,\n num_queued_reqs_model_avg,\n nv_inference_request_success_model_avg,\n nv_inference_request_failure_model_avg,\n nv_inference_request_duration_ms_perreq_avg,\n nv_inference_queue_duration_ms_perreq_avg,\n nv_inference_request_duration_ms_perreq_p95,\n nv_inference_queue_duration_ms_perreq_p95,\n nv_inference_request_success_model_max,\n nv_inference_request_failure_model_max,\n dcgm_fi_dev_gpu_util_pod_avg,\n k8s_container_bs_rate_cpu_core_used_request_pod_avg,\n k8s_container_rate_mem_working_set_request_pod_avg,\n k8s_dcgm_fi_dev_fb_util_pod_avg,\n k8s_container_vgpu_gpu_util_pod_avg\n FROM t3\n WHERE r = CASE\n WHEN today_holiday_date = 1 AND feature_holiday_total >= 1 THEN feature_holiday_index\n WHEN today_holiday_date = 1 AND feature_holiday_total < 1 THEN feature_weekday_index\n WHEN today_holiday_date = 0 AND feature_weekday_total >= 1 THEN feature_weekday_index\n WHEN today_holiday_date = 0 AND feature_weekday_total < 1 THEN feature_holiday_index\n END\n)\nSELECT dt, instance_uuid, service_name, workload_name, namespace, agg_time, agg_type,\n today_holiday_date AS is_holiday, day_of_week, prediction_type,\n nv_inference_count_model_avg AS nv_inference_count_model_avg_p90, statistic_time_count,\n nv_inference_request_duration_ms_model_avg, nv_inference_queue_duration_ms_model_avg,\n num_queued_reqs_model_avg, nv_inference_request_success_model_avg,\n nv_inference_request_failure_model_avg, nv_inference_request_duration_ms_perreq_avg,\n nv_inference_queue_duration_ms_perreq_avg, nv_inference_request_duration_ms_perreq_p95,\n nv_inference_queue_duration_ms_perreq_p95, nv_inference_request_success_model_max,\n nv_inference_request_failure_model_max, dcgm_fi_dev_gpu_util_pod_avg,\n k8s_container_bs_rate_cpu_core_used_request_pod_avg,\n k8s_container_rate_mem_working_set_request_pod_avg,\n k8s_dcgm_fi_dev_fb_util_pod_avg, k8s_container_vgpu_gpu_util_pod_avg\nFROM base\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n `dt` VARCHAR(256) COMMENT '分区时间(YYYYMMDD)',\n `instance_uuid` VARCHAR(256) COMMENT '实例UUID',\n `service_name` VARCHAR(256) COMMENT '服务名称',\n `workload_name` VARCHAR(256) COMMENT '工作负载名称',\n `namespace` VARCHAR(256) COMMENT '命名空间',\n `agg_time` VARCHAR(256) COMMENT '预测时间点',\n `agg_type` BIGINT COMMENT '1:10分钟粒度，2:小时粒度',\n `is_holiday` BIGINT COMMENT '是否节假日:0-1',\n `day_of_week` BIGINT COMMENT '星期几:1-7',\n `prediction_type` VARCHAR(256) COMMENT '预测模型',\n `nv_inference_count_model_avg_p90` DOUBLE COMMENT '预估流量P90',\n `statistic_time_count` BIGINT COMMENT '参与预估的时间点个数',\n `nv_inference_request_duration_ms_model_avg` DOUBLE,\n `nv_inference_queue_duration_ms_model_avg` DOUBLE,\n `num_queued_reqs_model_avg` DOUBLE,\n `nv_inference_request_success_model_avg` DOUBLE,\n `nv_inference_request_failure_model_avg` DOUBLE,\n `nv_inference_request_duration_ms_perreq_avg` DOUBLE,\n `nv_inference_queue_duration_ms_perreq_avg` DOUBLE,\n `nv_inference_request_duration_ms_perreq_p95` DOUBLE,\n `nv_inference_queue_duration_ms_perreq_p95` DOUBLE,\n `nv_inference_request_success_model_max` DOUBLE,\n `nv_inference_request_failure_model_max` DOUBLE,\n `DCGM_FI_DEV_GPU_UTIL_pod_avg` DOUBLE,\n `k8s_container_bs_rate_cpu_core_used_request_pod_avg` DOUBLE,\n `k8s_container_rate_mem_working_set_request_pod_avg` DOUBLE,\n `k8s_dcgm_fi_dev_fb_util_pod_avg` DOUBLE,\n `k8s_container_vgpu_gpu_util_pod_avg` DOUBLE\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n\n # Verify row count\n with conn.cursor() as cur:\n cur.execute(f\"SELECT COUNT(*) FROM {DB_NAME}.{OUTPUT_TABLE}\")\n count = cur.fetchone()[0]\n print(f\"mysql_005 ground_truth done: {count} rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_005_en"} {"task_id": "mysql_006", "id": "offline-compute_MySQL_mysql_006", "name": "推理服务流量与资源预测-P90分位数与节假日降级", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\n我需要你生成一段 MySQL 代码，以20260507为预测基准日，为所有在线推理服务生成未来14天的流量与资源使用量预测数据。预测采用断点检测（CPD）结果之后的历史数据，区分节假日/工作日类型计算P90分位数，当某类型历史数据不足时降级使用另一类型数据，输出10分钟和小时两种时间粒度的预测结果。\n\n**输入表（全名 + 简要描述）**：\n\n1. `internal_platform_db.dwm_gputj_platform_gpu_base_feature_agg_v2_mysql_006`（历史特征事实表）\n2. `internal_platform_db.dwd_aide_inferencev2_done_service_info_h_mysql_006`（服务信息维表）\n3. `internal_platform_db.dwd_gputj_platform_metric_cpd_offline_v2_mysql_006`（断点检测结果表）\n4. `internal_platform_db.dim_holiday_list_mysql_006`（节假日维表）\n\n（各表结构与字段含义请自行连接数据库查询确认）\n\n**处理规则**：\n1. **数据过滤**：历史特征数据需满足`nv_inference_count_model_avg >= 0`且`agg_type IN (1, 2)`；断点数据需满足`msg = 'success'`；服务名不能为空。\n2. **关联逻辑**：\n - 生成未来14天所有时间点与服务列表的笛卡尔积。\n - 历史特征数据先与断点表内关联（`service_name`相等，`agg_time >= 断点时间`），只保留断点后记录。\n - 关联结果再与未来时间点关联，条件为`service_name`相等、`agg_type`相等、时间部分（HH:mm）相等。\n - 未来时间点日期和历史记录日期分别左关联节假日表判断日期类型。\n - 最终输出前左关联断点表获取`cpd_agg_time`。\n3. **P90计算与降级口径**：按服务、未来时间点、时间粒度分组后：\n - 组内对各项性能指标取`max`值。\n - 标记每条历史记录是否为节假日（1是0否），统计组内总记录数`total`、节假日记录数`feature_holiday_total`，计算工作日记录数`feature_weekday_total = total - feature_holiday_total`。\n - 按规则生成排名`r`：排序规则为`(是否为节假日 asc, nv_inference_count_model_avg asc)`。\n - **降级逻辑**：预测日为节假日且`feature_holiday_total >=1`时，取`r = CEIL(feature_holiday_total*0.9) + feature_weekday_total`；预测日为节假日但`feature_holiday_total <1`时，降级取`r = CEIL(feature_weekday_total*0.9)`；预测日为工作日且`feature_weekday_total >=1`时，取`r = CEIL(feature_weekday_total*0.9)`；预测日为工作日但`feature_weekday_total <1`时，降级取`r = CEIL(feature_holiday_total*0.9) + feature_weekday_total`。\n - 历史时间点数量`statistic_time_count`按降级逻辑取对应的`feature_holiday_total`或`feature_weekday_total`。\n4. **派生字段**：`is_holiday`根据未来时间点日期是否在节假日表判断（1是0否）；`day_of_week`通过`MOD(DATEDIFF(预测日期, '2019-12-30'), 7) + 1`计算；`prediction_type`固定为'request_model_count'。\n\n**输出要求**：\n- 目标表：`internal_platform_db.dwm_gputj_platform_model_prediction_long_cpd_mysql_006`\n- 输出字段顺序：分区时间(YYYYMMDD)、实例UUID、服务名称、工作负载名称、命名空间、预测时间点、时间粒度(1:10分钟，2:小时)、是否节假日、周几、预测模型类型、预估流量(P90)、参与计算的历史时间点个数、请求持续时长均值、队列持续时长均值、排队请求数均值、请求成功率均值、请求失败率均值、单请求持续时长均值、单请求队列时长均值、单请求持续时长P95、单请求队列时长P95、请求成功率最大值、请求失败率最大值、GPU利用率均值、CPU使用率均值、内存使用率均值、GPU显存利用率均值、vGPU利用率均值、断点时间、历史特征总数、历史节假日特征数\n- 分区字段`dt`，本次任务分区值为'20260507'\n- 如果目标表不存在，请在`internal_platform_db`库下按MySQL InnoDB标准建表，再写入数据\n- 请使用标准 MySQL 语法，不要使用 Hive/Spark SQL 方言\n\n**环境与执行说明**：\n- 你的最终产出必须写入文件 `/tmp_workspace/result.py`，不能写 result.sql\n- 本机 MySQL 已在 localhost:3306 运行，用户名 root，密码 root123\n- 请使用 Python pymysql 在 result.py 中执行 SQL（不要用 mysql 命令行）\n- 脚本需要包含完整的建表（如目标表不存在）+ 写入数据的逻辑\n- 写出 result.py 后，你必须自己执行 `python3 /tmp_workspace/result.py` 验证它能成功运行并产出正确数据", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_006 ground truth: 推理服务流量与资源预测-P90分位数与节假日降级\n\nTask:\n 以20260507为预测基准日，为所有在线推理服务生成未来14天的流量与资源使用量预测数据。\n 预测采用断点检测（CPD）结果之后的历史数据，区分节假日/工作日类型计算P90分位数，\n 当某类型历史数据不足时降级使用另一类型数据，输出10分钟和小时两种时间粒度的预测结果。\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE_FEATURE = \"dwm_gputj_platform_gpu_base_feature_agg_v2_mysql_006\"\nINPUT_TABLE_SERVICE = \"dwd_aide_inferencev2_done_service_info_h_mysql_006\"\nINPUT_TABLE_CPD = \"dwd_gputj_platform_metric_cpd_offline_v2_mysql_006\"\nINPUT_TABLE_HOLIDAY = \"dim_holiday_list_mysql_006\"\nOUTPUT_TABLE = \"dwm_gputj_platform_model_prediction_long_cpd_mysql_006\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (instance_uuid, service_name, workload_name, namespace, agg_time, agg_type,\n is_holiday, day_of_week, prediction_type, nv_inference_count_model_avg_p90,\n statistic_time_count, nv_inference_request_duration_ms_model_avg,\n nv_inference_queue_duration_ms_model_avg, num_queued_reqs_model_avg,\n nv_inference_request_success_model_avg, nv_inference_request_failure_model_avg,\n nv_inference_request_duration_ms_perreq_avg, nv_inference_queue_duration_ms_perreq_avg,\n nv_inference_request_duration_ms_perreq_p95, nv_inference_queue_duration_ms_perreq_p95,\n nv_inference_request_success_model_max, nv_inference_request_failure_model_max,\n DCGM_FI_DEV_GPU_UTIL_pod_avg, k8s_container_bs_rate_cpu_core_used_request_pod_avg,\n k8s_container_rate_mem_working_set_request_pod_avg, k8s_dcgm_fi_dev_fb_util_pod_avg,\n k8s_container_vgpu_gpu_util_pod_avg, cpd_agg_time, feature_total, feature_holiday_total, dt)\nWITH RECURSIVE offsets AS (\n SELECT 0 AS n\n UNION ALL SELECT n + 1 FROM offsets WHERE n < 13\n),\nfuture_dates AS (\n SELECT DATE_ADD(STR_TO_DATE('20260507', '%Y%m%d'), INTERVAL n DAY) AS target_date\n FROM offsets\n),\ntime_template AS (\n SELECT SUBSTRING_INDEX(agg_time, ' ', -1) AS time_part, agg_type\n FROM {DB_NAME}.{INPUT_TABLE_FEATURE}\n WHERE dt >= '20260502' AND dt < '20260507' AND agg_time > ''\n GROUP BY SUBSTRING_INDEX(agg_time, ' ', -1), agg_type\n),\ntime_list AS (\n SELECT CONCAT(fd.target_date, ' ', tt.time_part) AS agg_time, tt.agg_type, fd.target_date\n FROM future_dates fd\n CROSS JOIN time_template tt\n),\nservice_list AS (\n SELECT DISTINCT name AS service_name\n FROM {DB_NAME}.{INPUT_TABLE_SERVICE}\n WHERE dt = '2026050723' AND name <> ''\n),\ncpd AS (\n SELECT service_name, agg_time\n FROM (\n SELECT service_name, agg_time,\n ROW_NUMBER() OVER (PARTITION BY service_name ORDER BY dt DESC, agg_time DESC) AS r1\n FROM (\n SELECT service_name, dt, agg_time,\n ROW_NUMBER() OVER (PARTITION BY service_name, dt, metric_name ORDER BY metric_value ASC) AS r\n FROM {DB_NAME}.{INPUT_TABLE_CPD}\n WHERE dt <= '20260507' AND dt >= '20260421'\n AND msg = 'success'\n AND agg_time < '20260507'\n ) t0\n WHERE r = 1\n ) t\n WHERE r1 = 1\n),\nfeature AS (\n SELECT t.service_name, instance_uuid, trial_job_name AS workload_name, namespace,\n t.agg_time, agg_type, nv_inference_count_model_avg,\n nv_inference_request_duration_ms_model_avg, nv_inference_queue_duration_ms_model_avg,\n num_queued_reqs_model_avg, nv_inference_request_success_model_avg,\n nv_inference_request_failure_model_avg, nv_inference_request_duration_ms_perreq_avg,\n nv_inference_queue_duration_ms_perreq_avg, nv_inference_request_duration_ms_perreq_p95,\n nv_inference_queue_duration_ms_perreq_p95, nv_inference_request_success_model_max,\n nv_inference_request_failure_model_max, dcgm_fi_dev_gpu_util_pod_avg,\n k8s_container_bs_rate_cpu_core_used_request_pod_avg,\n k8s_container_rate_mem_working_set_request_pod_avg,\n k8s_dcgm_fi_dev_fb_util_pod_avg, k8s_container_vgpu_gpu_util_pod_avg\n FROM {DB_NAME}.{INPUT_TABLE_FEATURE} t\n JOIN cpd ON cpd.service_name = t.service_name AND cpd.agg_time <= t.agg_time\n WHERE dt >= '20260421' AND dt <= '20260507'\n AND nv_inference_count_model_avg >= 0\n AND agg_type IN (1, 2)\n),\nbase AS (\n SELECT '20260507' AS dt,\n instance_uuid, service_name, workload_name, namespace, agg_time, agg_type,\n today_holiday_date,\n MOD(DATEDIFF(SUBSTRING_INDEX(agg_time, ' ', 1), '2019-12-30'), 7) + 1 AS day_of_week,\n 'request_model_count' AS prediction_type,\n nv_inference_count_model_avg,\n CASE\n WHEN today_holiday_date = 1 AND feature_holiday_total > 0 THEN feature_holiday_total\n WHEN today_holiday_date = 1 AND feature_holiday_total = 0 THEN feature_weekday_total\n WHEN today_holiday_date = 0 AND feature_weekday_total > 0 THEN feature_weekday_total\n WHEN today_holiday_date = 0 AND feature_weekday_total = 0 THEN feature_holiday_total\n END AS statistic_time_count,\n nv_inference_request_duration_ms_model_avg, nv_inference_queue_duration_ms_model_avg,\n num_queued_reqs_model_avg, nv_inference_request_success_model_avg,\n nv_inference_request_failure_model_avg, nv_inference_request_duration_ms_perreq_avg,\n nv_inference_queue_duration_ms_perreq_avg, nv_inference_request_duration_ms_perreq_p95,\n nv_inference_queue_duration_ms_perreq_p95, nv_inference_request_success_model_max,\n nv_inference_request_failure_model_max, dcgm_fi_dev_gpu_util_pod_avg,\n k8s_container_bs_rate_cpu_core_used_request_pod_avg,\n k8s_container_rate_mem_working_set_request_pod_avg,\n k8s_dcgm_fi_dev_fb_util_pod_avg, k8s_container_vgpu_gpu_util_pod_avg,\n total AS feature_total, feature_holiday_total\n FROM (\n SELECT *,\n total - feature_holiday_total AS feature_weekday_total,\n CEIL((total - feature_holiday_total) * 0.9) AS feature_weekday_index,\n CEIL(feature_holiday_total * 0.9) + (total - feature_holiday_total) AS feature_holiday_index\n FROM (\n SELECT *,\n ROW_NUMBER() OVER (PARTITION BY service_name, agg_time, agg_type\n ORDER BY feature_holiday_date ASC, nv_inference_count_model_avg ASC) AS r,\n COUNT(*) OVER (PARTITION BY service_name, agg_time, agg_type) AS total,\n SUM(feature_holiday_date) OVER (PARTITION BY service_name, agg_time, agg_type) AS feature_holiday_total\n FROM (\n SELECT time_list.agg_time, feature.agg_type,\n MAX(feature.instance_uuid) AS instance_uuid,\n feature.service_name,\n MAX(feature.workload_name) AS workload_name,\n MAX(feature.namespace) AS namespace,\n feature.agg_time AS feature_agg_time,\n MAX(CASE WHEN holiday_today.holiday_date > '' THEN 1 ELSE 0 END) AS today_holiday_date,\n MAX(feature.nv_inference_count_model_avg) AS nv_inference_count_model_avg,\n MAX(feature.nv_inference_request_duration_ms_model_avg) AS nv_inference_request_duration_ms_model_avg,\n MAX(feature.nv_inference_queue_duration_ms_model_avg) AS nv_inference_queue_duration_ms_model_avg,\n MAX(feature.num_queued_reqs_model_avg) AS num_queued_reqs_model_avg,\n MAX(feature.nv_inference_request_success_model_avg) AS nv_inference_request_success_model_avg,\n MAX(feature.nv_inference_request_failure_model_avg) AS nv_inference_request_failure_model_avg,\n MAX(feature.nv_inference_request_duration_ms_perreq_avg) AS nv_inference_request_duration_ms_perreq_avg,\n MAX(feature.nv_inference_queue_duration_ms_perreq_avg) AS nv_inference_queue_duration_ms_perreq_avg,\n MAX(feature.nv_inference_request_duration_ms_perreq_p95) AS nv_inference_request_duration_ms_perreq_p95,\n MAX(feature.nv_inference_queue_duration_ms_perreq_p95) AS nv_inference_queue_duration_ms_perreq_p95,\n MAX(feature.nv_inference_request_success_model_max) AS nv_inference_request_success_model_max,\n MAX(feature.nv_inference_request_failure_model_max) AS nv_inference_request_failure_model_max,\n MAX(feature.dcgm_fi_dev_gpu_util_pod_avg) AS dcgm_fi_dev_gpu_util_pod_avg,\n MAX(feature.k8s_container_bs_rate_cpu_core_used_request_pod_avg) AS k8s_container_bs_rate_cpu_core_used_request_pod_avg,\n MAX(feature.k8s_container_rate_mem_working_set_request_pod_avg) AS k8s_container_rate_mem_working_set_request_pod_avg,\n MAX(feature.k8s_dcgm_fi_dev_fb_util_pod_avg) AS k8s_dcgm_fi_dev_fb_util_pod_avg,\n MAX(feature.k8s_container_vgpu_gpu_util_pod_avg) AS k8s_container_vgpu_gpu_util_pod_avg,\n MAX(CASE WHEN holiday_feature.holiday_date > '' THEN 1 ELSE 0 END) AS feature_holiday_date\n FROM service_list\n CROSS JOIN time_list\n JOIN feature ON service_list.service_name = feature.service_name\n AND time_list.agg_type = feature.agg_type\n AND SUBSTRING_INDEX(time_list.agg_time, ' ', -1) = SUBSTRING_INDEX(feature.agg_time, ' ', -1)\n LEFT JOIN {DB_NAME}.{INPUT_TABLE_HOLIDAY} holiday_today\n ON SUBSTRING_INDEX(time_list.agg_time, ' ', 1) = holiday_today.holiday_date\n LEFT JOIN {DB_NAME}.{INPUT_TABLE_HOLIDAY} holiday_feature\n ON SUBSTRING_INDEX(feature.agg_time, ' ', 1) = holiday_feature.holiday_date\n GROUP BY time_list.agg_time, feature.agg_time, feature.agg_type, feature.service_name\n ) t1\n ) t2\n ) t3\n WHERE r = CASE\n WHEN today_holiday_date = 1 AND feature_holiday_total >= 1 THEN feature_holiday_index\n WHEN today_holiday_date = 1 AND feature_holiday_total < 1 THEN feature_weekday_index\n WHEN today_holiday_date = 0 AND feature_weekday_total >= 1 THEN feature_weekday_index\n WHEN today_holiday_date = 0 AND feature_weekday_total < 1 THEN feature_holiday_index\n END\n)\nSELECT instance_uuid, base.service_name, base.workload_name, base.namespace,\n base.agg_time, agg_type, today_holiday_date, day_of_week, prediction_type,\n nv_inference_count_model_avg, statistic_time_count,\n nv_inference_request_duration_ms_model_avg, nv_inference_queue_duration_ms_model_avg,\n num_queued_reqs_model_avg, nv_inference_request_success_model_avg,\n nv_inference_request_failure_model_avg, nv_inference_request_duration_ms_perreq_avg,\n nv_inference_queue_duration_ms_perreq_avg, nv_inference_request_duration_ms_perreq_p95,\n nv_inference_queue_duration_ms_perreq_p95, nv_inference_request_success_model_max,\n nv_inference_request_failure_model_max, dcgm_fi_dev_gpu_util_pod_avg,\n k8s_container_bs_rate_cpu_core_used_request_pod_avg,\n k8s_container_rate_mem_working_set_request_pod_avg,\n k8s_dcgm_fi_dev_fb_util_pod_avg, k8s_container_vgpu_gpu_util_pod_avg,\n cpd.agg_time AS cpd_agg_time, feature_total, feature_holiday_total, base.dt\nFROM base\nLEFT JOIN cpd ON base.service_name = cpd.service_name\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n instance_uuid VARCHAR(256),\n service_name VARCHAR(256),\n workload_name VARCHAR(256),\n namespace VARCHAR(256),\n agg_time VARCHAR(256),\n agg_type BIGINT,\n is_holiday BIGINT,\n day_of_week BIGINT,\n prediction_type VARCHAR(256),\n nv_inference_count_model_avg_p90 DOUBLE,\n statistic_time_count BIGINT,\n nv_inference_request_duration_ms_model_avg DOUBLE,\n nv_inference_queue_duration_ms_model_avg DOUBLE,\n num_queued_reqs_model_avg DOUBLE,\n nv_inference_request_success_model_avg DOUBLE,\n nv_inference_request_failure_model_avg DOUBLE,\n nv_inference_request_duration_ms_perreq_avg DOUBLE,\n nv_inference_queue_duration_ms_perreq_avg DOUBLE,\n nv_inference_request_duration_ms_perreq_p95 DOUBLE,\n nv_inference_queue_duration_ms_perreq_p95 DOUBLE,\n nv_inference_request_success_model_max DOUBLE,\n nv_inference_request_failure_model_max DOUBLE,\n DCGM_FI_DEV_GPU_UTIL_pod_avg DOUBLE,\n k8s_container_bs_rate_cpu_core_used_request_pod_avg DOUBLE,\n k8s_container_rate_mem_working_set_request_pod_avg DOUBLE,\n k8s_dcgm_fi_dev_fb_util_pod_avg DOUBLE,\n k8s_container_vgpu_gpu_util_pod_avg DOUBLE,\n cpd_agg_time VARCHAR(256),\n feature_total BIGINT,\n feature_holiday_total BIGINT,\n dt VARCHAR(256)\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n\n # Verify row count\n with conn.cursor() as cur:\n cur.execute(f\"SELECT COUNT(*) FROM {DB_NAME}.