#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ mysql_003 ground truth: 离线推理任务特征宽表构建 Task: Build an offline inference task feature wide table by joining 7 input tables, aggregating pipeline timing and wait time stats, extracting model info via JSON, and writing to the output table. WHERE conditions match the original Hive SQL: - a.dt = '2026050700' - a.base = 'JOB' AND a.model_type = 'STATIC_MODEL' - a.create_time < '2026-05-07 01:00:00' (dt parsed + 1 hour) - status NOT IN ('FINISH','KILL','FAILED') OR update_time >= '2026-05-06 23:00:00' (dt parsed - 1 hour) """ import pymysql import sys DB_NAME = "internal_platform_db" OUTPUT_TABLE = "dwd_aide_offline_inference_feature_cand_mysql_003" MYSQL_CONFIG = { "host": "localhost", "port": 3306, "user": "root", "password": "root123", "charset": "utf8mb4", } gt_sql = f""" INSERT INTO {DB_NAME}.{OUTPUT_TABLE} (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) WITH -- Step 1: Aggregate pipeline timing by task_id pipeline_time_agg AS ( SELECT task_id, SUM(time_cost_in_second) AS time_cost_in_second, CAST(SUM(CASE WHEN step_desc = '任务下发' THEN time_cost_in_second ELSE 0 END) AS SIGNED) AS task_dispatch_time, CAST(SUM(CASE WHEN step_desc = '离线推理' THEN time_cost_in_second ELSE 0 END) AS SIGNED) AS offline_inference_time FROM {DB_NAME}.aide_offline_inference_pipeline_time_mysql_003 WHERE end_time IS NOT NULL GROUP BY task_id ), -- Step 2: Aggregate wait time stats by service_name (latest partition) service_wait_time_stats AS ( SELECT service_name, CAST(MAX(first_wait_time) AS DOUBLE) AS max_instance_first_wait_time, CAST(AVG(first_wait_time) AS DOUBLE) AS avg_instance_first_wait_time FROM {DB_NAME}.task_instance_wait_time_stats_mysql_003 WHERE dt = (SELECT MAX(dt) FROM {DB_NAME}.task_instance_wait_time_stats_mysql_003) AND first_wait_time IS NOT NULL GROUP BY service_name ) -- Step 3: Join all tables and extract features SELECT '2026050700' AS dt, a.create_time, 1 AS ct, a.id, a.name AS `name`, CONCAT(a.name, '_', c.id, '_OFFLINE') AS servingName, a.base, a.status, a.submit_type, JSON_UNQUOTE(JSON_EXTRACT(a.model_ids, '$.mould_id')) AS mould_id, JSON_UNQUOTE(JSON_EXTRACT(a.model_ids, '$.mould_name')) AS mould_name, d.time_cost_in_second, b.dataset_act_num, b.dataset_fin_num, b.dataset_err_num, w.max_instance_first_wait_time, w.avg_instance_first_wait_time, d.task_dispatch_time, d.offline_inference_time, a.inference_config, e.operator AS submit_operator, e.create_time AS submit_time, f.image_tag FROM {DB_NAME}.aide_offline_inference_info_fixed_mysql_003 a LEFT JOIN {DB_NAME}.aide_offline_inference_dataset_info_h_mysql_003 b ON a.id = b.offline_inference_id LEFT JOIN {DB_NAME}.aide_offline_inference_stage_info_h_mysql_003 c ON a.id = c.offline_inference_id LEFT JOIN pipeline_time_agg d ON a.id = d.task_id LEFT JOIN service_wait_time_stats w ON CONCAT(a.name, '_', c.id, '_OFFLINE') = w.service_name LEFT JOIN ( SELECT id, operator, create_time, ROW_NUMBER() OVER(PARTITION BY id ORDER BY update_time DESC) AS rn FROM {DB_NAME}.aide_offline_inference_file_info_h_mysql_003 WHERE dt = '2026050700' ) e ON a.file_id = e.id AND e.rn = 1 LEFT JOIN {DB_NAME}.aide_mould_h_mysql_003 f ON CAST(JSON_UNQUOTE(JSON_EXTRACT(a.model_ids, '$.mould_id')) AS SIGNED) = f.id WHERE a.dt = '2026050700' AND a.base = 'JOB' AND a.model_type = 'STATIC_MODEL' AND a.create_time < '2026-05-07 01:00:00' AND ( a.status NOT IN ('FINISH', 'KILL', 'FAILED') OR a.update_time >= '2026-05-06 23:00:00' ) """ def main(): conn = pymysql.connect(**MYSQL_CONFIG) try: with conn.cursor() as cur: # Ensure output table exists cur.execute(f""" CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} ( dt VARCHAR(256) COMMENT '分区字段', create_time VARCHAR(256) COMMENT '创建时间', ct INT COMMENT '计数标识', id BIGINT COMMENT '离线推理任务ID', name VARCHAR(256) COMMENT '任务名称', servingName VARCHAR(256) COMMENT '服务名称', base VARCHAR(256) COMMENT '基础类型', status VARCHAR(256) COMMENT '状态', submit_type VARCHAR(256) COMMENT '提交类型', mould_id VARCHAR(256) COMMENT '模型ID', mould_name VARCHAR(256) COMMENT '模型名称', time_cost_in_second BIGINT COMMENT '总耗时(秒)', dataset_act_num BIGINT COMMENT '数据集激活数量', dataset_fin_num BIGINT COMMENT '数据集完成数量', dataset_err_num BIGINT COMMENT '数据集错误数量', max_instance_first_wait_time DOUBLE COMMENT '最大首次等待时长(秒)', avg_instance_first_wait_time DOUBLE COMMENT '平均首次等待时长(秒)', task_dispatch_time BIGINT COMMENT '任务下发耗时(秒)', offline_inference_time BIGINT COMMENT '离线推理耗时(秒)', inference_config VARCHAR(256) COMMENT '推理配置', submit_operator VARCHAR(256) COMMENT '提交人', submit_time VARCHAR(256) COMMENT '提交时间', image_tag VARCHAR(256) COMMENT '镜像标签' ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 """) # Truncate + insert cur.execute(f"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}") cur.execute(gt_sql) conn.commit() print("mysql_003 ground_truth done: rows written to output table") except Exception as e: print(f"ground_truth error: {e}", file=sys.stderr) conn.rollback() sys.exit(1) finally: conn.close() if __name__ == "__main__": main()