{OUTPUT_TABLE}\")\n count = cur.fetchone()[0]\n print(f\"mysql_006 ground_truth done: {count} rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_006"} {"task_id": "mysql_007", "id": "offline-compute_MySQL_mysql_007", "name": "热表治理元数据宽表汇总", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\n我需要你生成一段 MySQL 代码，从表治理明细中筛选有热度的正式表，按库名+表名去重汇总治理指标，形成热表元数据宽表。\n\n**业务背景与目标**：数据平台需要从表治理明细表中筛选出有热度（heat > 0）的正式表（排除表名含 'temp' 的临时表和库名含 'test' 的测试库），按 db_name + table_name 分组去重，对其余所有字段取 MAX，形成热表元数据宽表写入输出表。\n\n**输入表（全名 + 简要描述）**：\n- `internal_platform_db.ads_gov_cost_table_govern_detail_df_mysql_007`（表治理明细表）\n\n（表结构与字段含义请自行连接数据库查询确认）\n\n**过滤条件**：\n- `imp_date = 20260507`\n- `heat > 0`\n- `table_name` 不包含 'temp'（即 `table_name NOT LIKE '%temp%'`）\n- `db_name` 不包含 'test'（即 `db_name NOT LIKE '%test%'`）\n\n**聚合逻辑**：\n- 按 `db_name`, `table_name` 分组\n- 对其余所有字段取 MAX\n\n**输出要求**：\n- 目标表：`internal_platform_db.dwd_hot_metadata_table_cand_mysql_007`\n- 输出字段及顺序：`dt`, `db_name`, `table_name`, `owner`, `storage_detail`, `heat`, `read_gap_days`, `write_gap_days`, `last_date_partition`, `is_table_no_comment`, `is_table_comment_irregular`, `is_all_column_no_comment`, `is_part_column_no_comment`, `table_first_group`, `table_second_group`, `dw_appgroup`, `appgroup_obs_product_id`, `appgroup_obs_plan_id`, `is_partition`, `create_days`\n- `dt` 字段值为 `'20260507'`\n- 如果目标表不存在，请先按 MySQL InnoDB 标准建表，再写入数据\n- 请使用标准 MySQL 语法（INSERT INTO ... SELECT），不要使用 Hive/Spark SQL 方言（如 INSERT OVERWRITE、STORED AS ORC 等）\n\n**环境与执行说明**：\n- 你的最终产出必须写入文件 `/tmp_workspace/result.py`，不能写 result.sql\n- 本机 MySQL 已在 localhost:3306 运行，用户名 root，密码 root123\n- 请使用 Python pymysql 在 result.py 中执行 SQL（不要用 mysql 命令行）\n- 脚本需要包含完整的建表（如目标表不存在）+ 写入数据的逻辑\n- 写出 result.py 后，你必须自己执行 `python3 /tmp_workspace/result.py` 验证它能成功运行并产出正确数据", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_007 ground truth: 热表治理元数据宽表汇总\n\nTask:\n Filter input table WHERE imp_date = 20260507 AND heat > 0\n AND table_name NOT LIKE '%temp%' AND db_name NOT LIKE '%test%',\n group by db_name, table_name, take MAX of all other fields,\n write to output table with dt = '20260507'.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"ads_gov_cost_table_govern_detail_df_mysql_007\"\nOUTPUT_TABLE = \"dwd_hot_metadata_table_cand_mysql_007\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (dt, db_name, table_name, owner, storage_detail, heat,\n read_gap_days, write_gap_days, last_date_partition,\n is_table_no_comment, is_table_comment_irregular,\n is_all_column_no_comment, is_part_column_no_comment,\n table_first_group, table_second_group, dw_appgroup,\n appgroup_obs_product_id, appgroup_obs_plan_id,\n is_partition, create_days)\nSELECT\n '20260507' AS dt,\n db_name,\n table_name,\n MAX(owner) AS owner,\n MAX(storage_detail) AS storage_detail,\n MAX(heat) AS heat,\n MAX(read_gap_days) AS read_gap_days,\n MAX(write_gap_days) AS write_gap_days,\n MAX(last_date_partition) AS last_date_partition,\n MAX(is_table_no_comment) AS is_table_no_comment,\n MAX(is_table_comment_irregular) AS is_table_comment_irregular,\n MAX(is_all_column_no_comment) AS is_all_column_no_comment,\n MAX(is_part_column_no_comment) AS is_part_column_no_comment,\n MAX(table_first_group) AS table_first_group,\n MAX(table_second_group) AS table_second_group,\n MAX(dw_appgroup) AS dw_appgroup,\n MAX(appgroup_obs_product_id) AS appgroup_obs_product_id,\n MAX(appgroup_obs_plan_id) AS appgroup_obs_plan_id,\n MAX(is_partition) AS is_partition,\n MAX(create_days) AS create_days\nFROM {DB_NAME}.{INPUT_TABLE}\nWHERE imp_date = 20260507\n AND heat > 0\n AND table_name NOT LIKE '%temp%'\n AND db_name NOT LIKE '%test%'\nGROUP BY db_name, table_name\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n dt VARCHAR(16) DEFAULT NULL COMMENT '日期分区',\n db_name VARCHAR(256) DEFAULT NULL COMMENT '库名',\n table_name VARCHAR(256) DEFAULT NULL COMMENT '表名',\n owner VARCHAR(256) DEFAULT NULL COMMENT '第一责任人',\n storage_detail DOUBLE DEFAULT NULL COMMENT '存储大小含副本',\n heat BIGINT DEFAULT NULL COMMENT '90天热度',\n read_gap_days BIGINT DEFAULT NULL COMMENT '多久未读',\n write_gap_days BIGINT DEFAULT NULL COMMENT '多久未写',\n last_date_partition VARCHAR(256) DEFAULT NULL COMMENT '最后一个分区',\n is_table_no_comment BIGINT DEFAULT NULL COMMENT '表无描述',\n is_table_comment_irregular BIGINT DEFAULT NULL COMMENT '表描述过短或等于表名',\n is_all_column_no_comment BIGINT DEFAULT NULL COMMENT '所有字段都无描述',\n is_part_column_no_comment BIGINT DEFAULT NULL COMMENT '部分字段无描述',\n table_first_group VARCHAR(256) DEFAULT NULL COMMENT '一级应用组',\n table_second_group VARCHAR(256) DEFAULT NULL COMMENT '二级应用组',\n dw_appgroup VARCHAR(256) DEFAULT NULL COMMENT '数据仓库DW应用组',\n appgroup_obs_product_id VARCHAR(256) DEFAULT NULL COMMENT '运营产品ID',\n appgroup_obs_plan_id VARCHAR(256) DEFAULT NULL COMMENT '规划产品ID',\n is_partition BIGINT DEFAULT NULL COMMENT '是否分区表',\n create_days BIGINT DEFAULT NULL COMMENT '建表距今天数',\n PRIMARY KEY (db_name, table_name)\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_007 ground_truth done: rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_007"} {"task_id": "mysql_008", "id": "offline-compute_MySQL_mysql_008", "name": "消费组治理项明细提取", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\n任务目标：从消费特征增量表提取近90天有消费量且非 reader 类型的消费组治理项明细。\n\n**输入表**：`internal_platform_db.dws_mq_consumption_feature_d_increase_mysql_008`（消费特征增量表）\n\n（表结构与字段含义请自行连接数据库查询确认）\n\n**处理规则**：\n\n1. 过滤条件：`dt = '20260507'` AND `(total_consume_last_90d <> 0 AND total_consume_last_90d IS NOT NULL)` AND `is_reader = 0`\n2. 派生字段：\n - `mq_full_topic`：若 `tenant` 和 `namespaces` 均非 NULL，拼接 `'persistent://' + tenant + '/' + namespaces + '/' + topic`；否则取 `topic`\n - `app_group`：取自 `dw_appgroup`\n - `consumergroup_incharge`：`COALESCE(consumergroup_incharge, bid_incharge)`，即消费组负责人为空时取 bid 负责人\n - `consumergroup_description`：取自 `usage_desc`\n - `hitted_gov_items`：固定 NULL\n - `governance_benefit_estimate`：固定 NULL\n3. 无 Join，单表处理\n\n**输出要求**：\n\n- 目标表：`internal_platform_db.ads_mq_consumergroup_governance_item_d_cand_mysql_008`\n- 输出字段顺序为：`dt`、`business_id`、`business_name`、`cluster_set`、`mq_full_topic`、`topic`、`consumergroup`、`system_belong`、`bg`、`category_name`、`app_group`、`consumergroup_incharge`、`last_operator`、`consumergroup_description`、`create_time`、`modify_time`、`bid_incharge`、`cluster_id`、`has_metadata`、`total_produce_pkg_d`、`total_consume_pkg_d`、`consume_ratio`、`total_produce_pkg_last_7d`、`total_produce_pkg_last_30d`、`total_produce_pkg_last_90d`、`total_consume_last_7d`、`consume_ratio_last_7d`、`backlog_ratio_last_7d`、`total_consume_last_30d`、`consume_ratio_last_30d`、`backlog_ratio_last_30d`、`total_consume_last_90d`、`consume_ratio_last_90d`、`backlog_ratio_last_90d`、`backlog_days_last_30d`、`hitted_gov_items`、`governance_benefit_estimate`\n- 字段类型：\n - `dt` VARCHAR(8)\n - `mq_full_topic` VARCHAR(512)\n - `app_group` VARCHAR(256)\n - `consumergroup_incharge` VARCHAR(256)\n - `consumergroup_description` VARCHAR(256)\n - `has_metadata` TINYINT\n - `total_produce_pkg_d` BIGINT\n - `total_consume_pkg_d` BIGINT\n - `consume_ratio` VARCHAR(256)\n - `total_produce_pkg_last_7d` BIGINT\n - `total_produce_pkg_last_30d` BIGINT\n - `total_produce_pkg_last_90d` BIGINT\n - `total_consume_last_7d` BIGINT\n - `consume_ratio_last_7d` VARCHAR(256)\n - `backlog_ratio_last_7d` VARCHAR(256)\n - `total_consume_last_30d` BIGINT\n - `consume_ratio_last_30d` VARCHAR(256)\n - `backlog_ratio_last_30d` VARCHAR(256)\n - `total_consume_last_90d` BIGINT\n - `consume_ratio_last_90d` VARCHAR(256)\n - `backlog_ratio_last_90d` VARCHAR(256)\n - `backlog_days_last_30d` INT\n - `hitted_gov_items` VARCHAR(256)\n - `governance_benefit_estimate` DOUBLE\n - 其余字符串字段均为 VARCHAR(256)\n- 写入方式：使用 `INSERT INTO ... SELECT ...` 写入目标表\n- 如果目标表不存在，请先按 MySQL InnoDB 标准建表，再写入数据\n- 请使用标准 MySQL 语法，不要使用 Hive/Spark SQL 方言\n\n**环境与执行说明**：\n- 你的最终产出必须写入文件 `/tmp_workspace/result.py`，不能写 result.sql\n- 本机 MySQL 已在 localhost:3306 运行，用户名 root，密码 root123\n- 请使用 Python pymysql 在 result.py 中执行 SQL（不要用 mysql 命令行）\n- 脚本需要包含完整的建表（如目标表不存在）+ 写入数据的逻辑\n- 写出 result.py 后，你必须自己执行 `python3 /tmp_workspace/result.py` 验证它能成功运行并产出正确数据", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_008 ground truth: 消费组治理项明细提取\n\nTask:\n Filter input table WHERE dt = '20260507'\n AND (total_consume_last_90d <> 0 AND total_consume_last_90d IS NOT NULL)\n AND is_reader = 0\n Derive mq_full_topic, app_group, consumergroup_incharge,\n consumergroup_description, hitted_gov_items, governance_benefit_estimate\n Write to output table.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"dws_mq_consumption_feature_d_increase_mysql_008\"\nOUTPUT_TABLE = \"ads_mq_consumergroup_governance_item_d_cand_mysql_008\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (dt, business_id, business_name, cluster_set, mq_full_topic, topic,\n consumergroup, system_belong, bg, category_name, app_group,\n consumergroup_incharge, last_operator, consumergroup_description,\n create_time, modify_time, bid_incharge, cluster_id, has_metadata,\n total_produce_pkg_d, total_consume_pkg_d, consume_ratio,\n total_produce_pkg_last_7d, total_produce_pkg_last_30d, total_produce_pkg_last_90d,\n total_consume_last_7d, consume_ratio_last_7d, backlog_ratio_last_7d,\n total_consume_last_30d, consume_ratio_last_30d, backlog_ratio_last_30d,\n total_consume_last_90d, consume_ratio_last_90d, backlog_ratio_last_90d,\n backlog_days_last_30d, hitted_gov_items, governance_benefit_estimate)\nSELECT\n '20260507' AS dt,\n business_id,\n business_name,\n cluster_set,\n IF(tenant IS NOT NULL AND namespaces IS NOT NULL,\n CONCAT('persistent://', tenant, '/', namespaces, '/', topic),\n topic) AS mq_full_topic,\n topic,\n consumergroup,\n system_belong,\n bg,\n category_name,\n dw_appgroup AS app_group,\n COALESCE(consumergroup_incharge, bid_incharge) AS consumergroup_incharge,\n last_operator,\n usage_desc AS consumergroup_description,\n create_time,\n modify_time,\n bid_incharge,\n cluster_id,\n has_metadata,\n total_produce_pkg_d,\n total_consume_pkg_d,\n consume_ratio,\n total_produce_pkg_last_7d,\n total_produce_pkg_last_30d,\n total_produce_pkg_last_90d,\n total_consume_last_7d,\n consume_ratio_last_7d,\n backlog_ratio_last_7d,\n total_consume_last_30d,\n consume_ratio_last_30d,\n backlog_ratio_last_30d,\n total_consume_last_90d,\n consume_ratio_last_90d,\n backlog_ratio_last_90d,\n backlog_days_last_30d,\n NULL AS hitted_gov_items,\n NULL AS governance_benefit_estimate\nFROM {DB_NAME}.{INPUT_TABLE}\nWHERE dt = '20260507'\n AND (total_consume_last_90d <> 0 AND total_consume_last_90d IS NOT NULL)\n AND is_reader = 0\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n dt VARCHAR(8) COMMENT '天分区',\n business_id VARCHAR(256) COMMENT '消费组所属的business_id',\n business_name VARCHAR(256) COMMENT 'business_name',\n cluster_set VARCHAR(256) COMMENT 'cluster_set',\n mq_full_topic VARCHAR(512) COMMENT 'mq的完整topic名',\n topic VARCHAR(256) COMMENT '消费组消费的topic',\n consumergroup VARCHAR(256) COMMENT '消费组',\n system_belong VARCHAR(256) COMMENT 'bid所属的系统（数据总线/流处理平台）',\n bg VARCHAR(256) COMMENT '消费组所属的bg',\n category_name VARCHAR(256) COMMENT '消费组所归属的产品',\n app_group VARCHAR(256) COMMENT '消费组所属的应用组',\n consumergroup_incharge VARCHAR(256) COMMENT '消费组负责人',\n last_operator VARCHAR(256) COMMENT '消费组最后一次的操作人',\n consumergroup_description VARCHAR(256) COMMENT '消费组使用描述',\n create_time VARCHAR(256) COMMENT '消费组创建时间',\n modify_time VARCHAR(256) COMMENT '消费组修改时间',\n bid_incharge VARCHAR(256) COMMENT 'bid负责人',\n cluster_id VARCHAR(256) COMMENT 'bid所属集群id',\n has_metadata TINYINT COMMENT '消费组是否在数据总线/流处理平台中有元数据',\n total_produce_pkg_d BIGINT COMMENT '当天上游的消息数的总量',\n total_consume_pkg_d BIGINT COMMENT '当天消费者消费的消息数的总量',\n consume_ratio VARCHAR(256) COMMENT '当天消费组的消费生产比例(%)',\n total_produce_pkg_last_7d BIGINT COMMENT '近7天topic生产的消息数的总量',\n total_produce_pkg_last_30d BIGINT COMMENT '近30天topic生产的消息数的总量',\n total_produce_pkg_last_90d BIGINT COMMENT '近90天topic生产的消息数的总量',\n total_consume_last_7d BIGINT COMMENT '消费组近7天总消费量',\n consume_ratio_last_7d VARCHAR(256) COMMENT '消费组近7天的生产消费比例(%)',\n backlog_ratio_last_7d VARCHAR(256) COMMENT '消费组近7天的生产积压比例(%)',\n total_consume_last_30d BIGINT COMMENT '消费组近30天总消费量',\n consume_ratio_last_30d VARCHAR(256) COMMENT '消费组近30天的生产消费比例(%)',\n backlog_ratio_last_30d VARCHAR(256) COMMENT '消费组近30天的生产积压比例(%)',\n total_consume_last_90d BIGINT COMMENT '消费组近90天总消费量',\n consume_ratio_last_90d VARCHAR(256) COMMENT '消费组近90天的生产消费比例(%)',\n backlog_ratio_last_90d VARCHAR(256) COMMENT '消费组近90天的生产积压比例(%)',\n backlog_days_last_30d INT COMMENT '消费组近30天有新增积压的天数',\n hitted_gov_items VARCHAR(256) COMMENT '命中治理项',\n governance_benefit_estimate DOUBLE COMMENT '预估治理收益（集群单元/月）'\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_008 ground_truth done: rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_008"} {"task_id": "mysql_009_en", "id": "offline-compute_MySQL_mysql_009", "name": "数据总线 Consumer Group Cost Detail Data Aggregation and Column Renaming", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "en", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\nI need you to generate a MySQL script that summarizes packet count, size, and cost from 数据总线 consumer group cost raw data, grouped by consumer group + business ID + topic, to produce a consumer group cost detail table.\n\n**Business Background and Objective**: 数据总线 periodically writes cost data for each consumer group into a raw detail table. Downstream consumers only need records from the `dt = '20260507'` partition, grouped by `business_id`, `topic`, and `consumer_group`, with MAX aggregation applied to all other fields before writing to the output table. The output field names must also match the downstream table conventions. This task aggregates qualifying records from the input table, performs column renaming, and writes to the output table.\n\n**Input Table (full name + brief description)**:\n- `internal_platform_db.ods_t_databus_consume_cost_final_date_d_mysql_009` (数据总线 consumer group cost raw data table)\n\n(Please connect to the database and query to confirm the table structure and field semantics.)\n\n**Filter and Aggregation Rules**:\n- Filter condition: `dt = '20260507'`\n- Aggregation logic: Group by `business_id`, `topic`, `consumer_group`\n- Apply MAX to `systemname`, `dwproductname`, `dwappgroup`, `cityid`, `iset`, `pkgcnt`, `tubesize`, `total_cost`, and `in_charge`\n\n**Column Renaming Rules**:\n- Input column `systemname` → output column `system_belong`\n- Input column `dwproductname` → output column `category_name`\n- Input column `dwappgroup` → output column `dw_appgroup`\n- Input column `cityid` → output column `city_id`\n- Input column `iset` → output column `cluster_set`\n- Input column `consumer_group` → output column `consumergroup`\n- Input column `pkgcnt` → output column `pkg_cnt`\n- Input column `tubesize` → output column `data_size`\n- `business_id`, `topic`, `total_cost`, `in_charge` retain their original names\n\n**Output Requirements**:\n- Target table: `internal_platform_db.dwd_databus_consumergroup_cost_detail_d_cand_mysql_009`\n- Table comment: 数据总线 consumer group cost detail table, performing data cleansing on the ODS table\n- Output field order: `dt`, `system_belong`, `category_name`, `dw_appgroup`, `city_id`, `cluster_set`, `consumergroup`, `business_id`, `topic`, `pkg_cnt`, `data_size`, `total_cost`, `in_charge`\n- Field types: `dt` VARCHAR(256), `system_belong` VARCHAR(256), `category_name` VARCHAR(256), `dw_appgroup` VARCHAR(256), `city_id` VARCHAR(256), `cluster_set` VARCHAR(256), `consumergroup` VARCHAR(256), `business_id` VARCHAR(256), `topic` VARCHAR(256), `pkg_cnt` BIGINT, `data_size` BIGINT, `total_cost` DOUBLE, `in_charge` VARCHAR(256)\n- If the target table does not exist, first create it using standard MySQL InnoDB format, then write the data\n- Use standard MySQL syntax; do not use Hive/Spark SQL dialects\n\n**Environment and Execution Notes**:\n- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`\n- The local MySQL is running at localhost:3306, username `root`, password `root123`\n- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)\n- The script must include complete table creation (if the target table does not exist) and data writing logic\n- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_009 ground truth: 数据总线消费组成本明细数据聚合与列重命名\n\nTask:\n Filter input table WHERE dt = '20260507',\n GROUP BY business_id, topic, consumer_group,\n MAX() for systemname, dwproductname, dwappgroup, cityid, iset, pkgcnt, tubesize, total_cost, in_charge,\n rename columns, write to output table.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"ods_t_databus_consume_cost_final_date_d_mysql_009\"\nOUTPUT_TABLE = \"dwd_databus_consumergroup_cost_detail_d_cand_mysql_009\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (dt, system_belong, category_name, dw_appgroup, city_id, cluster_set,\n consumergroup, business_id, topic, pkg_cnt, data_size, total_cost, in_charge)\nSELECT\n '20260507' AS dt,\n MAX(systemname) AS system_belong,\n MAX(dwproductname) AS category_name,\n MAX(dwappgroup) AS dw_appgroup,\n MAX(cityid) AS city_id,\n MAX(iset) AS cluster_set,\n consumer_group AS consumergroup,\n business_id,\n topic,\n MAX(CAST(pkgcnt AS UNSIGNED)) AS pkg_cnt,\n MAX(CAST(tubesize AS UNSIGNED)) AS data_size,\n MAX(CAST(total_cost AS DOUBLE)) AS total_cost,\n MAX(in_charge) AS in_charge\nFROM {DB_NAME}.{INPUT_TABLE}\nWHERE dt = '20260507'\nGROUP BY business_id, topic, consumer_group\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n dt VARCHAR(256) NOT NULL COMMENT '天分区',\n system_belong VARCHAR(256) NOT NULL COMMENT '系统名',\n category_name VARCHAR(256) NOT NULL COMMENT '数据仓库DW产品名称',\n dw_appgroup VARCHAR(256) NOT NULL COMMENT '数据仓库DW应用组',\n city_id VARCHAR(256) NOT NULL COMMENT '城市ID',\n cluster_set VARCHAR(256) NOT NULL COMMENT '集群',\n consumergroup VARCHAR(256) NOT NULL COMMENT '消费组',\n business_id VARCHAR(256) NOT NULL COMMENT '业务ID',\n topic VARCHAR(256) NOT NULL COMMENT 'topic',\n pkg_cnt BIGINT NOT NULL COMMENT '包数',\n data_size BIGINT NOT NULL COMMENT '大小',\n total_cost DOUBLE NOT NULL COMMENT '总成本',\n in_charge VARCHAR(256) NOT NULL COMMENT '负责人'\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='数据总线消费组成本明细表，ods表做数据清洗'\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_009 ground_truth done: 3 rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_009_en"} {"task_id": "mysql_010_en", "id": "offline-compute_MySQL_mysql_010", "name": "Table Heat Field Count and Query User Count Statistics with TOP Ranking", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "en", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\nI need you to generate a MySQL script that retrieves the current day's data from the heat table, counts the number of heat fields queried and the number of querying users per table, and lands the top 10000 rows ranked by heat in descending order.\n\n**Business Background and Objective**: Retrieve the current day's data from the heat table, count how many fields and how many users queried each table, and land the top 10000 rows ranked by heat in descending order into the output table.\n\n**Input Table (full name + brief description)**:\n- `internal_platform_db.sql_heat_column_to_ao_mysql_010` (table heat field summary table)\n\n(Please connect to the database and query to confirm the table structure and field semantics.)\n\n**Filter Condition**:\n- `imp_date = '20260507'`\n\n**Derived Field Rules**:\n- `column_count`: Take the JSON array length of the `column_list` field (i.e., the number of fields), which can be implemented using `JSON_LENGTH(column_list)`; equivalent method: remove brackets and quotes, then split by comma and take the length\n- `user_count`: Apply the same treatment to the `user_list` field, taking the JSON array length (i.e., the number of users)\n\n**Sorting and Limit**:\n- Sort by `heat DESC, column_count DESC, user_count DESC`\n- Take the first 10000 rows\n\n**Output Requirements**:\n- Target table: `internal_platform_db.sql_heat_column_to_ao_top_mysql_010`\n- Output field order: `imp_date` (VARCHAR), `databasename` (VARCHAR), `tablename` (VARCHAR), `column_list` (VARCHAR), `user_list` (VARCHAR), `heat` (VARCHAR), `column_count` (VARCHAR), `user_count` (VARCHAR)\n- No deduplication or aggregation\n- If the target table does not exist, first create it using standard MySQL InnoDB format, then write the data\n- Use standard MySQL syntax; do not use Hive/Spark SQL dialects\n\n**Environment and Execution Notes**:\n- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`\n- The local MySQL is running at localhost:3306, username `root`, password `root123`\n- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)\n- The script must include complete table creation (if the target table does not exist) and data writing logic\n- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_010 ground truth: 表热度字段数与查询用户数统计TOP排序\n\nTask:\n Filter input table WHERE imp_date = '20260507',\n derive column_count (length of JSON array in column_list) and\n user_count (length of JSON array in user_list),\n sort by heat DESC, column_count DESC, user_count DESC,\n limit 10000, write to output table.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"sql_heat_column_to_ao_mysql_010\"\nOUTPUT_TABLE = \"sql_heat_column_to_ao_top_mysql_010\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\n# MySQL equivalent of Spark's size(split(regexp_replace(regexp_replace(regexp_replace(col, '\\\\[|\\\\]', ''), '\"', ''), ' ', ''), ','))\n# In MySQL: strip brackets/quotes/spaces, then count comma-separated items.\n# Helper: JSON_LENGTH works for valid JSON arrays; fallback to comma-count.\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (imp_date, databasename, tablename, column_list, user_list, heat, column_count, user_count)\nSELECT\n imp_date,\n databasename,\n tablename,\n column_list,\n user_list,\n CAST(heat AS CHAR) AS heat,\n CAST(JSON_LENGTH(column_list) AS CHAR) AS column_count,\n CAST(JSON_LENGTH(user_list) AS CHAR) AS user_count\nFROM {DB_NAME}.{INPUT_TABLE}\nWHERE imp_date = '20260507'\nORDER BY heat DESC, JSON_LENGTH(column_list) DESC, JSON_LENGTH(user_list) DESC\nLIMIT 10000\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n imp_date VARCHAR(256) NOT NULL COMMENT '数据日期',\n databasename VARCHAR(256) NOT NULL COMMENT '库名',\n tablename VARCHAR(256) NOT NULL COMMENT '表名',\n column_list VARCHAR(256) NOT NULL COMMENT '热度字段名',\n user_list VARCHAR(256) NOT NULL COMMENT '用户名',\n heat VARCHAR(256) NOT NULL COMMENT '表热度',\n column_count VARCHAR(256) NOT NULL COMMENT '列字段数',\n user_count VARCHAR(256) NOT NULL COMMENT '用户数'\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n\n # Verify row count\n with conn.cursor() as cur:\n cur.execute(f\"SELECT COUNT(*) FROM {DB_NAME}.{OUTPUT_TABLE}\")\n count = cur.fetchone()[0]\n print(f\"mysql_010 ground_truth done: {count} rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_010_en"} {"task_id": "mysql_011_en", "id": "offline-compute_MySQL_mysql_011", "name": "Low-Value Task Benefit Aggregation Statistics by Application Group", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "en", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\nTask Objective: Summarize the current day's actual and estimated benefit metrics for low-value tasks, grouped by planning product, operations product, OBS product, and application group dimensions.\n\n**Business Background and Objective**: Low-value task governance requires summarizing the current day's actual benefits (task count, runtime, CPU, memory) and estimated benefits (task count, runtime, CPU, memory) at the application group level to evaluate governance effectiveness. This task groups the detail data from the specified partition of the input table by four dimension fields, applies SUM aggregation to all metric fields, and writes the results to the output table.\n\n**Input Table (full name + brief description)**:\n- `internal_platform_db.dws_low_value_task_compute_statistics_day_mysql_011` (low-value task compute benefit detail daily table)\n\n(Please connect to the database and query to confirm the table structure and field semantics.)\n\n**Processing Rules**:\n- No joins, single-table processing\n- Filter condition: `dt = '20260507'`\n- Aggregation logic: Group by `plan_product_name`, `obs_product_name`, `product_name`, `dw_appgroup`, applying SUM to all metric fields (`actural_task_count`, `actural_time_sum_hour`, `actural_vcore_sum_vcore_hour`, `actural_memory_sum_gb_hour`, `estimated_task_count`, `estimated_time_sum_hour`, `estimated_vcore_sum_vcore_hour`, `estimated_memory_sum_gb_hour`)\n\n**Output Requirements**:\n- Target table: `internal_platform_db.dws_low_value_task_compute_appgroup_stats_day_cand_mysql_011`\n- Output field order: `dt`, `plan_product_name`, `obs_product_name`, `product_name`, `dw_appgroup`, `actural_task_count`, `actural_time_sum_hour`, `actural_vcore_sum_vcore_hour`, `actural_memory_sum_gb_hour`, `estimated_task_count`, `estimated_time_sum_hour`, `estimated_vcore_sum_vcore_hour`, `estimated_memory_sum_gb_hour`\n- `dt` field value: `'20260507'`\n- Types: `dt` VARCHAR(8), `plan_product_name` VARCHAR(256), `obs_product_name` VARCHAR(256), `product_name` VARCHAR(256), `dw_appgroup` VARCHAR(256), `actural_task_count` INT, `actural_time_sum_hour` DOUBLE, `actural_vcore_sum_vcore_hour` DOUBLE, `actural_memory_sum_gb_hour` DOUBLE, `estimated_task_count` INT, `estimated_time_sum_hour` DOUBLE, `estimated_vcore_sum_vcore_hour` DOUBLE, `estimated_memory_sum_gb_hour` DOUBLE\n- If the target table does not exist, first create it using standard MySQL InnoDB format, then write the data\n- Use standard MySQL syntax; do not use Hive/Spark SQL dialects (e.g., `INSERT OVERWRITE`, `STORED AS ORC`, `PARTITIONED BY` are not supported)\n- Write method: Use `INSERT INTO ... SELECT ...` statements\n\n**Environment and Execution Notes**:\n- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`\n- The local MySQL is running at localhost:3306, username `root`, password `root123`\n- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)\n- The script must include complete table creation (if the target table does not exist) and data writing logic\n- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_011 ground truth: 低价值任务收益按应用组聚合统计\n\nTask:\n Filter input table WHERE dt = '20260507',\n GROUP BY plan_product_name, obs_product_name, product_name, dw_appgroup,\n SUM all metric fields, write to output table with dt = '20260507'.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"dws_low_value_task_compute_statistics_day_mysql_011\"\nOUTPUT_TABLE = \"dws_low_value_task_compute_appgroup_stats_day_cand_mysql_011\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (dt, plan_product_name, obs_product_name, product_name, dw_appgroup,\n actural_task_count, actural_time_sum_hour, actural_vcore_sum_vcore_hour,\n actural_memory_sum_gb_hour, estimated_task_count, estimated_time_sum_hour,\n estimated_vcore_sum_vcore_hour, estimated_memory_sum_gb_hour)\nSELECT\n '20260507' AS dt,\n plan_product_name,\n obs_product_name,\n product_name,\n dw_appgroup,\n SUM(actural_task_count) AS actural_task_count,\n SUM(actural_time_sum_hour) AS actural_time_sum_hour,\n SUM(actural_vcore_sum_vcore_hour) AS actural_vcore_sum_vcore_hour,\n SUM(actural_memory_sum_gb_hour) AS actural_memory_sum_gb_hour,\n SUM(estimated_task_count) AS estimated_task_count,\n SUM(estimated_time_sum_hour) AS estimated_time_sum_hour,\n SUM(estimated_vcore_sum_vcore_hour) AS estimated_vcore_sum_vcore_hour,\n SUM(estimated_memory_sum_gb_hour) AS estimated_memory_sum_gb_hour\nFROM {DB_NAME}.{INPUT_TABLE}\nWHERE dt = '20260507'\nGROUP BY plan_product_name, obs_product_name, product_name, dw_appgroup\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n dt VARCHAR(8) NOT NULL COMMENT '天级分区',\n plan_product_name VARCHAR(256) DEFAULT NULL COMMENT '规划产品名',\n obs_product_name VARCHAR(256) DEFAULT NULL COMMENT '运营产品名',\n product_name VARCHAR(256) DEFAULT NULL COMMENT 'OBS产品名',\n dw_appgroup VARCHAR(256) DEFAULT NULL COMMENT '应用组',\n actural_task_count INT DEFAULT NULL COMMENT '实际收益-任务数',\n actural_time_sum_hour DOUBLE DEFAULT NULL COMMENT '实际收益-运行时间(hour)',\n actural_vcore_sum_vcore_hour DOUBLE DEFAULT NULL COMMENT '实际收益-CPU(vcore*hour)',\n actural_memory_sum_gb_hour DOUBLE DEFAULT NULL COMMENT '实际收益-内存(gb*hour)',\n estimated_task_count INT DEFAULT NULL COMMENT '预估收益-任务数',\n estimated_time_sum_hour DOUBLE DEFAULT NULL COMMENT '预估收益-运行时间(hour)',\n estimated_vcore_sum_vcore_hour DOUBLE DEFAULT NULL COMMENT '预估收益-CPU(vcore*hour)',\n estimated_memory_sum_gb_hour DOUBLE DEFAULT NULL COMMENT '预估收益-内存(gb*hour)'\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_011 ground_truth done: 12 rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_011_en"} {"task_id": "mysql_012", "id": "offline-compute_MySQL_mysql_012", "name": "Notebook管道任务GPU利用率分钟级统计", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\n任务目标：按任务实例+分钟粒度统计 Notebook 管道任务的 GPU 利用率，区分实例整体利用率与代码运行时段利用率。\n\n输入：\n- 表：`internal_platform_db.dwd_notebook_instance_pod_detail_d_mysql_012`（Notebook 实例 Pod 明细表）\n\n（表结构与字段含义请自行连接数据库查询确认）\n\n- 过滤条件：`dt = '20260507'`\n\n处理规则：\n- 无 Join，单表处理\n- 分组：按 `trace_id`, `p_date`, `pkg_agg_time` 分组\n- 聚合口径：\n - `datawd_project_id` ~ `apply_for_gpu_count`：取 `MAX`\n - `code_gpu_count`：`SUM(IF(gpu_util IS NOT NULL, COALESCE(gpu_count, 0), gpu_count))`\n - `instance_gpu_util`：`AVG(CASE WHEN gpu_count IS NOT NULL THEN COALESCE(gpu_util, 0) ELSE NULL END)`（注意：当分组内所有行的 gpu_count 均为 NULL 时，CASE 表达式均返回 NULL，AVG 无有效行参与，结果为 NULL）\n - `code_gpu_util`：`AVG(CASE WHEN code_start_time IS NOT NULL AND code_end_time IS NOT NULL AND pkg_agg_time >= code_start_time AND pkg_agg_time <= code_end_time AND gpu_count IS NOT NULL THEN COALESCE(gpu_util, 0) ELSE NULL END)`\n - `is_code_running`：`MAX(CASE WHEN code_start_time IS NOT NULL AND code_end_time IS NOT NULL AND pkg_agg_time >= code_start_time AND pkg_agg_time <= code_end_time THEN 1 ELSE 0 END)`\n - `used_gpu_count`：`SUM(COALESCE(gpu_util, 0) * COALESCE(gpu_count, 0) / 100)`\n - `code_used_gpu_count`：`SUM(CASE WHEN code_start_time IS NOT NULL AND code_end_time IS NOT NULL AND pkg_agg_time >= code_start_time AND pkg_agg_time <= code_end_time THEN COALESCE(gpu_util, 0) * COALESCE(gpu_count, 0) / 100 ELSE 0 END)`\n\n输出要求：\n- 输出表：`internal_platform_db.dws_notebook_instance_execute_minute_stat_d_cand_mysql_012`\n- 表注释：Notebook 管道任务实例分钟级运行统计信息\n- 输出字段（按顺序）：`dt` VARCHAR(8)、`trace_id` VARCHAR(256)、`p_date` VARCHAR(256)、`pkg_agg_time` VARCHAR(256)、`datawd_project_id` VARCHAR(256)、`datawd_task_id` VARCHAR(256)、`datawd_task_instance_id` VARCHAR(256)、`compute_type` VARCHAR(256)、`status_code` INT、`instance_run_time` INT、`code_run_time` INT、`resource_wait_time` INT、`code_start_time` VARCHAR(256)、`code_end_time` VARCHAR(256)、`instance_start_time` VARCHAR(256)、`instance_end_time` VARCHAR(256)、`serving_id` VARCHAR(256)、`is_permanent` TINYINT(1)、`apply_for_gpu_count` INT、`code_gpu_count` INT、`instance_gpu_util` DOUBLE、`code_gpu_util` DOUBLE、`is_code_running` INT、`used_gpu_count` DOUBLE、`code_used_gpu_count` DOUBLE\n\n写入要求：\n- 使用 `INSERT INTO ... SELECT ...` 将结果写入输出表\n- 若目标表不存在，先按 MySQL InnoDB 标准建表（`ENGINE=InnoDB DEFAULT CHARSET=utf8mb4`），再写入\n- 请使用标准 MySQL 语法，不要使用 Hive/Spark SQL 方言（如不要使用 `INSERT OVERWRITE`、不要使用 `STORED AS ORC`、不要使用分区表语法）\n\n**环境与执行说明**：\n- 你的最终产出必须写入文件 `/tmp_workspace/result.py`，不能写 result.sql\n- 本机 MySQL 已在 localhost:3306 运行，用户名 root，密码 root123\n- 请使用 Python pymysql 在 result.py 中执行 SQL（不要用 mysql 命令行）\n- 脚本需要包含完整的建表（如目标表不存在）+ 写入数据的逻辑\n- 写出 result.py 后，你必须自己执行 `python3 /tmp_workspace/result.py` 验证它能成功运行并产出正确数据", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_012 ground truth: Notebook 管道任务实例分钟级 GPU 利用率统计\n\nTask:\n Filter input table WHERE dt = '20260507',\n group by trace_id, p_date, pkg_agg_time,\n aggregate MAX/SUM/AVG per the spec,\n write to output table.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"dwd_notebook_instance_pod_detail_d_mysql_012\"\nOUTPUT_TABLE = \"dws_notebook_instance_execute_minute_stat_d_cand_mysql_012\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (dt, trace_id, p_date, pkg_agg_time, datawd_project_id, datawd_task_id,\n datawd_task_instance_id, compute_type, status_code, instance_run_time,\n code_run_time, resource_wait_time, code_start_time, code_end_time,\n instance_start_time, instance_end_time, serving_id, is_permanent,\n apply_for_gpu_count, code_gpu_count, instance_gpu_util, code_gpu_util,\n is_code_running, used_gpu_count, code_used_gpu_count)\nSELECT\n '20260507' AS dt,\n trace_id,\n p_date,\n pkg_agg_time,\n MAX(datawd_project_id) AS datawd_project_id,\n MAX(datawd_task_id) AS datawd_task_id,\n MAX(datawd_task_instance_id) AS datawd_task_instance_id,\n MAX(compute_type) AS compute_type,\n MAX(status_code) AS status_code,\n MAX(instance_run_time) AS instance_run_time,\n MAX(code_run_time) AS code_run_time,\n MAX(resource_wait_time) AS resource_wait_time,\n MAX(code_start_time) AS code_start_time,\n MAX(code_end_time) AS code_end_time,\n MAX(instance_start_time) AS instance_start_time,\n MAX(instance_end_time) AS instance_end_time,\n MAX(serving_id) AS serving_id,\n MAX(is_permanent) AS is_permanent,\n MAX(apply_for_gpu_count) AS apply_for_gpu_count,\n SUM(IF(gpu_util IS NOT NULL, COALESCE(gpu_count, 0), gpu_count)) AS code_gpu_count,\n AVG(CASE WHEN gpu_count IS NOT NULL THEN COALESCE(gpu_util, 0) ELSE NULL END) AS instance_gpu_util,\n AVG(\n CASE\n WHEN code_start_time IS NOT NULL AND code_end_time IS NOT NULL\n AND pkg_agg_time >= code_start_time AND pkg_agg_time <= code_end_time\n AND gpu_count IS NOT NULL\n THEN COALESCE(gpu_util, 0)\n ELSE NULL\n END\n ) AS code_gpu_util,\n MAX(\n CASE\n WHEN code_start_time IS NOT NULL\n AND code_end_time IS NOT NULL\n AND pkg_agg_time >= code_start_time\n AND pkg_agg_time <= code_end_time\n THEN 1 ELSE 0\n END\n ) AS is_code_running,\n SUM(COALESCE(gpu_util, 0) * COALESCE(gpu_count, 0) / 100) AS used_gpu_count,\n SUM(\n CASE\n WHEN code_start_time IS NOT NULL AND code_end_time IS NOT NULL\n AND pkg_agg_time >= code_start_time AND pkg_agg_time <= code_end_time\n THEN COALESCE(gpu_util, 0) * COALESCE(gpu_count, 0) / 100\n ELSE 0\n END\n ) AS code_used_gpu_count\nFROM {DB_NAME}.{INPUT_TABLE}\nWHERE dt = '20260507'\nGROUP BY\n trace_id,\n p_date,\n pkg_agg_time\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n dt VARCHAR(8) COMMENT '天分区',\n trace_id VARCHAR(256) COMMENT '',\n p_date VARCHAR(256) COMMENT '',\n pkg_agg_time VARCHAR(256) COMMENT '分钟级时间戳',\n datawd_project_id VARCHAR(256) COMMENT '',\n datawd_task_id VARCHAR(256) COMMENT '',\n datawd_task_instance_id VARCHAR(256) COMMENT '',\n compute_type VARCHAR(256) COMMENT '',\n status_code INT COMMENT '',\n instance_run_time INT COMMENT '',\n code_run_time INT COMMENT '',\n resource_wait_time INT COMMENT '',\n code_start_time VARCHAR(256) COMMENT '',\n code_end_time VARCHAR(256) COMMENT '',\n instance_start_time VARCHAR(256) COMMENT '',\n instance_end_time VARCHAR(256) COMMENT '',\n serving_id VARCHAR(256) COMMENT '',\n is_permanent TINYINT(1) COMMENT '布尔型',\n apply_for_gpu_count INT COMMENT '',\n code_gpu_count INT COMMENT '',\n instance_gpu_util DOUBLE COMMENT '',\n code_gpu_util DOUBLE COMMENT '',\n is_code_running INT COMMENT '',\n used_gpu_count DOUBLE COMMENT '',\n code_used_gpu_count DOUBLE COMMENT ''\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci\n COMMENT='Notebook 管道任务实例分钟级运行统计信息'\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_012 ground_truth done: rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_012"} {"task_id": "mysql_013_en", "id": "offline-compute_MySQL_mysql_013", "name": "Notebook Cross-Day Instance Pod Runtime Detail", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "en", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\nI need you to generate a MySQL script that splits Notebook runner cross-day instances by day, joins with service instance, Pod, GPU aggregation, and engine information, to produce cross-day instance Pod runtime detail records.\n\n**Business Background and Objective**: Instances in the Notebook runner may run across multiple days. The detail records need to be split by day and joined with service instance mapping, Pod names, GPU aggregation metrics, and engine configuration information, ultimately producing detail records at the instance × Pod × day granularity.\n\n**Input Tables (full name + brief description)**:\n- `internal_platform_db.notebook_span_info_mysql_013` (Notebook span information table)\n- `internal_platform_db.dwd_gputj_service_instance_map_mysql_013` (service instance mapping table)\n- `internal_platform_db.dwd_ml_platform_instance_podname_mysql_013` (instance Pod name table)\n- `internal_platform_db.gputj_gpu_info_parsed_agg_1min_mysql_013` (GPU aggregation metrics table)\n- `internal_platform_db.notebook_engine_info_mysql_013` (engine configuration information table)\n\n(Please connect to the database and query to confirm the table structures and field semantics.)\n\n**Processing Rules**:\n1. Identify cross-day instances from `notebook_span_info` (`compute_type='ray'` AND `service_name='notebook-runner'` AND `span_name='runner.execute'`), split them by day, and generate detail records for each instance per day;\n2. Obtain `serving_id` via `notebook_engine_info`, then join with `dwd_gputj_service_instance_map` on `service_id` (i.e., `serving_id`) to obtain `instance_uuid`, and subsequently retrieve service instance mapping information;\n3. Join with `dwd_ml_platform_instance_podname` on `instance_uuid` to obtain Pod name information;\n4. Join with `gputj_gpu_info_parsed_agg_1min` on Pod name and time granularity to obtain GPU aggregation metrics;\n5. Join with `notebook_engine_info` on `trace_id` to obtain engine configuration information (`serving_id`, `is_permanent`, `apply_for_gpu_count`, etc.).\n\n**Output Requirements**:\n- Output granularity: instance × Pod × day;\n- Output fields: `trace_id`, `datawd_project_id`, `datawd_task_id`, `datawd_task_instance_id`, `compute_type`, `status_code`, `instance_run_time`, `code_run_time`, `resource_wait_time`, `code_start_time`, `code_end_time`, `instance_start_time`, `instance_end_time`, `serving_id`, `is_permanent`, `apply_for_gpu_count`, `pod_name`, `pkg_agg_time`, `gpu_util`, `gpu_count`, `p_date`, `dt`\n- Sort by instance ID and date\n\n**Write Requirements**:\n- Target table: `internal_platform_db.dwd_notebook_instance_pod_cross_day_detail_d_cand_mysql_013`\n- Write mode: `INSERT INTO ... SELECT`\n- If the target table does not exist, first create it using standard MySQL InnoDB format, then write the data\n- Use standard MySQL syntax; do not use Hive/Spark SQL dialects\n\n**Environment and Execution Notes**:\n- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`\n- The local MySQL is running at localhost:3306, username `root`, password `root123`\n- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)\n- The script must include complete table creation (if the target table does not exist) and data writing logic\n- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_013 ground truth: Notebook跨天实例Pod运行明细\n\nTask:\n Split Notebook runner cross-day instances by day, join with service instance,\n Pod, GPU aggregation and engine info, produce cross-day instance Pod detail.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE_1 = \"notebook_span_info_mysql_013\"\nINPUT_TABLE_2 = \"dwd_gputj_service_instance_map_mysql_013\"\nINPUT_TABLE_3 = \"dwd_ml_platform_instance_podname_mysql_013\"\nINPUT_TABLE_4 = \"gputj_gpu_info_parsed_agg_1min_mysql_013\"\nINPUT_TABLE_5 = \"notebook_engine_info_mysql_013\"\nOUTPUT_TABLE = \"dwd_notebook_instance_pod_cross_day_detail_d_cand_mysql_013\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (trace_id, datawd_project_id, datawd_task_id, datawd_task_instance_id,\n compute_type, status_code, instance_run_time, code_run_time, resource_wait_time,\n code_start_time, code_end_time, instance_start_time, instance_end_time,\n serving_id, is_permanent, apply_for_gpu_count,\n pod_name, pkg_agg_time, gpu_util, gpu_count, p_date, dt)\nWITH\nbase_data_raw AS (\n SELECT DISTINCT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM {DB_NAME}.{INPUT_TABLE_1}\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND trace_id IN (\n SELECT trace_id\n FROM {DB_NAME}.{INPUT_TABLE_1}\n WHERE databus_imp_date >= '2026050700'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n AND span_name = 'runner.execute'\n GROUP BY trace_id\n )\n AND (\n span_name = 'runner.execute'\n OR\n span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'set.permanent.compute', 'create.non.permanent.compute', 'runner.killed')\n )\n),\nbase_data_time_fixed AS (\n SELECT\n trace_id,\n span_name,\n CASE\n WHEN span_name = 'runner.killed'\n THEN MIN(CASE WHEN span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'runner.killed') THEN start_time END)\n OVER(PARTITION BY trace_id)\n ELSE start_time\n END AS start_time,\n CASE\n WHEN span_name = 'runner.execute'\n THEN MAX(CASE WHEN span_name IN ('runner.execute', 'runner.killed') THEN end_time END)\n OVER(PARTITION BY trace_id)\n ELSE end_time\n END AS end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM base_data_raw\n),\nbase_data AS (\n SELECT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n DATE(FROM_UNIXTIME(CAST(start_time AS UNSIGNED) / 1000)) AS start_date,\n DATE(FROM_UNIXTIME(CAST(end_time AS UNSIGNED) / 1000)) AS end_date,\n DATEDIFF(DATE(FROM_UNIXTIME(CAST(end_time AS UNSIGNED) / 1000)), DATE(FROM_UNIXTIME(CAST(start_time AS UNSIGNED) / 1000))) AS diff_days\n FROM base_data_time_fixed\n),\npos_series AS (\n SELECT 0 AS pos UNION ALL SELECT 1 AS pos UNION ALL SELECT 2 AS pos UNION ALL SELECT 3 AS pos\n),\ndaily_split_spans AS (\n SELECT\n trace_id,\n span_name,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n start_date,\n end_date,\n diff_days,\n DATE_ADD(b.start_date, INTERVAL s.pos DAY) AS calc_date,\n CASE WHEN s.pos = 0 THEN b.start_time\n ELSE CAST(UNIX_TIMESTAMP(CAST(DATE_ADD(b.start_date, INTERVAL s.pos DAY) AS DATETIME)) * 1000 AS CHAR)\n END AS start_time,\n CASE WHEN s.pos = b.diff_days THEN b.end_time\n ELSE CAST((UNIX_TIMESTAMP(CAST(DATE_ADD(b.start_date, INTERVAL s.pos + 1 DAY) AS DATETIME)) * 1000) - 1 AS CHAR)\n END AS end_time,\n b.start_time AS span_start_time,\n b.end_time AS span_end_time\n FROM base_data b\n INNER JOIN pos_series s ON s.pos <= b.diff_days\n),\ntrace_time_metrics AS (\n SELECT\n trace_id,\n calc_date,\n MAX(datawd_project_id) AS datawd_project_id,\n MAX(datawd_task_id) AS datawd_task_id,\n MAX(datawd_task_instance_id) AS datawd_task_instance_id,\n MAX(compute_type) AS compute_type,\n MAX(MAX(CASE WHEN span_name = 'runner.execute' THEN status_code END)) OVER(PARTITION BY trace_id) AS status_code,\n CAST(ROUND((MAX(CASE WHEN span_name = 'runner.execute' THEN CAST(end_time AS UNSIGNED) END) -\n MIN(CASE WHEN span_name = 'runner.execute' THEN CAST(start_time AS UNSIGNED) END)) / 1000.0) AS SIGNED) AS instance_run_time,\n CAST(ROUND((MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(end_time AS UNSIGNED) END) -\n MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(start_time AS UNSIGNED) END)) / 1000.0) AS SIGNED) AS code_run_time,\n CAST(ROUND((MAX(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN CAST(end_time AS UNSIGNED) END) -\n MIN(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN CAST(start_time AS UNSIGNED) END)) / 1000.0) AS SIGNED) AS resource_wait_time,\n FROM_UNIXTIME(MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(start_time AS UNSIGNED) END) / 1000) AS code_start_time,\n FROM_UNIXTIME(MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(end_time AS UNSIGNED) END) / 1000) AS code_end_time,\n FROM_UNIXTIME(MIN(CASE WHEN span_name = 'runner.execute' THEN CAST(span_start_time AS UNSIGNED) END) / 1000) AS instance_start_time,\n FROM_UNIXTIME(MAX(CASE WHEN span_name = 'runner.execute' THEN CAST(span_end_time AS UNSIGNED) END) / 1000) AS instance_end_time\n FROM daily_split_spans\n GROUP BY trace_id, calc_date\n),\ngputj_dim AS (\n SELECT\n t1.service_id,\n t2.pod_name\n FROM {DB_NAME}.{INPUT_TABLE_2} t1\n INNER JOIN {DB_NAME}.{INPUT_TABLE_3} t2\n ON t1.instance_uuid = t2.instance_uuid\n AND t1.dt = IF('2026050700' >= '2025080900', '2026050700', '2025080900')\n AND t2.dt = IF('2026050700' >= '2025060519', '2026050700', '2025060519')\n GROUP BY t1.service_id, t2.pod_name\n),\ngpu_metrics AS (\n SELECT\n pkg_agg_time,\n pod_name,\n k8s_container_vgpu_gpu_util_sum / k8s_container_vgpu_gpu_util_count AS gpu_util,\n k8s_container_resource_request_gpu_sum / k8s_container_resource_request_gpu_count AS gpu_count\n FROM {DB_NAME}.{INPUT_TABLE_4}\n WHERE dt >= '2026050400'\n AND dt <= '2026050700'\n AND ( k8s_container_vgpu_gpu_util_count > 0 OR k8s_container_resource_request_gpu_count > 0 )\n AND pod_name IS NOT NULL\n)\nSELECT\n t1.trace_id,\n t1.datawd_project_id,\n t1.datawd_task_id,\n t1.datawd_task_instance_id,\n t1.compute_type,\n t1.status_code,\n t1.instance_run_time,\n t1.code_run_time,\n t1.resource_wait_time,\n t1.code_start_time,\n t1.code_end_time,\n t1.instance_start_time,\n t1.instance_end_time,\n t2.serving_id,\n t2.is_permanent,\n CAST(t2.apply_for_gpu_count AS SIGNED) AS apply_for_gpu_count,\n t3.pod_name,\n t4.pkg_agg_time,\n t4.gpu_util,\n t4.gpu_count,\n DATE(t1.calc_date) AS p_date,\n '20260507' AS dt\nFROM trace_time_metrics t1 LEFT JOIN (\n SELECT\n trace_id,\n MAX(serving_id) AS serving_id,\n MAX(CASE WHEN is_permanent = 'true' THEN 1 WHEN is_permanent = 'false' THEN 0 ELSE is_permanent END) AS is_permanent,\n SUM(CAST(replicas AS SIGNED) * CAST(num_gpu AS SIGNED)) AS apply_for_gpu_count\n FROM {DB_NAME}.{INPUT_TABLE_5}\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n GROUP BY trace_id\n) t2 ON t1.trace_id = t2.trace_id\nLEFT JOIN gputj_dim t3 ON CAST(t2.serving_id AS UNSIGNED) = t3.service_id\nLEFT JOIN gpu_metrics t4\n ON t3.pod_name = t4.pod_name\n AND (\n CASE\n WHEN t1.instance_start_time IS NOT NULL AND t1.instance_end_time IS NOT NULL\n THEN t4.pkg_agg_time >= t1.instance_start_time AND t4.pkg_agg_time <= t1.instance_end_time\n WHEN t1.code_start_time IS NOT NULL AND t1.code_end_time IS NOT NULL\n THEN t4.pkg_agg_time >= t1.code_start_time AND t4.pkg_agg_time <= t1.code_end_time\n ELSE FALSE\n END\n )\nORDER BY t1.trace_id, t1.calc_date\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Set timezone to Asia/Shanghai so FROM_UNIXTIME aligns with expected dates\n cur.execute(\"SET time_zone = '+08:00'\")\n\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n trace_id VARCHAR(256) NOT NULL COMMENT '追踪ID',\n datawd_project_id VARCHAR(256) DEFAULT NULL COMMENT '项目ID',\n datawd_task_id VARCHAR(256) DEFAULT NULL COMMENT '任务ID',\n datawd_task_instance_id VARCHAR(256) DEFAULT NULL COMMENT '任务实例ID',\n compute_type VARCHAR(256) DEFAULT NULL COMMENT '计算类型',\n status_code INT DEFAULT NULL COMMENT '状态码',\n instance_run_time INT DEFAULT NULL COMMENT '实例运行时长(秒)',\n code_run_time INT DEFAULT NULL COMMENT '代码运行时长(秒)',\n resource_wait_time INT DEFAULT NULL COMMENT '资源等待时长(秒)',\n code_start_time VARCHAR(256) DEFAULT NULL COMMENT '代码开始时间',\n code_end_time VARCHAR(256) DEFAULT NULL COMMENT '代码结束时间',\n instance_start_time VARCHAR(256) DEFAULT NULL COMMENT '实例开始时间',\n instance_end_time VARCHAR(256) DEFAULT NULL COMMENT '实例结束时间',\n serving_id VARCHAR(256) DEFAULT NULL COMMENT '服务ID',\n is_permanent TINYINT(1) DEFAULT NULL COMMENT '是否永久(0/1)',\n apply_for_gpu_count INT DEFAULT NULL COMMENT '申请GPU数量',\n pod_name VARCHAR(256) DEFAULT NULL COMMENT 'Pod名称',\n pkg_agg_time VARCHAR(256) DEFAULT NULL COMMENT 'GPU聚合时间',\n gpu_util DOUBLE DEFAULT NULL COMMENT 'GPU利用率',\n gpu_count DOUBLE DEFAULT NULL COMMENT 'GPU数量',\n p_date VARCHAR(256) DEFAULT NULL COMMENT '日期',\n dt VARCHAR(256) NOT NULL COMMENT '分区日期'\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_013 ground_truth done: rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_013_en"} {"task_id": "mysql_014", "id": "offline-compute_MySQL_mysql_014", "name": "Notebook执行实例明细按天拆分与引擎关联", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\n我需要你生成一段 MySQL 代码，从 Notebook runner trace 数据中筛选出存在 execute.code 等代码执行 span 的 trace，按天拆分跨天运行记录，计算实例/代码/资源等待时长等指标，关联引擎信息，产出每日执行实例明细表。\n\n**业务背景与目标**：Notebook runner 在运行过程中会产生多个 trace span（如 runner.execute、execute.code、set.permanent.compute 等）。本任务需要筛选出包含代码执行 span 的 trace，对跨天运行的记录按天拆分，计算各 span 类别的运行时长，并左关联引擎信息（serving_id、is_permanent、GPU 申请数量），最终写入每日执行实例明细表。\n\n**输入表（全名 + 简要描述）**：\n- `internal_platform_db.notebook_span_info_mysql_014`（Notebook trace span 数据）\n- `internal_platform_db.notebook_engine_info_mysql_014`（引擎信息）\n\n（各表结构与字段含义请自行连接数据库查询确认）\n\n**处理规则**：\n1. 过滤 notebook_span_info：databus_imp_date 在 ['2026050400', '2026050700'] 范围内，且 trace_id 满足子查询条件（compute_type='ray'、service_name='notebook-runner'、span_name='runner.execute'），保留 span_name 为 'runner.execute' 或在 ['execute.code', 'execute.code.cell', 'client.execute.code', 'set.permanent.compute', 'create.non.permanent.compute', 'runner.killed'] 中的记录\n2. 时间修正：对 runner.killed span，其 start_time 取同 trace 内相关 span 的最小 start_time；对 runner.execute span，其 end_time 取同 trace 内相关 span 的最大 end_time\n3. 按天拆分：计算 span 跨天天数（DATEDIFF），使用 pos_series（0-3）展开，每条记录按天生成多行，start_time/end_time 按当天边界截断\n4. 时长计算（按 trace_id + calc_date 聚合）：\n - instance_run_time：runner.execute span 的 (end_time - start_time) / 1000 秒\n - code_run_time：execute.code 等代码执行 span 的 (end_time - start_time) / 1000 秒\n - resource_wait_time：set.permanent.compute / create.non.permanent.compute span 的 (end_time - start_time) / 1000 秒\n - status_code：runner.execute 的 status_code 取 max\n5. 左关联 notebook_engine_info：按 trace_id 关联，聚合获取 serving_id(MAX)、is_permanent(MAX)、apply_for_gpu_count(SUM(replicas * num_gpu))\n\n**输出要求**：\n- 目标表：`internal_platform_db.dwd_notebook_execute_instance_detail_d_mysql_014`\n- 输出字段顺序：dt, p_date, trace_id, datawd_project_id, datawd_task_id, datawd_task_instance_id, compute_type, status_code, instance_run_time, code_run_time, resource_wait_time, code_start_time, code_end_time, instance_start_time, instance_end_time, serving_id, is_permanent, apply_for_gpu_count\n- dt 为分区值 '20260507'，p_date 为拆分后的日期\n- 时长字段取整（ROUND），时间字段转为字符串格式（YYYY-MM-DD HH:MM:SS）\n- 如果目标表不存在，请先按 MySQL InnoDB 标准建表，再写入数据\n- 请使用标准 MySQL 语法，不要使用 Hive/Spark SQL 方言\n\n**环境与执行说明**：\n- 你的最终产出必须写入文件 `/tmp_workspace/result.py`，不能写 result.sql\n- 本机 MySQL 已在 localhost:3306 运行，用户名 root，密码 root123\n- 请使用 Python pymysql 在 result.py 中执行 SQL（不要用 mysql 命令行）\n- 脚本需要包含完整的建表（如目标表不存在）+ 写入数据的逻辑\n- 写出 result.py 后，你必须自己执行 `python3 /tmp_workspace/result.py` 验证它能成功运行并产出正确数据", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_014 ground truth: Notebook执行实例明细按天拆分与引擎关联\n\nTask:\n Filter notebook_span_info for traces with execute.code spans,\n split cross-day records, calculate time metrics, left join engine info,\n write to output table.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE_1 = \"notebook_span_info_mysql_014\"\nINPUT_TABLE_2 = \"notebook_engine_info_mysql_014\"\nOUTPUT_TABLE = \"dwd_notebook_execute_instance_detail_d_mysql_014\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (dt, p_date, trace_id, datawd_project_id, datawd_task_id, datawd_task_instance_id,\n compute_type, status_code, instance_run_time, code_run_time, resource_wait_time,\n code_start_time, code_end_time, instance_start_time, instance_end_time,\n serving_id, is_permanent, apply_for_gpu_count)\nWITH\nbase_data_raw AS (\n SELECT DISTINCT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM {DB_NAME}.{INPUT_TABLE_1}\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND trace_id IN (\n SELECT trace_id\n FROM {DB_NAME}.{INPUT_TABLE_1}\n WHERE databus_imp_date >= '2026050700'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n AND span_name = 'runner.execute'\n GROUP BY trace_id\n )\n AND (\n span_name = 'runner.execute'\n OR\n span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'set.permanent.compute', 'create.non.permanent.compute', 'runner.killed')\n )\n),\nbase_data_time_fixed AS (\n SELECT\n trace_id,\n span_name,\n CASE\n WHEN span_name = 'runner.killed'\n THEN MIN(CASE WHEN span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'runner.killed') THEN start_time END)\n OVER(PARTITION BY trace_id)\n ELSE start_time\n END AS start_time,\n CASE\n WHEN span_name = 'runner.execute'\n THEN MAX(CASE WHEN span_name IN ('runner.execute', 'runner.killed') THEN end_time END)\n OVER(PARTITION BY trace_id)\n ELSE end_time\n END AS end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM base_data_raw\n),\nbase_data AS (\n SELECT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n DATE(FROM_UNIXTIME(CAST(start_time AS SIGNED) / 1000)) as start_date,\n DATE(FROM_UNIXTIME(CAST(end_time AS SIGNED) / 1000)) as end_date,\n DATEDIFF(DATE(FROM_UNIXTIME(CAST(end_time AS SIGNED) / 1000)), DATE(FROM_UNIXTIME(CAST(start_time AS SIGNED) / 1000))) AS diff_days\n FROM base_data_time_fixed\n),\npos_series AS (\n SELECT 0 AS pos UNION ALL SELECT 1 AS pos UNION ALL SELECT 2 AS pos UNION ALL SELECT 3 AS pos\n),\ndaily_split_spans AS (\n SELECT\n trace_id,\n span_name,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n start_date,\n end_date,\n diff_days,\n DATE_ADD(b.start_date, INTERVAL s.pos DAY) AS calc_date,\n CASE WHEN s.pos = 0 THEN CAST(b.start_time AS SIGNED)\n ELSE UNIX_TIMESTAMP(CAST(DATE_ADD(b.start_date, INTERVAL s.pos DAY) AS DATETIME)) * 1000\n END AS start_time,\n CASE WHEN s.pos = b.diff_days THEN CAST(b.end_time AS SIGNED)\n ELSE (UNIX_TIMESTAMP(CAST(DATE_ADD(b.start_date, INTERVAL s.pos + 1 DAY) AS DATETIME)) * 1000) - 1\n END AS end_time,\n CAST(b.start_time AS SIGNED) AS span_start_time,\n CAST(b.end_time AS SIGNED) AS span_end_time\n FROM base_data b\n INNER JOIN pos_series s ON s.pos <= b.diff_days\n),\ntrace_time_metrics AS (\n SELECT\n trace_id,\n calc_date,\n MAX(datawd_project_id) AS datawd_project_id,\n MAX(datawd_task_id) AS datawd_task_id,\n MAX(datawd_task_instance_id) AS datawd_task_instance_id,\n MAX(compute_type) AS compute_type,\n MAX(MAX(CASE WHEN span_name = 'runner.execute' THEN status_code END)) OVER(PARTITION BY trace_id) AS status_code,\n ROUND((MAX(CASE WHEN span_name = 'runner.execute' THEN end_time END) -\n MIN(CASE WHEN span_name = 'runner.execute' THEN start_time END)) / 1000.0) AS instance_run_time,\n ROUND((MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed') THEN end_time END) -\n MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed') THEN start_time END)) / 1000.0) AS code_run_time,\n ROUND((MAX(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN end_time END) -\n MIN(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN start_time END)) / 1000.0) AS resource_wait_time,\n FROM_UNIXTIME(MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed') THEN start_time END) / 1000) AS code_start_time,\n FROM_UNIXTIME(MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed') THEN end_time END) / 1000) AS code_end_time,\n FROM_UNIXTIME(MIN(CASE WHEN span_name = 'runner.execute' THEN span_start_time END) / 1000) AS instance_start_time,\n FROM_UNIXTIME(MAX(CASE WHEN span_name = 'runner.execute' THEN span_end_time END) / 1000) AS instance_end_time\n FROM daily_split_spans\n GROUP BY trace_id, calc_date\n)\nSELECT\n '20260507' AS dt,\n DATE_FORMAT(t1.calc_date, '%Y-%m-%d') AS p_date,\n t1.trace_id,\n t1.datawd_project_id,\n t1.datawd_task_id,\n t1.datawd_task_instance_id,\n t1.compute_type,\n t1.status_code,\n t1.instance_run_time,\n t1.code_run_time,\n t1.resource_wait_time,\n DATE_FORMAT(t1.code_start_time, '%Y-%m-%d %H:%i:%s') AS code_start_time,\n DATE_FORMAT(t1.code_end_time, '%Y-%m-%d %H:%i:%s') AS code_end_time,\n DATE_FORMAT(t1.instance_start_time, '%Y-%m-%d %H:%i:%s') AS instance_start_time,\n DATE_FORMAT(t1.instance_end_time, '%Y-%m-%d %H:%i:%s') AS instance_end_time,\n t2.serving_id,\n t2.is_permanent,\n t2.apply_for_gpu_count\nFROM trace_time_metrics t1\nLEFT JOIN (\n SELECT\n trace_id,\n MAX(serving_id) AS serving_id,\n MAX(is_permanent) AS is_permanent,\n SUM(CAST(replicas AS SIGNED) * CAST(num_gpu AS SIGNED)) AS apply_for_gpu_count\n FROM {DB_NAME}.{INPUT_TABLE_2}\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n GROUP BY trace_id\n ) t2 ON t1.trace_id = t2.trace_id\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Set timezone to Asia/Shanghai so FROM_UNIXTIME aligns with expected dates\n cur.execute(\"SET time_zone = '+08:00'\")\n\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n `dt` VARCHAR(256) COMMENT '天分区',\n `p_date` VARCHAR(256) COMMENT '拆分后日期',\n `trace_id` VARCHAR(256) COMMENT 'trace_id',\n `datawd_project_id` VARCHAR(256) COMMENT '项目ID',\n `datawd_task_id` VARCHAR(256) COMMENT '任务ID',\n `datawd_task_instance_id` VARCHAR(256) COMMENT '任务实例ID',\n `compute_type` VARCHAR(256) COMMENT '计算类型',\n `status_code` INT COMMENT '状态码',\n `instance_run_time` INT COMMENT '实例运行时长(秒)',\n `code_run_time` INT COMMENT '代码执行时长(秒)',\n `resource_wait_time` INT COMMENT '资源等待时长(秒)',\n `code_start_time` VARCHAR(256) COMMENT '代码开始时间',\n `code_end_time` VARCHAR(256) COMMENT '代码结束时间',\n `instance_start_time` VARCHAR(256) COMMENT '实例开始时间',\n `instance_end_time` VARCHAR(256) COMMENT '实例结束时间',\n `serving_id` VARCHAR(256) COMMENT 'serving_id',\n `is_permanent` VARCHAR(256) COMMENT '是否永久引擎',\n `apply_for_gpu_count` INT COMMENT 'GPU申请数量'\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n\n # Verify row count\n with conn.cursor() as cur:\n cur.execute(f\"SELECT COUNT(*) FROM {DB_NAME}.{OUTPUT_TABLE}\")\n count = cur.fetchone()[0]\n\n print(f\"mysql_014 ground_truth done: {count} rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_014"} {"task_id": "mysql_015_en", "id": "offline-compute_MySQL_mysql_015", "name": "Exposure Log First Exposure Time Computation", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "en", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\nI need you to generate a MySQL script that computes the first exposure time for each advertisement from the exposure log table.\n\n**Business Background and Objective**: The advertising system periodically writes exposure logs into a summary table. Downstream consumers need to compute the first exposure time per ad ID dimension and output the corresponding minute-level timestamp string. This task filters records from the input table that meet the specified partition and conditions, groups by ad ID to obtain the minimum exposure time, formats it as a minute-level string, and writes the results to the output table.\n\n**Input Table (full name + brief description)**:\n- `internal_platform_db.etl_pageview_exposure_mysql_015` (exposure log detail table)\n\n(Please connect to the database and query to confirm the table structure and field semantics.)\n\n**Processing Rules**:\n- Single-table processing, no joins\n- Filter condition: `partition_time = 2026060914` AND `ad_data_model_version = 3` AND `ad_optimization_goal > 0`\n- Group by `ad_aid`, compute `MIN(action_imp_time)` as the first exposure time\n- Format the first exposure time (millisecond timestamp divided by 1000 to convert to seconds) as a `yyyyMMddHHmm` string\n\n**Column Mapping Rules**:\n- Input column `ad_aid` → output column `aid`\n- Aggregation result `MIN(action_imp_time)` → output column `first_time`\n- Formatted string `FROM_UNIXTIME(MIN(action_imp_time)/1000, '%Y%m%d%H%i')` → output column `first_fen`\n- Fixed value `2026060914` → output column `dt`\n\n**Output Requirements**:\n- Target table: `internal_platform_db.first_expo_time_cand_mysql_015`\n- Output field order: `aid`, `first_time`, `first_fen`, `dt`\n- If the target table does not exist, first create it using standard MySQL InnoDB format, then write the data\n- Use standard MySQL syntax; do not use Hive/Spark SQL dialects\n\n**Environment and Execution Notes**:\n- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`\n- The local MySQL is running at localhost:3306, username `root`, password `root123`\n- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)\n- The script must include complete table creation (if the target table does not exist) and data writing logic\n- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_015 ground truth: 曝光日志首次曝光时间计算\n\nTask:\n Filter input table WHERE partition_time = 2026060914\n AND ad_data_model_version = 3 AND ad_optimization_goal > 0,\n group by ad_aid, compute MIN(action_imp_time) as first_time,\n format as FROM_UNIXTIME(MIN/1000, '%Y%m%d%H%i') as first_fen,\n write to output table with dt = 2026060914.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"etl_pageview_exposure_mysql_015\"\nOUTPUT_TABLE = \"first_expo_time_cand_mysql_015\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (aid, first_time, first_fen, dt)\nSELECT\n ad_aid AS aid,\n MIN(action_imp_time) AS first_time,\n FROM_UNIXTIME(MIN(action_imp_time) / 1000, '%Y%m%d%H%i') AS first_fen,\n 2026060914 AS dt\nFROM {DB_NAME}.{INPUT_TABLE}\nWHERE partition_time = 2026060914\n AND ad_data_model_version = 3\n AND ad_optimization_goal > 0\nGROUP BY ad_aid\nORDER BY ad_aid\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure consistent timezone (Hive cluster was UTC+8)\n cur.execute(\"SET SESSION time_zone = '+08:00'\")\n\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n aid BIGINT NOT NULL,\n first_time BIGINT NOT NULL,\n first_fen VARCHAR(32) NOT NULL,\n dt BIGINT NOT NULL,\n PRIMARY KEY (aid, dt)\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_015 ground_truth done: rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_015_en"} {"task_id": "mysql_016", "id": "offline-compute_MySQL_mysql_016", "name": "应用X模型与组织关系小时表全量迁移", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\n我需要你生成一段 MySQL 代码，将源表数据全量迁移至目标表，保持数据完整性。\n\n**业务背景与目标**：应用 X 模型与组织关系小时表记录了资产明细与组织架构的关联关系。本任务将输入表中的全量数据迁移至输出表，不做任何过滤、聚合或字段转换。\n\n**输入表（全名 + 简要描述）**：\n- `internal_platform_db.t_app_xmodel_and_org_relation_hour_src_mysql_016`（应用X模型与组织关系小时表）\n\n（表结构与字段含义请自行连接数据库查询确认）\n\n**处理规则**：\n- 无过滤条件，全量读取\n- 无 Join，单表处理\n- 无聚合、无去重\n- 保留所有字段，不做字段派生或转换\n\n**输出要求**：\n- 目标表：`internal_platform_db.t_app_xmodel_and_org_relation_hour_cand_mysql_016`\n- 输出字段与源表完全一致\n- 输出记录数与源表一致\n- 如果目标表不存在，请先按 MySQL InnoDB 标准建表，再写入数据\n- 请使用标准 MySQL 语法，不要使用 Hive/Spark SQL 方言\n\n**环境与执行说明**：\n- 你的最终产出必须写入文件 `/tmp_workspace/result.py`，不能写 result.sql\n- 本机 MySQL 已在 localhost:3306 运行，用户名 root，密码 root123\n- 请使用 Python pymysql 在 result.py 中执行 SQL（不要用 mysql 命令行）\n- 脚本需要包含完整的建表（如目标表不存在）+ 写入数据的逻辑\n- 写出 result.py 后，你必须自己执行 `python3 /tmp_workspace/result.py` 验证它能成功运行并产出正确数据", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_016 ground truth: 应用X模型与组织关系小时表全量迁移\n\nTask:\n Full migration from source table to target table,\n no filtering, no transformation, all columns preserved.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"t_app_xmodel_and_org_relation_hour_src_mysql_016\"\nOUTPUT_TABLE = \"t_app_xmodel_and_org_relation_hour_cand_mysql_016\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (xsoa_id, principal, xsoa_org_principal, xsoa_team_name, xsoa_team_id,\n xsoa_center_name, xsoa_center_id, xsoa_dept_name, xsoa_dept_id,\n xsoa_principal_index, xsoa_dimension, ds)\nSELECT\n xsoa_id,\n principal,\n xsoa_org_principal,\n xsoa_team_name,\n xsoa_team_id,\n xsoa_center_name,\n xsoa_center_id,\n xsoa_dept_name,\n xsoa_dept_id,\n xsoa_principal_index,\n xsoa_dimension,\n ds\nFROM {DB_NAME}.{INPUT_TABLE}\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n xsoa_id VARCHAR(256) NOT NULL,\n principal VARCHAR(256) NOT NULL,\n xsoa_org_principal VARCHAR(256) NOT NULL,\n xsoa_team_name VARCHAR(256) NOT NULL,\n xsoa_team_id VARCHAR(256) NOT NULL,\n xsoa_center_name VARCHAR(256) NOT NULL,\n xsoa_center_id VARCHAR(256) NOT NULL,\n xsoa_dept_name VARCHAR(256) NOT NULL,\n xsoa_dept_id VARCHAR(256) NOT NULL,\n xsoa_principal_index BIGINT NOT NULL,\n xsoa_dimension VARCHAR(256) NOT NULL,\n ds BIGINT NOT NULL,\n PRIMARY KEY (xsoa_id)\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_016 ground_truth done: 8 rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_016"} {"task_id": "mysql_017", "id": "offline-compute_MySQL_mysql_017", "name": "新闻曝光数据全量迁移", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\n任务目标：将输入表数据同步至输出表，实现表间数据迁移。\n\n输入：\n- `internal_platform_db.dwd_news_dt_others_imp_hi_mysql_017`\n\n处理规则：\n- 无 Join，单表处理\n- 无过滤条件，保留全部记录\n- 无聚合操作\n- 字段全量映射，保留源表所有字段\n\n输出要求：\n- 输出表：`internal_platform_db.dwd_news_dt_others_imp_si_cand_mysql_017`\n- 输出字段顺序与输入表一致\n- 不去重，保留所有记录\n\n写入要求：\n- 写入模式：覆盖写入（先清空目标表再写入）\n- 请使用标准 MySQL 语法，不要使用 Hive/Spark SQL 方言\n\n**环境与执行说明**：\n- 你的最终产出必须写入文件 `/tmp_workspace/result.py`，不能写 result.sql\n- 本机 MySQL 已在 localhost:3306 运行，用户名 root，密码 root123\n- 请使用 Python pymysql 在 result.py 中执行 SQL（不要用 mysql 命令行）\n- 脚本需要包含完整的建表（如目标表不存在）+ 写入数据的逻辑\n- 写出 result.py 后，你必须自己执行 `python3 /tmp_workspace/result.py` 验证它能成功运行并产出正确数据\n- 如果目标表不存在，请先按 MySQL InnoDB 标准建表，再写入数据", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_017 ground truth: 新闻曝光数据全量迁移\n\nTask:\n Copy all rows from input table to output table (full field mapping, no filter).\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"dwd_news_dt_others_imp_hi_mysql_017\"\nOUTPUT_TABLE = \"dwd_news_dt_others_imp_si_cand_mysql_017\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\nALL_COLUMNS = [\n \"iceberg_imp_date\", \"watermark_ts\", \"ftime\", \"imp_hour\", \"time_range\",\n \"platform_user_id\", \"device_id\", \"social_uid\", \"os\", \"network_type\", \"app_version\", \"callfrom\",\n \"page_start_from\", \"start_article_id\", \"start_article_type\", \"article_uuid\",\n \"article_page\", \"article_type\", \"article_pos\", \"article_real_pos\",\n \"rcmd_reason\", \"is_tagversion\", \"event_time\", \"client_ts\", \"server_time\",\n \"social_openid\", \"suid\", \"session_id\", \"session_ts\", \"brand\", \"manufacturer\",\n \"model\", \"login_type\", \"country\", \"province\", \"pgid\", \"ref_pgid\", \"media_id\",\n \"chl_id\", \"video_id\", \"article_id\", \"article_ptype\", \"article_cmt_id\",\n \"is_hotnews\", \"is_cpfans\", \"is_xiaoshipin\", \"is_coldstart\", \"is_minivideo\",\n \"imp_date\", \"is_landingpage\", \"article_imp_pv\", \"video_imp_pv\", \"imgtext_imp_pv\",\n \"eid\", \"tag_id\", \"pg_tag_id\", \"pg_tag_type\", \"pg_article_type\", \"pg_article_id\",\n \"is_major_upgrade\", \"idfv\", \"android_id\", \"tab_id\", \"context_type\",\n \"element_path\", \"p1_article_ptype\", \"refpg_article_type\", \"tag_type\",\n \"pg_tab_id\", \"brand_type\", \"fulltext_imp_pv\", \"scheme_type\", \"module\",\n \"cmt_replyid\", \"comment_imp_pv\", \"bubble_msg_type\", \"untitled\",\n \"hot_rank_imp_pv\", \"pg_tag_scene\", \"header_type\", \"window_open_from\",\n \"user_more_id\", \"section_id\", \"search_cell_type\", \"pg_source2\", \"bar_name\",\n \"scheme_url\", \"member_btn_id\", \"vert_cell_scheme_url\", \"article_title\",\n \"pg_article_title\", \"bigevent_type\", \"schedule_type\", \"pg_path\", \"ussn\",\n \"top_banner_type\", \"pendant_type\", \"banner_url\", \"sort_menu_id\", \"video_pid\",\n \"error_tips\", \"gameid\", \"ad_atype\", \"ad_action\", \"article_module_pos\",\n \"refpg_chl_id\", \"refpg_last_clck_ele\", \"live_article_id\", \"nav_item_id\",\n \"nav_item_name\", \"have_redpoint\", \"nav_pos\", \"undetermined\", \"pg_tab2_from\",\n \"scheme_scene_type\", \"is_reservable\", \"is_reserve\", \"pg_subtab_id\",\n \"pg_article_live_status\", \"pg_article_relate_event_type\", \"pg_search_keyword\",\n \"vert_cell_title\", \"mod_article_type\", \"pub_btn_type\", \"pg_hotask_type\",\n \"mod_article_ptype\", \"question_id\", \"answer_id\", \"is_answerer\",\n \"article_review_status\", \"banner_module_id\", \"article_live_status\",\n \"article_pay_status\", \"pg_article_pay_status\", \"tag_scene\", \"pg_detail_type\",\n \"column_type\", \"pg_column_type\", \"is_column_purchased\", \"pg_is_column_purchased\",\n \"pay_product_id\", \"huaci_type\", \"panel_btn_id\", \"is_user_self\", \"crepg_chl_id\",\n \"crepg_article_id\", \"crepg_article_type\", \"dt_cre_pgid\", \"crepg_last_clck_ele\",\n \"dialog_type\", \"pg_subtab_name\", \"e_pos\", \"sug_word\", \"e_from\", \"e_type\",\n \"article_list_pos\", \"pg_article_bool_parad_platform\", \"city_level\", \"has_authority\",\n \"pg_is_audio\", \"etl_pgid\", \"search_keyword\", \"cardpanel_type\", \"mod_article_page\",\n \"mod_alg_info\", \"mod_article_real_pos\", \"article_id_list\", \"e_state\", \"user_suid\",\n \"pg_login_from\", \"pg_last_login_type\", \"is_ad\", \"hometown_adcode\",\n \"user_service_id\", \"user_cpcenter_id\", \"agent_id\", \"e_title\", \"tab_setid\",\n \"pg_page_start_from\", \"is_flash_keyword\", \"search_query_from\",\n \"is_query_from_cache\", \"p1_search_cell_type\", \"pg_search_query_from\",\n \"refpg_search_query_from\",\n]\n\ncol_list = \", \".join(ALL_COLUMNS)\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE} ({col_list})\nSELECT {col_list}\nFROM {DB_NAME}.{INPUT_TABLE}\n\"\"\"\n\n# Output table DDL (same schema as input)\nOUTPUT_DDL = f\"\"\"\nCREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n `_id` BIGINT AUTO_INCREMENT PRIMARY KEY COMMENT '自增代理主键',\n `iceberg_imp_date` TEXT NOT NULL COMMENT '分区时间',\n `watermark_ts` BIGINT NOT NULL COMMENT 'watermark时间毫秒',\n `ftime` TEXT NOT NULL COMMENT '数据上报时间',\n `imp_hour` TEXT NOT NULL COMMENT '时间分区',\n `time_range` TEXT NOT NULL COMMENT '时段分类',\n `platform_user_id` VARCHAR(256) NOT NULL COMMENT '用户platform_user_id',\n `device_id` TEXT NOT NULL COMMENT '用户device_id',\n `social_uid` TEXT NOT NULL COMMENT 'social user id',\n `os` TEXT NOT NULL COMMENT '操作系统',\n `network_type` TEXT NOT NULL COMMENT '网络类型',\n `app_version` TEXT NOT NULL COMMENT 'app版本号',\n `callfrom` TEXT NOT NULL COMMENT '启动方式',\n `page_start_from` TEXT NOT NULL COMMENT '落地页拉起来源',\n `start_article_id` TEXT NOT NULL COMMENT '外部拉起时的文章id',\n `start_article_type` TEXT NOT NULL COMMENT '外部拉起时的文章类型',\n `article_uuid` TEXT NOT NULL COMMENT '文章唯一uuid',\n `article_page` BIGINT NOT NULL COMMENT '刷次',\n `article_type` TEXT NOT NULL COMMENT '文章类型',\n `article_pos` BIGINT NOT NULL COMMENT '文章位置',\n `article_real_pos` BIGINT NOT NULL COMMENT '文章真实位置',\n `rcmd_reason` TEXT NOT NULL COMMENT '推荐理由',\n `is_tagversion` TEXT NOT NULL COMMENT '是否tag新版本',\n `event_time` TEXT NOT NULL COMMENT '事件时间',\n `client_ts` TEXT NOT NULL COMMENT '事件时间戳',\n `server_time` TEXT NOT NULL COMMENT '上报时间',\n `social_openid` TEXT NOT NULL COMMENT '平台openid',\n `suid` TEXT NOT NULL COMMENT '用户登陆ID',\n `session_id` TEXT NOT NULL COMMENT '会话ID',\n `session_ts` TEXT NOT NULL COMMENT '会话时间戳',\n `brand` TEXT NOT NULL COMMENT '设备品牌',\n `manufacturer` TEXT NOT NULL COMMENT '设备制造商',\n `model` TEXT NOT NULL COMMENT '设备型号',\n `login_type` TEXT NOT NULL COMMENT '登陆类型',\n `country` TEXT NOT NULL COMMENT '国家',\n `province` TEXT NOT NULL COMMENT '省份',\n `pgid` TEXT NOT NULL COMMENT '页面类型',\n `ref_pgid` TEXT NOT NULL COMMENT '上一级页面类型',\n `media_id` TEXT NOT NULL COMMENT '用户媒体ID',\n `chl_id` TEXT NOT NULL COMMENT '频道ID',\n `video_id` TEXT NOT NULL COMMENT '视频ID',\n `article_id` TEXT NOT NULL COMMENT '文章ID',\n `article_ptype` TEXT NOT NULL COMMENT '展示样式类型',\n `article_cmt_id` TEXT NOT NULL COMMENT '文章评论ID',\n `is_hotnews` TEXT NOT NULL COMMENT '是否热点文章',\n `is_cpfans` TEXT NOT NULL COMMENT '是否关注状态',\n `is_xiaoshipin` TEXT NOT NULL COMMENT '是否小视频（接入层）',\n `is_coldstart` TEXT NOT NULL COMMENT '是否冷启动',\n `is_minivideo` TEXT NOT NULL COMMENT '是否小视频（内平）',\n `imp_date` TEXT NOT NULL COMMENT '日期',\n `is_landingpage` TEXT NOT NULL COMMENT '是否落地页',\n `article_imp_pv` BIGINT NOT NULL COMMENT '文章曝光pv',\n `video_imp_pv` BIGINT NOT NULL COMMENT '视频文章曝光pv',\n `imgtext_imp_pv` BIGINT NOT NULL COMMENT '图文文章曝光pv',\n `eid` TEXT NOT NULL COMMENT '元素id',\n `tag_id` TEXT NOT NULL COMMENT 'tag_id',\n `pg_tag_id` TEXT NOT NULL COMMENT '页面的tag_id',\n `pg_tag_type` TEXT NOT NULL COMMENT 'tag类型',\n `pg_article_type` TEXT NOT NULL COMMENT '页面文章类型',\n `pg_article_id` TEXT NOT NULL COMMENT '页面文章id',\n `is_major_upgrade` TEXT NOT NULL COMMENT 'NoComment',\n `idfv` TEXT NOT NULL COMMENT 'idfv',\n `android_id` TEXT NOT NULL COMMENT 'android_id',\n `tab_id` TEXT NOT NULL COMMENT '底部导航Tab Id',\n `context_type` TEXT NOT NULL COMMENT 'NoComment',\n `element_path` TEXT NOT NULL COMMENT '元素路径',\n `p1_article_ptype` TEXT NOT NULL COMMENT '父级文章展示类型',\n `refpg_article_type` TEXT NOT NULL COMMENT '上一级页面的文章类型',\n `tag_type` TEXT NOT NULL COMMENT 'tag类型',\n `pg_tab_id` TEXT NOT NULL COMMENT '来源页面底部导航Tab Id',\n `brand_type` TEXT NOT NULL COMMENT 'brand_type',\n `fulltext_imp_pv` BIGINT NOT NULL COMMENT '展开全文按钮真实曝光',\n `scheme_type` TEXT NOT NULL COMMENT 'scheme类型',\n `module` TEXT NOT NULL COMMENT '模块',\n `cmt_replyid` TEXT NOT NULL COMMENT '评论id',\n `comment_imp_pv` BIGINT NOT NULL COMMENT '评论曝光pv',\n `bubble_msg_type` TEXT NOT NULL COMMENT '气泡类型',\n `untitled` TEXT NOT NULL COMMENT '待更名',\n `hot_rank_imp_pv` BIGINT NOT NULL COMMENT '热榜入口曝光',\n `pg_tag_scene` TEXT NOT NULL COMMENT '页面tag场景类型',\n `header_type` TEXT NOT NULL COMMENT '头部类型',\n `window_open_from` TEXT NOT NULL COMMENT '弹窗来源',\n `user_more_id` TEXT NOT NULL COMMENT 'user_more_id',\n `section_id` TEXT NOT NULL COMMENT 'section_id',\n `search_cell_type` TEXT NOT NULL COMMENT '搜索页模块种类',\n `pg_source2` TEXT NOT NULL COMMENT '长视频流量来源',\n `bar_name` TEXT NOT NULL COMMENT '提示条名称',\n `scheme_url` TEXT NOT NULL COMMENT 'scheme_url',\n `member_btn_id` TEXT NOT NULL COMMENT '元素id',\n `vert_cell_scheme_url` TEXT NOT NULL COMMENT 'vert元素的url',\n `article_title` TEXT NOT NULL COMMENT '元素名称标识',\n `pg_article_title` TEXT NOT NULL COMMENT '页面或者文章标题',\n `bigevent_type` TEXT NOT NULL COMMENT '大事件类型',\n `schedule_type` TEXT NOT NULL COMMENT '赛程类型',\n `pg_path` TEXT NOT NULL COMMENT '页面路径',\n `ussn` TEXT NOT NULL COMMENT '新版session_id',\n `top_banner_type` TEXT NOT NULL COMMENT '顶部banner类型',\n `pendant_type` TEXT NOT NULL COMMENT '挂件类型',\n `banner_url` TEXT NOT NULL COMMENT 'banner跳转的URL',\n `sort_menu_id` TEXT NOT NULL COMMENT '分类菜单id',\n `video_pid` TEXT NOT NULL COMMENT 'video_pid',\n `error_tips` TEXT NOT NULL COMMENT '敏感词拦截提示文案',\n `gameid` TEXT NOT NULL COMMENT '游戏ID',\n `ad_atype` TEXT NOT NULL COMMENT '广告类型',\n `ad_action` TEXT NOT NULL COMMENT '广告动作类型',\n `article_module_pos` TEXT NOT NULL COMMENT '模块位置',\n `refpg_chl_id` TEXT NOT NULL COMMENT '来源页面chl_id',\n `refpg_last_clck_ele` TEXT NOT NULL COMMENT '上一级页面业务私参',\n `live_article_id` TEXT NOT NULL COMMENT '直播文章id',\n `nav_item_id` TEXT NOT NULL COMMENT '导航id',\n `nav_item_name` TEXT NOT NULL COMMENT '导航名称',\n `have_redpoint` TEXT NOT NULL COMMENT '是否带红点',\n `nav_pos` TEXT NOT NULL COMMENT '频道位置',\n `undetermined` TEXT NOT NULL COMMENT '废弃字段',\n `pg_tab2_from` TEXT NOT NULL COMMENT 'tab2来源',\n `scheme_scene_type` TEXT NOT NULL COMMENT '短带长类型',\n `is_reservable` TEXT NOT NULL COMMENT '是否带预约按钮',\n `is_reserve` TEXT NOT NULL COMMENT '是否预约状态',\n `pg_subtab_id` TEXT NOT NULL COMMENT '底层页子tab的id',\n `pg_article_live_status` TEXT NOT NULL COMMENT '直播状态',\n `pg_article_relate_event_type` TEXT NOT NULL COMMENT '关联事件类型',\n `pg_search_keyword` TEXT NOT NULL COMMENT '搜索词',\n `vert_cell_title` TEXT NOT NULL COMMENT 'cell 标题',\n `mod_article_type` TEXT NOT NULL COMMENT '模块文章类型',\n `pub_btn_type` TEXT NOT NULL COMMENT '发布按钮类型',\n `pg_hotask_type` TEXT NOT NULL COMMENT '热问页面类型',\n `mod_article_ptype` TEXT NOT NULL COMMENT '模块文章展示样式类型',\n `question_id` TEXT NOT NULL COMMENT '问题 id',\n `answer_id` TEXT NOT NULL COMMENT '外显回答 id',\n `is_answerer` TEXT NOT NULL COMMENT '是否答主',\n `article_review_status` TEXT NOT NULL COMMENT '回答状态',\n `banner_module_id` TEXT NOT NULL COMMENT 'banner模块 id',\n `article_live_status` TEXT NOT NULL COMMENT '文章直播状态',\n `article_pay_status` TEXT NOT NULL COMMENT '文章付费状态',\n `pg_article_pay_status` TEXT NOT NULL COMMENT '文章付费状态（页面参数）',\n `tag_scene` TEXT NOT NULL COMMENT 'tag场景类型',\n `pg_detail_type` TEXT NOT NULL COMMENT '底层页类型',\n `column_type` TEXT NOT NULL COMMENT '专栏类型',\n `pg_column_type` TEXT NOT NULL COMMENT '页面专栏类型',\n `is_column_purchased` TEXT NOT NULL COMMENT '是否购买专栏',\n `pg_is_column_purchased` TEXT NOT NULL COMMENT '页面是否购买专栏',\n `pay_product_id` TEXT NOT NULL COMMENT '支付时所选的产品ID',\n `huaci_type` TEXT NOT NULL COMMENT '划词类型',\n `panel_btn_id` TEXT NOT NULL COMMENT '面板按钮ID',\n `is_user_self` TEXT NOT NULL COMMENT '是否用户本人',\n `crepg_chl_id` TEXT NOT NULL COMMENT '跳转起始页面频道ID',\n `crepg_article_id` TEXT NOT NULL COMMENT '跳转起始页面文章ID',\n `crepg_article_type` TEXT NOT NULL COMMENT '跳转起始页面文章类型',\n `dt_cre_pgid` TEXT NOT NULL COMMENT '创造页页面ID',\n `crepg_last_clck_ele` TEXT NOT NULL COMMENT '创造页上一次点击元素信息',\n `dialog_type` TEXT NOT NULL COMMENT '对话类型',\n `pg_subtab_name` TEXT NOT NULL COMMENT '子 tab名称',\n `e_pos` TEXT NOT NULL COMMENT '元素位置',\n `sug_word` TEXT NOT NULL COMMENT '建议词条',\n `e_from` TEXT NOT NULL COMMENT '元素来源',\n `e_type` TEXT NOT NULL COMMENT '元素类型',\n `article_list_pos` TEXT NOT NULL COMMENT '绝对位置',\n `pg_article_bool_parad_platform` TEXT NOT NULL COMMENT '文章携带的布尔参数',\n `city_level` TEXT NOT NULL COMMENT '城市等级',\n `has_authority` TEXT NOT NULL COMMENT '是否同意隐私协议',\n `pg_is_audio` TEXT NOT NULL COMMENT '是否是音频',\n `etl_pgid` TEXT NOT NULL COMMENT 'ETL页面id',\n `search_keyword` TEXT NOT NULL COMMENT '搜索关键词',\n `cardpanel_type` TEXT NOT NULL COMMENT '卡片面板类型',\n `mod_article_page` TEXT NOT NULL COMMENT '模块文章所在刷次',\n `mod_alg_info` TEXT NOT NULL COMMENT '模块文章推荐信息',\n `mod_article_real_pos` TEXT NOT NULL COMMENT '模块文章在刷次中的位置',\n `article_id_list` TEXT NOT NULL COMMENT '文章id集合',\n `e_state` TEXT NOT NULL COMMENT '元素状态',\n `user_suid` TEXT NOT NULL COMMENT '用户suid',\n `pg_login_from` TEXT NOT NULL COMMENT '登录页来源',\n `pg_last_login_type` TEXT NOT NULL COMMENT '上次登录类型',\n `is_ad` TEXT NOT NULL COMMENT '是否广告',\n `hometown_adcode` TEXT NOT NULL COMMENT '家乡code',\n `user_service_id` TEXT NOT NULL COMMENT '用户service id',\n `user_cpcenter_id` TEXT NOT NULL COMMENT '用户中心id',\n `agent_id` TEXT NOT NULL COMMENT '智能体ID',\n `e_title` TEXT NOT NULL COMMENT '元素文本',\n `tab_setid` TEXT NOT NULL COMMENT '发布按钮setid',\n `pg_page_start_from` TEXT NOT NULL COMMENT '落地页拉起来源（页面参数）',\n `is_flash_keyword` TEXT NOT NULL COMMENT '轮播词query是否无价值曝光',\n `search_query_from` TEXT NOT NULL COMMENT '搜索发起来源',\n `is_query_from_cache` TEXT NOT NULL COMMENT 'query是否缓存',\n `p1_search_cell_type` TEXT NOT NULL COMMENT '文章所在模块的cell类型',\n `pg_search_query_from` TEXT NOT NULL COMMENT '页面搜索发起来源',\n `refpg_search_query_from` TEXT NOT NULL COMMENT '来源页面发起搜索的方式',\n KEY `idx_platform_user_id` (`platform_user_id`)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(OUTPUT_DDL)\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_017 ground_truth done: all rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_017"} {"task_id": "mysql_018_en", "id": "offline-compute_MySQL_mysql_018", "name": "URL Safety Detection Multi-Dimensional Access Statistics Report", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "en", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\nI need you to generate a MySQL script that computes multi-dimensional access statistics from the URL safety detection detail table, producing a URL access statistics report.\n\n**Business Background and Objective**: The URL safety detection system collects URL access records for each `taid` daily. It needs to compute UV and its 1d/7d/30d/1m periodic metrics across multiple dimensions (taid, taid+url, taid+domain/site, taid+cgi), and output a single-row summary result to the report table.\n\n**Input Tables (full name + brief description)**:\n- `internal_platform_db.t_dws_urlsafe_rela_mobile_browser_taid_url_di_mysql_018` (URL safety detection detail table)\n- Historical partition data: Historical records in the output table itself where `ds = 20260531, channel = 'mobile_browser'` (left join source, used to obtain 1m metrics)\n\n(Please connect to the database and query to confirm the table structures and field semantics.)\n\n**Filter Condition**:\n- Input table: `ds > 20260509 AND ds <= 20260608`\n\n**Processing Rules**:\n### Subquery a (taid-level UV statistics)\n- Group by `taid`, compute:\n - `uv_1d = COUNT(IF(ds_max = 20260608, 1, NULL))`\n - `uv_avg_7d = (COUNT(state_6d=1) + ... + COUNT(state_daily=1)) / 7`, where `state_*d = MAX(IF(ds = corresponding_date, 1, 0))`\n - `uv_7d = COUNT(IF(ds_max > 20260601, 1, NULL))`\n - `uv_30d = COUNT(1)`\n\n### Subquery c (taid+url-level statistics)\n- Group by `taid`, `url` and aggregate, computing `userid_url_1d`/`avg_7d`/`7d`/`30d` using the same logic as above\n\n### Subquery d (taid+domain/site-level statistics)\n- First group by `taid`, `site` and aggregate `domain`, `ds_max`, `state_*d`\n - Domain value rule: Under the same taid+site, if multiple domains exist, take `MAX(domain)` (lexicographically largest value) as the domain attribution for that site\n- Then group by `taid`, `domain` and aggregate, computing:\n - `userid_domain_1d`/`avg_7d`/`7d`/`30d`\n - `userid_site_1d = SUM(site_cnt_daily)`\n - `userid_site_avg_7d = (SUM(site_cnt_6d) + ... + SUM(site_cnt_daily)) / 7`\n - `userid_site_7d = SUM(site_cnt_7d)`\n - `userid_site_30d = SUM(site_cnt)`\n\n### Subquery e (taid+cgi-level statistics)\n- Group by `taid`, `cgi` and aggregate, computing `userid_cgi_1d`/`avg_7d`/`7d`/`30d` using the same logic as above\n\n### Join Logic\n- `a LEFT JOIN b` (historical partition, joined on `data_par`): `IFNULL(b.uv_1m, 0)` and 4 other fields\n- `a JOIN c, d, e` (all inner joins, joined on `data_par`)\n\n### Final Output Fields (in order)\n- `access_type = '网址检测API'`\n- `access_channel = '手机IM平台Q浏览器'`\n- `uv_1d`, `uv_avg_7d` (`ROUND(x, 0)`), `uv_7d`, `uv_30d`, `uv_1m`\n- `userid_url_1d`, `userid_url_avg_7d` (ROUND), `userid_url_7d`, `userid_url_30d`, `userid_url_1m`\n- `userid_domain_1d`, `userid_domain_avg_7d` (ROUND), `userid_domain_7d`, `userid_domain_30d`, `userid_domain_1m`\n- `userid_site_1d`, `userid_site_avg_7d` (ROUND), `userid_site_7d`, `userid_site_30d`, `userid_site_1m`\n- `userid_cgi_1d`, `userid_cgi_avg_7d` (ROUND), `userid_cgi_7d`, `userid_cgi_30d`, `userid_cgi_1m`\n\n**Output Requirements**:\n- Target table: `internal_platform_db.t_app_urlsafe_report_url_access_stat_di_cand_mysql_018`\n- Single-row result, 27 fields\n- All avg fields retain 0 decimal places (using ROUND)\n- 1m fields use IFNULL to handle NULL as 0\n- If the target table does not exist, first create it using standard MySQL InnoDB format, then write the data\n- Use standard MySQL syntax; do not use Hive/Spark SQL dialects\n\n**Environment and Execution Notes**:\n- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`\n- The local MySQL is running at localhost:3306, username `root`, password `root123`\n- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)\n- The script must include complete table creation (if the target table does not exist) and data writing logic\n- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_018 ground truth: 网址安全检测多维度访问统计报表\n\nTask:\n From URL safety detection detail table, compute multi-dimensional\n access statistics (UV metrics at taid/url/domain/site/cgi levels),\n join with historical partition data, and write single-row result\n to output table.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE = \"t_dws_urlsafe_rela_mobile_browser_taid_url_di_mysql_018\"\nOUTPUT_TABLE = \"t_app_urlsafe_report_url_access_stat_di_mysql_018\"\nCAND_TABLE = \"t_app_urlsafe_report_url_access_stat_di_cand_mysql_018\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\n# MySQL-adapted GT SQL:\n# - INSERT OVERWRITE -> INSERT INTO ... SELECT\n# - NVL -> IFNULL\n# - to_char(date_sub(...)) -> hardcoded 20260531\n# - PARTITION columns become regular columns\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{CAND_TABLE}\n (access_type, access_channel,\n uv_1d, uv_avg_7d, uv_7d, uv_30d, uv_1m,\n userid_url_1d, userid_url_avg_7d, userid_url_7d, userid_url_30d, userid_url_1m,\n userid_domain_1d, userid_domain_avg_7d, userid_domain_7d, userid_domain_30d, userid_domain_1m,\n userid_site_1d, userid_site_avg_7d, userid_site_7d, userid_site_30d, userid_site_1m,\n userid_cgi_1d, userid_cgi_avg_7d, userid_cgi_7d, userid_cgi_30d, userid_cgi_1m,\n ds, channel)\nSELECT\n '网址检测API' AS access_type,\n '手机IM平台Q浏览器' AS access_channel,\n a.uv_1d,\n ROUND(a.uv_avg_7d, 0) AS uv_avg_7d,\n a.uv_7d,\n a.uv_30d,\n IFNULL(b.uv_1m, 0) AS uv_1m,\n c.userid_url_1d,\n ROUND(c.userid_url_avg_7d, 0) AS userid_url_avg_7d,\n c.userid_url_7d,\n c.userid_url_30d,\n IFNULL(b.userid_url_1m, 0) AS userid_url_1m,\n d.userid_domain_1d,\n ROUND(d.userid_domain_avg_7d, 0) AS userid_domain_avg_7d,\n d.userid_domain_7d,\n d.userid_domain_30d,\n IFNULL(b.userid_domain_1m, 0) AS userid_domain_1m,\n d.userid_site_1d,\n ROUND(d.userid_site_avg_7d, 0) AS userid_site_avg_7d,\n d.userid_site_7d,\n d.userid_site_30d,\n IFNULL(b.userid_site_1m, 0) AS userid_site_1m,\n e.userid_cgi_1d,\n ROUND(e.userid_cgi_avg_7d, 0) AS userid_cgi_avg_7d,\n e.userid_cgi_7d,\n e.userid_cgi_30d,\n IFNULL(b.userid_cgi_1m, 0) AS userid_cgi_1m,\n 20260608 AS ds,\n 'mobile_browser' AS channel\nFROM\n(\n SELECT 20260608 AS data_par,\n COUNT(IF(ds_max = 20260608, 1, NULL)) AS uv_1d,\n (COUNT(IF(state_6d=1,1,NULL))+COUNT(IF(state_5d=1,1,NULL))+COUNT(IF(state_4d=1,1,NULL))+COUNT(IF(state_3d=1,1,NULL))+COUNT(IF(state_2d=1,1,NULL))+COUNT(IF(state_1d=1,1,NULL))+COUNT(IF(state_daily=1,1,NULL)))/7 AS uv_avg_7d,\n COUNT(IF(ds_max > 20260601, 1, NULL)) AS uv_7d,\n COUNT(1) AS uv_30d\n FROM\n (\n SELECT taid,\n MAX(ds) AS ds_max,\n MAX(IF(ds = 20260602,1,0)) AS state_6d,\n MAX(IF(ds = 20260603,1,0)) AS state_5d,\n MAX(IF(ds = 20260604,1,0)) AS state_4d,\n MAX(IF(ds = 20260605,1,0)) AS state_3d,\n MAX(IF(ds = 20260606,1,0)) AS state_2d,\n MAX(IF(ds = 20260607,1,0)) AS state_1d,\n MAX(IF(ds = 20260608,1,0)) AS state_daily\n FROM {DB_NAME}.{INPUT_TABLE}\n WHERE ds > 20260509 AND ds <= 20260608\n GROUP BY taid\n ) t\n) a\nLEFT JOIN\n(\n SELECT 20260608 AS data_par,\n uv_30d AS uv_1m,\n userid_url_30d AS userid_url_1m,\n userid_domain_30d AS userid_domain_1m,\n userid_site_30d AS userid_site_1m,\n userid_cgi_30d AS userid_cgi_1m\n FROM {DB_NAME}.{OUTPUT_TABLE}\n WHERE ds = 20260531\n AND channel = 'mobile_browser'\n) b ON a.data_par = b.data_par\nJOIN\n(\n SELECT 20260608 AS data_par,\n COUNT(IF(ds_max = 20260608, 1, NULL)) AS userid_url_1d,\n (COUNT(IF(state_6d=1,1,NULL))+COUNT(IF(state_5d=1,1,NULL))+COUNT(IF(state_4d=1,1,NULL))+COUNT(IF(state_3d=1,1,NULL))+COUNT(IF(state_2d=1,1,NULL))+COUNT(IF(state_1d=1,1,NULL))+COUNT(IF(state_daily=1,1,NULL)))/7 AS userid_url_avg_7d,\n COUNT(IF(ds_max > 20260601, 1, NULL)) AS userid_url_7d,\n COUNT(1) AS userid_url_30d\n FROM\n (\n SELECT taid,\n url,\n MAX(ds) AS ds_max,\n MAX(IF(ds = 20260602,1,0)) AS state_6d,\n MAX(IF(ds = 20260603,1,0)) AS state_5d,\n MAX(IF(ds = 20260604,1,0)) AS state_4d,\n MAX(IF(ds = 20260605,1,0)) AS state_3d,\n MAX(IF(ds = 20260606,1,0)) AS state_2d,\n MAX(IF(ds = 20260607,1,0)) AS state_1d,\n MAX(IF(ds = 20260608,1,0)) AS state_daily\n FROM {DB_NAME}.{INPUT_TABLE}\n WHERE ds > 20260509 AND ds <= 20260608\n GROUP BY taid, url\n ) t\n) c ON a.data_par = c.data_par\nJOIN\n(\n SELECT 20260608 AS data_par,\n COUNT(IF(ds_max = 20260608, 1, NULL)) AS userid_domain_1d,\n (COUNT(IF(state_6d=1,1,NULL))+COUNT(IF(state_5d=1,1,NULL))+COUNT(IF(state_4d=1,1,NULL))+COUNT(IF(state_3d=1,1,NULL))+COUNT(IF(state_2d=1,1,NULL))+COUNT(IF(state_1d=1,1,NULL))+COUNT(IF(state_daily=1,1,NULL)))/7 AS userid_domain_avg_7d,\n COUNT(IF(ds_max > 20260601, 1, NULL)) AS userid_domain_7d,\n COUNT(1) AS userid_domain_30d,\n SUM(site_cnt_daily) AS userid_site_1d,\n (SUM(site_cnt_6d)+SUM(site_cnt_5d)+SUM(site_cnt_4d)+SUM(site_cnt_3d)+SUM(site_cnt_2d)+SUM(site_cnt_1d)+SUM(site_cnt_daily))/7 AS userid_site_avg_7d,\n SUM(site_cnt_7d) AS userid_site_7d,\n SUM(site_cnt) AS userid_site_30d\n FROM\n (\n SELECT taid,\n domain,\n MAX(ds_max) AS ds_max,\n MAX(IF(state_6d=1,1,0)) AS state_6d,\n MAX(IF(state_5d=1,1,0)) AS state_5d,\n MAX(IF(state_4d=1,1,0)) AS state_4d,\n MAX(IF(state_3d=1,1,0)) AS state_3d,\n MAX(IF(state_2d=1,1,0)) AS state_2d,\n MAX(IF(state_1d=1,1,0)) AS state_1d,\n MAX(IF(state_daily=1,1,0)) AS state_daily,\n COUNT(1) AS site_cnt,\n COUNT(IF(state_6d=1,1,NULL)) AS site_cnt_6d,\n COUNT(IF(state_5d=1,1,NULL)) AS site_cnt_5d,\n COUNT(IF(state_4d=1,1,NULL)) AS site_cnt_4d,\n COUNT(IF(state_3d=1,1,NULL)) AS site_cnt_3d,\n COUNT(IF(state_2d=1,1,NULL)) AS site_cnt_2d,\n COUNT(IF(state_1d=1,1,NULL)) AS site_cnt_1d,\n COUNT(IF(ds_max = 20260608,1,NULL)) AS site_cnt_daily,\n COUNT(IF(ds_max > 20260601,1,NULL)) AS site_cnt_7d\n FROM\n (\n SELECT taid,\n site,\n MAX(domain) AS domain,\n MAX(ds) AS ds_max,\n MAX(IF(ds = 20260602,1,0)) AS state_6d,\n MAX(IF(ds = 20260603,1,0)) AS state_5d,\n MAX(IF(ds = 20260604,1,0)) AS state_4d,\n MAX(IF(ds = 20260605,1,0)) AS state_3d,\n MAX(IF(ds = 20260606,1,0)) AS state_2d,\n MAX(IF(ds = 20260607,1,0)) AS state_1d,\n MAX(IF(ds = 20260608,1,0)) AS state_daily\n FROM {DB_NAME}.{INPUT_TABLE}\n WHERE ds > 20260509 AND ds <= 20260608\n GROUP BY taid, site\n ) t1\n GROUP BY taid, domain\n ) t\n) d ON a.data_par = d.data_par\nJOIN\n(\n SELECT 20260608 AS data_par,\n COUNT(IF(ds_max = 20260608, 1, NULL)) AS userid_cgi_1d,\n (COUNT(IF(state_6d=1,1,NULL))+COUNT(IF(state_5d=1,1,NULL))+COUNT(IF(state_4d=1,1,NULL))+COUNT(IF(state_3d=1,1,NULL))+COUNT(IF(state_2d=1,1,NULL))+COUNT(IF(state_1d=1,1,NULL))+COUNT(IF(state_daily=1,1,NULL)))/7 AS userid_cgi_avg_7d,\n COUNT(IF(ds_max > 20260601, 1, NULL)) AS userid_cgi_7d,\n COUNT(1) AS userid_cgi_30d\n FROM\n (\n SELECT taid,\n cgi,\n MAX(ds) AS ds_max,\n MAX(IF(ds = 20260602,1,0)) AS state_6d,\n MAX(IF(ds = 20260603,1,0)) AS state_5d,\n MAX(IF(ds = 20260604,1,0)) AS state_4d,\n MAX(IF(ds = 20260605,1,0)) AS state_3d,\n MAX(IF(ds = 20260606,1,0)) AS state_2d,\n MAX(IF(ds = 20260607,1,0)) AS state_1d,\n MAX(IF(ds = 20260608,1,0)) AS state_daily\n FROM {DB_NAME}.{INPUT_TABLE}\n WHERE ds > 20260509 AND ds <= 20260608\n GROUP BY taid, cgi\n ) t1\n) e ON a.data_par = e.data_par\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{CAND_TABLE} (\n access_type VARCHAR(256) DEFAULT NULL COMMENT '访问类型',\n access_channel VARCHAR(256) DEFAULT NULL COMMENT '访问渠道',\n uv_1d BIGINT DEFAULT NULL COMMENT '1日UV',\n uv_avg_7d BIGINT DEFAULT NULL COMMENT '7日平均UV',\n uv_7d BIGINT DEFAULT NULL COMMENT '7日UV',\n uv_30d BIGINT DEFAULT NULL COMMENT '30日UV',\n uv_1m BIGINT DEFAULT NULL COMMENT '1月UV',\n userid_url_1d BIGINT DEFAULT NULL COMMENT 'URL维度1日用户数',\n userid_url_avg_7d BIGINT DEFAULT NULL COMMENT 'URL维度7日平均用户数',\n userid_url_7d BIGINT DEFAULT NULL COMMENT 'URL维度7日用户数',\n userid_url_30d BIGINT DEFAULT NULL COMMENT 'URL维度30日用户数',\n userid_url_1m BIGINT DEFAULT NULL COMMENT 'URL维度1月用户数',\n userid_domain_1d BIGINT DEFAULT NULL COMMENT '域名维度1日用户数',\n userid_domain_avg_7d BIGINT DEFAULT NULL COMMENT '域名维度7日平均用户数',\n userid_domain_7d BIGINT DEFAULT NULL COMMENT '域名维度7日用户数',\n userid_domain_30d BIGINT DEFAULT NULL COMMENT '域名维度30日用户数',\n userid_domain_1m BIGINT DEFAULT NULL COMMENT '域名维度1月用户数',\n userid_site_1d BIGINT DEFAULT NULL COMMENT '站点维度1日用户数',\n userid_site_avg_7d BIGINT DEFAULT NULL COMMENT '站点维度7日平均用户数',\n userid_site_7d BIGINT DEFAULT NULL COMMENT '站点维度7日用户数',\n userid_site_30d BIGINT DEFAULT NULL COMMENT '站点维度30日用户数',\n userid_site_1m BIGINT DEFAULT NULL COMMENT '站点维度1月用户数',\n userid_cgi_1d BIGINT DEFAULT NULL COMMENT 'CGI维度1日用户数',\n userid_cgi_avg_7d BIGINT DEFAULT NULL COMMENT 'CGI维度7日平均用户数',\n userid_cgi_7d BIGINT DEFAULT NULL COMMENT 'CGI维度7日用户数',\n userid_cgi_30d BIGINT DEFAULT NULL COMMENT 'CGI维度30日用户数',\n userid_cgi_1m BIGINT DEFAULT NULL COMMENT 'CGI维度1月用户数',\n ds BIGINT DEFAULT NULL COMMENT '日期分区',\n channel VARCHAR(256) DEFAULT NULL COMMENT '渠道分区'\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{CAND_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_018 ground_truth done: 1 row written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_018_en"} {"task_id": "mysql_019", "id": "offline-compute_MySQL_mysql_019", "name": "传感器事件宽表关联用户组帖子分类圈组维度", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\n我需要你生成一段 MySQL 代码，将传感器事件数据关联用户组、帖子、分类和圈组维度，生成帖子事件明细宽表。\n\n**业务背景与目标**：传感器事件明细表记录了用户在社区中的各种行为（浏览帖子、点赞、收藏、评论等）。需要将事件数据与圈组成员关系、帖子信息、分类信息和圈组信息进行关联，生成一张帖子事件明细宽表，供下游分析使用。\n\n**输入表（全名 + 简要描述）**：\n\n- `internal_platform_db.dwd_knowledge_base_sensors_event_di_mysql_019`（传感器事件明细表）\n- `internal_platform_db.dwv_knowledge_base_groups_users_df_mysql_019`（圈组成员关系表）\n- `internal_platform_db.dwv_kb_posts_df_mysql_019`（帖子信息表）\n- `internal_platform_db.dwv_kb_categories_df_mysql_019`（分类信息表）\n- `internal_platform_db.dwv_knowledge_base_groups_df_mysql_019`（圈组信息表）\n\n（各表结构与字段含义请自行连接数据库查询确认）\n\n**处理规则**：\n\n1. 主表 t1 从 `dwd_knowledge_base_sensors_event_di_mysql_019` 筛选：\n - `concat(year,month,day)='20260608'`\n - `lower(event) in ('postdetailview','postdigg','postbooknowledge_baseark','postrecommend','commentsend','postcomment')`\n - `(group_id is not null or show_groups is not null)`\n - `post_id is not null and post_id != ''`\n - 派生字段：\n - `time as event_time`\n - `pc_or_mobile = if(lower(platform_type) in ('ios','android','h5'), 'mobile', 'pc')`\n - `group_id = ifnull(show_groups, group_id)`\n\n2. 左关联 t2（来自 `dwv_knowledge_base_groups_users_df_mysql_019`）：\n - 筛选条件：`concat(year,month,day)='20260608'` 且 `enabled=1`\n - 关联条件：`t1.distinct_id = t2.nick` 且 `t1.group_id = t2.group_id`\n - 派生字段：`is_group_member = IF(t2.nick is null, 0, 1)`\n\n3. 左关联 t3（`dwv_kb_posts_df_mysql_019` 左关联 `dwv_kb_categories_df_mysql_019`）：\n - t3 内部 tt1 来自 `dwv_kb_posts_df_mysql_019`，分区 `concat(year,month,day)='20260608'`\n - t3 内部 tt2 来自 `dwv_kb_categories_df_mysql_019`，分区 `concat(year,month,day)='20260608'`\n - tt1 和 tt2 通过 `tt1.category_id = tt2.id` 关联\n - t3 与 t1 通过 `post_id = id` 关联\n - 输出字段：`post_authorship`（来自 authorship）, `posts_category_id`（来自 category_id）, `posts_category_name`（来自 category_name 即 tt2.name）\n\n4. 左关联 t4（来自 `dwv_knowledge_base_groups_df_mysql_019`）：\n - 筛选条件：`concat(year,month,day)='20260608'`\n - 关联条件：`t1.group_id = t4.code`\n - 输出字段：`t4_group_id`（来自 t4.id）\n\n5. 最终 `group_id` 字段取值：`ifnull(t4_group_id, group_id)`\n\n**输出要求**：\n- 目标表：`internal_platform_db.dwd_knowledge_base_k_bar_posts_event_di_cand_mysql_019`\n- 输出字段顺序：`event`, `distinct_id`, `appname`, `user_id`, `event_time`, `receive_time`, `os`, `track_signup_original_id`, `author_nick`, `platform_type`, `target_type`, `target_id`, `ip`, `post_id`, `show_groups`, `group_id`（解析后）, `source_page`, `source_module`, `operation_type`, `pc_or_mobile`, `is_group_member`, `post_authorship`, `posts_category_id`, `posts_category_name`\n- 如果目标表不存在，请先按 MySQL InnoDB 标准建表，再写入数据\n- 请使用标准 MySQL 语法，不要使用 Hive/Spark SQL 方言（如 `INSERT OVERWRITE`、`STORED AS ORC`、`PARTITIONED BY` 等）\n\n**环境与执行说明**：\n- 你的最终产出必须写入文件 `/tmp_workspace/result.py`，不能写 result.sql\n- 本机 MySQL 已在 localhost:3306 运行，用户名 root，密码 root123\n- 请使用 Python pymysql 在 result.py 中执行 SQL（不要用 mysql 命令行）\n- 脚本需要包含完整的建表（如目标表不存在）+ 写入数据的逻辑\n- 写出 result.py 后，你必须自己执行 `python3 /tmp_workspace/result.py` 验证它能成功运行并产出正确数据", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_019 ground truth: 传感器事件宽表关联用户组帖子分类圈组维度\n\nTask:\n Join sensor events with groups_users, posts+categories, and groups\n to produce a posts event detail wide table.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE_SENSORS = \"dwd_knowledge_base_sensors_event_di_mysql_019\"\nINPUT_TABLE_GROUPS_USERS = \"dwv_knowledge_base_groups_users_df_mysql_019\"\nINPUT_TABLE_POSTS = \"dwv_kb_posts_df_mysql_019\"\nINPUT_TABLE_CADEPT_TORIES = \"dwv_kb_categories_df_mysql_019\"\nINPUT_TABLE_GROUPS = \"dwv_knowledge_base_groups_df_mysql_019\"\nOUTPUT_TABLE = \"dwd_knowledge_base_k_bar_posts_event_di_cand_mysql_019\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (event, distinct_id, appname, user_id, event_time, receive_time, os,\n track_signup_original_id, author_nick, platform_type, target_type, target_id,\n ip, post_id, show_groups, group_id, source_page, source_module, operation_type,\n pc_or_mobile, is_group_member, post_authorship, posts_category_id, posts_category_name,\n year, month, day)\nSELECT\n event,\n distinct_id,\n appname,\n user_id,\n event_time,\n receive_time,\n os,\n track_signup_original_id,\n author_nick,\n platform_type,\n target_type,\n target_id,\n ip,\n post_id,\n show_groups,\n IFNULL(t4_group_id, group_id) AS group_id,\n source_page,\n source_module,\n operation_type,\n pc_or_mobile,\n IF(t2.nick IS NULL, 0, 1) AS is_group_member,\n post_authorship,\n posts_category_id,\n posts_category_name,\n '2026' AS year,\n '06' AS month,\n '08' AS day\nFROM (\n SELECT\n event,\n distinct_id,\n appname,\n user_id,\n time AS event_time,\n receive_time,\n os,\n track_signup_original_id,\n author_nick,\n platform_type,\n target_type,\n target_id,\n ip,\n post_id,\n show_groups,\n IFNULL(show_groups, group_id) AS group_id,\n source_page,\n source_module,\n operation_type,\n IF(LOWER(platform_type) IN ('ios', 'android', 'h5'), 'mobile', 'pc') AS pc_or_mobile\n FROM\n {DB_NAME}.{INPUT_TABLE_SENSORS}\n WHERE\n CONCAT(year, month, day) = '20260608'\n AND LOWER(event) IN ('postdetailview', 'postdigg', 'postbooknowledge_baseark',\n 'postrecommend', 'commentsend', 'postcomment')\n AND (group_id IS NOT NULL OR show_groups IS NOT NULL)\n AND post_id IS NOT NULL AND post_id != ''\n) t1\nLEFT JOIN (\n SELECT\n nick,\n group_id AS t2_group_id\n FROM\n {DB_NAME}.{INPUT_TABLE_GROUPS_USERS}\n WHERE\n CONCAT(year, month, day) = '20260608'\n AND enabled = 1\n) t2\nON t1.distinct_id = t2.nick AND t1.group_id COLLATE utf8mb4_unicode_ci = CONVERT(t2.t2_group_id, CHAR) COLLATE utf8mb4_unicode_ci\nLEFT JOIN (\n SELECT\n id AS t3_id,\n authorship AS post_authorship,\n category_id AS posts_category_id,\n category_name AS posts_category_name\n FROM (\n SELECT\n id,\n authorship,\n category_id\n FROM\n {DB_NAME}.{INPUT_TABLE_POSTS}\n WHERE\n CONCAT(year, month, day) = '20260608'\n ) tt1\n LEFT JOIN (\n SELECT\n id AS tt2_id,\n name AS category_name\n FROM\n {DB_NAME}.{INPUT_TABLE_CADEPT_TORIES}\n WHERE\n CONCAT(year, month, day) = '20260608'\n ) tt2\n ON tt1.category_id = tt2.tt2_id\n) t3\nON t1.post_id COLLATE utf8mb4_unicode_ci = CONVERT(t3.t3_id, CHAR) COLLATE utf8mb4_unicode_ci\nLEFT JOIN (\n SELECT\n id AS t4_group_id,\n code AS t4_group_code\n FROM\n {DB_NAME}.{INPUT_TABLE_GROUPS}\n WHERE\n CONCAT(year, month, day) = '20260608'\n) t4\nON t1.group_id = t4.t4_group_code\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n event VARCHAR(256) DEFAULT NULL,\n distinct_id VARCHAR(256) DEFAULT NULL,\n appname VARCHAR(256) DEFAULT NULL,\n user_id VARCHAR(256) DEFAULT NULL,\n event_time VARCHAR(256) DEFAULT NULL,\n receive_time VARCHAR(256) DEFAULT NULL,\n os VARCHAR(256) DEFAULT NULL,\n track_signup_original_id VARCHAR(256) DEFAULT NULL,\n author_nick VARCHAR(256) DEFAULT NULL,\n platform_type VARCHAR(256) DEFAULT NULL,\n target_type VARCHAR(256) DEFAULT NULL,\n target_id VARCHAR(256) DEFAULT NULL,\n ip VARCHAR(256) DEFAULT NULL,\n post_id VARCHAR(256) DEFAULT NULL,\n show_groups VARCHAR(256) DEFAULT NULL,\n group_id VARCHAR(256) DEFAULT NULL,\n source_page VARCHAR(256) DEFAULT NULL,\n source_module VARCHAR(256) DEFAULT NULL,\n operation_type VARCHAR(256) DEFAULT NULL,\n pc_or_mobile VARCHAR(256) DEFAULT NULL,\n is_group_member INT DEFAULT NULL,\n post_authorship VARCHAR(256) DEFAULT NULL,\n posts_category_id BIGINT DEFAULT NULL,\n posts_category_name VARCHAR(256) DEFAULT NULL,\n year VARCHAR(256) DEFAULT NULL,\n month VARCHAR(256) DEFAULT NULL,\n day VARCHAR(256) DEFAULT NULL\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_019 ground_truth done: rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_019"} {"task_id": "mysql_020_en", "id": "offline-compute_MySQL_mysql_020", "name": "Build Volume-Lift Candidate Ad List", "workload": "offline-compute", "engine": "MySQL", "category": "offline-compute/MySQL", "language": "en", "modality": "pure-text", "timeout_seconds": 1800, "prompt": "## Prompt\nI need you to generate a MySQL script that builds a volume-lift candidate ad list, outputting to table `internal_platform_db.dwm_union_raise_candidate_new_ads_hf_cand_mysql_020` with partition key `p_partition='2026060905'`.\n\n**Business Background and Objective**: The ad volume-lift system needs to jointly filter qualifying ads from multiple dimensions (creative, ad group, ecology budget, advertiser, conversion link, volume-lift configuration, creative platform) to generate a candidate list for downstream consumption. This task requires multi-table JOINs, aggregation, window function ranking, and filtering across 7 input tables, ultimately writing to the output table.\n\n**Input Tables (full name + brief description)**:\n- `internal_platform_db.dim_creative_info_f_mysql_020` (creative information)\n- `internal_platform_db.dim_adgroup_info_f_mysql_020` (ad group information)\n- `internal_platform_db.dim_adgroup_ecology_budget_info_di_mysql_020` (ecology budget information)\n- `internal_platform_db.f_union_dim_advertiser_info_d_mysql_020` (advertiser information)\n- `internal_platform_db.t_daily_conv_link_tid_dimension_mysql_020` (conversion link dimension)\n- `internal_platform_db.dim_union_ad_raised_config_hf_mysql_020` (volume-lift configuration)\n- `internal_platform_db.dim_tbl_creative_f_mysql_020` (creative platform information)\n\n(Please connect to the database and query to confirm the table structures and field semantics.)\n\n**Processing Rules**:\n1. Main table a (`dim_creative_info_f`): `creative_id > 0`, aggregate by `creative_id` taking `MAX(adgroup_id)`, `MAX(advertiser_id)`, `MAX(landing_page_type)`\n2. Subquery e (`dim_adgroup_info_f`) INNER JOIN:\n - Filter `adgroup_id > 0`\n - Filter `placement_group_id_list` containing 15 or 136 (using the `FIND_IN_SET` function)\n - `begin_time >= MIN(begintime)` from the config table AND `<= MAX(endtime)`, config table conditions: `partition_time = 2026060905`, `strategyid > 0`, `20260609` within the `begintime-endtime` range (use `FROM_UNIXTIME` to convert unix timestamps to date format `yyyyMMdd` for comparison)\n - Aggregate by `adgroup_id` taking `MAX(product_id)`, `MAX(optimization_goal)`, `MAX(second_optimization_goal)`, `MAX(deep_conversion_optimization_goal)`, `MAX(marketing_target_id)`, `MAX(begin_time)`, `MAX(end_time)`, `MAX(created_time)`, `MAX(exploration_strategy_id)`, `MAX(placement_group_id_list)`\n3. Subquery b (`dim_adgroup_ecology_budget_info_di`) LEFT JOIN: `partition_time` between 20260607–20260608, take the latest partition's `ecology_level2_id` per `creative_id` (using the `ROW_NUMBER` window function ranked by `partition_time DESC`, taking rn=1)\n4. Subquery c (`f_union_dim_advertiser_info_d`) LEFT JOIN: `partition_time` between 20260607–20260608, first aggregate by `advertiser_id + partition_time` taking `MAX(operation_industry_name) AS team`, `MAX(short_advertiser_name)`, then take the latest partition's `team`, `short_advertiser_name` per `advertiser_id` (ROW_NUMBER)\n5. Subquery d (`t_daily_conv_link_tid_dimension`) LEFT JOIN: `partition_time` between 20260607–20260608, first aggregate by `tid + partition_time` taking `MAX(landingpage_link_type)`, then take the latest partition's `landingpage_link_type` per `tid` (ROW_NUMBER), join condition `a.creative_id = d.tid`\n6. Subquery f (`dim_tbl_creative_f`) LEFT JOIN: `ftid > 0`, aggregate by `ftid` taking `MAX(fsmartdeliveryplatform) AS smart_delivery_platform`, join condition `a.creative_id = f.ftid`\n7. Final SELECT: `partition_time = 2026060905`, `adgroup_id`, `COALESCE(advertiser_id, 0)`, `COALESCE(product_id, '')`, `CONCAT(COALESCE(optimization_goal, 0), '_', COALESCE(second_optimization_goal, 0), '_', COALESCE(deep_conversion_optimization_goal, 0)) AS mix_goal`, `COALESCE(landing_page_type, '')`, `COALESCE(marketing_target_id, 0)`, `COALESCE(ecology_level2_id, 0)`, `COALESCE(team, '')`, `COALESCE(short_advertiser_name, '')`, `COALESCE(landingpage_link_type, '')`, `COALESCE(begin_time, 0)`, `COALESCE(end_time, 0)`, `COALESCE(created_time, 0)`, `COALESCE(smart_delivery_platform, 0)`, `COALESCE(exploration_strategy_id, 0)`, `COALESCE(placement_group_id_list, '')`\n\n**Output Requirements**:\n- Target table: `internal_platform_db.dwm_union_raise_candidate_new_ads_hf_cand_mysql_020`\n- Output field order: `partition_time`, `adgroup_id`, `advertiser_id`, `product_id`, `mix_goal`, `landing_page_type`, `marketing_target_id`, `ecology_level2_id`, `team`, `short_advertiser_name`, `landingpage_link_type`, `begin_time`, `end_time`, `created_time`, `smart_delivery_platform`, `exploration_strategy_id`, `placement_group_id_list`, `p_partition`\n- `mix_goal` is generated by concatenating three optimization goal fields with underscores; null values are replaced with defaults using COALESCE (0 for numeric, empty string for string)\n- Final deduplication by GROUP BY on all non-`partition_time` fields\n- `p_partition` is fixed as `'2026060905'`\n- If the target table does not exist, first create it using standard MySQL InnoDB format, then write the data\n- Use standard MySQL syntax; do not use Hive/Spark SQL dialects (e.g., `INSERT OVERWRITE`, `ARRAY` type, `array_contains`, `concat_ws` for arrays, and other Hive-specific functions are not supported)\n\n**Environment and Execution Notes**:\n- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`\n- The local MySQL is running at localhost:3306, username `root`, password `root123`\n- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)\n- The script must include complete table creation (if the target table does not exist) and data writing logic\n- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data", "ground_truth": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nmysql_020 ground truth: 构建提量候选广告列表\n\nTask:\n Build raise-candidate ad list from 7 input tables via multi-table JOIN,\n aggregation, window functions, and filtering, write to output table\n with p_partition='2026060905'.\n\"\"\"\nimport pymysql\nimport sys\n\nDB_NAME = \"internal_platform_db\"\nINPUT_TABLE_CREATIVE = \"dim_creative_info_f_mysql_020\"\nINPUT_TABLE_ADGROUP = \"dim_adgroup_info_f_mysql_020\"\nINPUT_TABLE_ECOLOGY = \"dim_adgroup_ecology_budget_info_di_mysql_020\"\nINPUT_TABLE_ADVERTISER = \"f_union_dim_advertiser_info_d_mysql_020\"\nINPUT_TABLE_CONV_LINK = \"t_daily_conv_link_tid_dimension_mysql_020\"\nINPUT_TABLE_CONFIG = \"dim_union_ad_raised_config_hf_mysql_020\"\nINPUT_TABLE_CREATIVE_PLATFORM = \"dim_tbl_creative_f_mysql_020\"\nOUTPUT_TABLE = \"dwm_union_raise_candidate_new_ads_hf_cand_mysql_020\"\n\nMYSQL_CONFIG = {\n \"host\": \"localhost\",\n \"port\": 3306,\n \"user\": \"root\",\n \"password\": \"root123\",\n \"charset\": \"utf8mb4\",\n}\n\ngt_sql = f\"\"\"\nINSERT INTO {DB_NAME}.{OUTPUT_TABLE}\n (partition_time, adgroup_id, advertiser_id, product_id, mix_goal,\n landing_page_type, marketing_target_id, ecology_level2_id, team,\n short_advertiser_name, landingpage_link_type, begin_time, end_time,\n created_time, smart_delivery_platform, exploration_strategy_id,\n placement_group_id_list, p_partition)\nSELECT\n 2026060905 AS partition_time,\n a.adgroup_id,\n COALESCE(a.advertiser_id, 0) AS advertiser_id,\n COALESCE(e.product_id, '') AS product_id,\n CONCAT(COALESCE(e.optimization_goal, 0), '_',\n COALESCE(e.second_optimization_goal, 0), '_',\n COALESCE(e.deep_conversion_optimization_goal, 0)) AS mix_goal,\n COALESCE(a.landing_page_type, '') AS landing_page_type,\n COALESCE(e.marketing_target_id, 0) AS marketing_target_id,\n COALESCE(b.ecology_level2_id, 0) AS ecology_level2_id,\n COALESCE(c.team, '') AS team,\n COALESCE(c.short_advertiser_name, '') AS short_advertiser_name,\n COALESCE(d.landingpage_link_type, '') AS landingpage_link_type,\n COALESCE(e.begin_time, 0) AS begin_time,\n COALESCE(e.end_time, 0) AS end_time,\n COALESCE(e.created_time, 0) AS created_time,\n COALESCE(f.smart_delivery_platform, 0) AS smart_delivery_platform,\n COALESCE(e.exploration_strategy_id, 0) AS exploration_strategy_id,\n COALESCE(e.placement_group_id_list, '') AS placement_group_id_list,\n '2026060905' AS p_partition\nFROM (\n SELECT creative_id,\n MAX(adgroup_id) AS adgroup_id,\n MAX(advertiser_id) AS advertiser_id,\n MAX(landing_page_type) AS landing_page_type\n FROM {DB_NAME}.{INPUT_TABLE_CREATIVE}\n WHERE creative_id > 0\n GROUP BY creative_id\n) a\nLEFT JOIN (\n SELECT creative_id, ecology_level2_id\n FROM (\n SELECT creative_id, partition_time, ecology_level2_id,\n ROW_NUMBER() OVER (PARTITION BY creative_id ORDER BY partition_time DESC) AS rn\n FROM {DB_NAME}.{INPUT_TABLE_ECOLOGY}\n WHERE partition_time BETWEEN 20260607 AND 20260608\n ) ranked\n WHERE rn = 1\n) b ON a.creative_id = b.creative_id\nLEFT JOIN (\n SELECT advertiser_id, team, short_advertiser_name\n FROM (\n SELECT advertiser_id, partition_time, team, short_advertiser_name,\n ROW_NUMBER() OVER (PARTITION BY advertiser_id ORDER BY partition_time DESC) AS rn\n FROM (\n SELECT advertiser_id, partition_time,\n MAX(operation_industry_name) AS team,\n MAX(short_advertiser_name) AS short_advertiser_name\n FROM {DB_NAME}.{INPUT_TABLE_ADVERTISER}\n WHERE partition_time BETWEEN 20260607 AND 20260608\n GROUP BY advertiser_id, partition_time\n ) agg\n ) ranked\n WHERE rn = 1\n) c ON a.advertiser_id = c.advertiser_id\nLEFT JOIN (\n SELECT tid, landingpage_link_type\n FROM (\n SELECT tid, partition_time, landingpage_link_type,\n ROW_NUMBER() OVER (PARTITION BY tid ORDER BY partition_time DESC) AS rn\n FROM (\n SELECT tid, partition_time,\n MAX(landingpage_link_type) AS landingpage_link_type\n FROM {DB_NAME}.{INPUT_TABLE_CONV_LINK}\n WHERE partition_time BETWEEN 20260607 AND 20260608\n GROUP BY tid, partition_time\n ) agg\n ) ranked\n WHERE rn = 1\n) d ON a.creative_id = d.tid\nJOIN (\n SELECT adgroup_id,\n MAX(product_id) AS product_id,\n MAX(optimization_goal) AS optimization_goal,\n MAX(second_optimization_goal) AS second_optimization_goal,\n MAX(deep_conversion_optimization_goal) AS deep_conversion_optimization_goal,\n MAX(marketing_target_id) AS marketing_target_id,\n MAX(begin_time) AS begin_time,\n MAX(end_time) AS end_time,\n MAX(created_time) AS created_time,\n MAX(exploration_strategy_id) AS exploration_strategy_id,\n MAX(placement_group_id_list) AS placement_group_id_list\n FROM {DB_NAME}.{INPUT_TABLE_ADGROUP}\n WHERE adgroup_id > 0\n AND (FIND_IN_SET(15, placement_group_id_list) > 0 OR FIND_IN_SET(136, placement_group_id_list) > 0)\n AND begin_time >= (\n SELECT MIN(begintime)\n FROM {DB_NAME}.{INPUT_TABLE_CONFIG}\n WHERE partition_time = 2026060905\n AND strategyid > 0\n AND 20260609 >= DATE_FORMAT(FROM_UNIXTIME(begintime), '%Y%m%d')\n AND 20260609 <= DATE_FORMAT(FROM_UNIXTIME(endtime), '%Y%m%d')\n )\n AND begin_time <= (\n SELECT MAX(endtime)\n FROM {DB_NAME}.{INPUT_TABLE_CONFIG}\n WHERE partition_time = 2026060905\n AND strategyid > 0\n AND 20260609 >= DATE_FORMAT(FROM_UNIXTIME(begintime), '%Y%m%d')\n AND 20260609 <= DATE_FORMAT(FROM_UNIXTIME(endtime), '%Y%m%d')\n )\n GROUP BY adgroup_id\n) e ON a.adgroup_id = e.adgroup_id\nLEFT JOIN (\n SELECT ftid,\n MAX(fsmartdeliveryplatform) AS smart_delivery_platform\n FROM {DB_NAME}.{INPUT_TABLE_CREATIVE_PLATFORM}\n WHERE ftid > 0\n GROUP BY ftid\n) f ON a.creative_id = f.ftid\nGROUP BY\n a.adgroup_id,\n COALESCE(a.advertiser_id, 0),\n COALESCE(e.product_id, ''),\n CONCAT(COALESCE(e.optimization_goal, 0), '_',\n COALESCE(e.second_optimization_goal, 0), '_',\n COALESCE(e.deep_conversion_optimization_goal, 0)),\n COALESCE(a.landing_page_type, ''),\n COALESCE(e.marketing_target_id, 0),\n COALESCE(b.ecology_level2_id, 0),\n COALESCE(c.team, ''),\n COALESCE(c.short_advertiser_name, ''),\n COALESCE(d.landingpage_link_type, ''),\n COALESCE(e.begin_time, 0),\n COALESCE(e.end_time, 0),\n COALESCE(e.created_time, 0),\n COALESCE(f.smart_delivery_platform, 0),\n COALESCE(e.exploration_strategy_id, 0),\n COALESCE(e.placement_group_id_list, '')\n\"\"\"\n\n\ndef main():\n conn = pymysql.connect(**MYSQL_CONFIG)\n\n try:\n with conn.cursor() as cur:\n # Ensure output table exists\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} (\n partition_time BIGINT NOT NULL COMMENT '分区时间',\n adgroup_id BIGINT NOT NULL COMMENT '广告组ID',\n advertiser_id BIGINT NOT NULL COMMENT '广告主ID',\n product_id VARCHAR(256) NOT NULL COMMENT '产品ID',\n mix_goal VARCHAR(256) NOT NULL COMMENT '混合优化目标',\n landing_page_type VARCHAR(256) NOT NULL COMMENT '落地页类型',\n marketing_target_id BIGINT NOT NULL COMMENT '营销目标ID',\n ecology_level2_id INT NOT NULL COMMENT '生态二级ID',\n team VARCHAR(256) NOT NULL COMMENT '团队',\n short_advertiser_name VARCHAR(256) NOT NULL COMMENT '广告主简称',\n landingpage_link_type VARCHAR(256) NOT NULL COMMENT '落地页链接类型',\n begin_time BIGINT NOT NULL COMMENT '开始时间',\n end_time BIGINT NOT NULL COMMENT '结束时间',\n created_time BIGINT NOT NULL COMMENT '创建时间',\n smart_delivery_platform INT NOT NULL COMMENT '智能投放平台',\n exploration_strategy_id BIGINT NOT NULL COMMENT '探索策略ID',\n placement_group_id_list VARCHAR(256) NOT NULL COMMENT '广告位组ID列表',\n p_partition VARCHAR(256) NOT NULL COMMENT '分区键'\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4\n \"\"\")\n\n # Truncate + insert\n cur.execute(f\"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}\")\n cur.execute(gt_sql)\n\n conn.commit()\n print(\"mysql_020 ground_truth done: rows written to output table\")\n except Exception as e:\n print(f\"ground_truth error: {e}\", file=sys.stderr)\n conn.rollback()\n sys.exit(1)\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()", "expected_csv": "", "grade_spec_csv": "", "path": "tasks/offline-compute/MySQL/mysql_020_en"}