#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ mysql_014 ground truth: Notebook执行实例明细按天拆分与引擎关联 Task: Filter notebook_span_info for traces with execute.code spans, split cross-day records, calculate time metrics, left join engine info, write to output table. """ import pymysql import sys DB_NAME = "internal_platform_db" INPUT_TABLE_1 = "notebook_span_info_mysql_014" INPUT_TABLE_2 = "notebook_engine_info_mysql_014" OUTPUT_TABLE = "dwd_notebook_execute_instance_detail_d_mysql_014" MYSQL_CONFIG = { "host": "localhost", "port": 3306, "user": "root", "password": "root123", "charset": "utf8mb4", } gt_sql = f""" INSERT INTO {DB_NAME}.{OUTPUT_TABLE} (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) WITH base_data_raw AS ( SELECT DISTINCT trace_id, span_name, start_time, end_time, datawd_project_id, datawd_task_id, datawd_task_instance_id, compute_type, status_code FROM {DB_NAME}.{INPUT_TABLE_1} WHERE databus_imp_date >= '2026050400' AND databus_imp_date <= '2026050700' AND trace_id IN ( SELECT trace_id FROM {DB_NAME}.{INPUT_TABLE_1} WHERE databus_imp_date >= '2026050700' AND databus_imp_date <= '2026050700' AND compute_type = 'ray' AND service_name = 'notebook-runner' AND span_name = 'runner.execute' GROUP BY trace_id ) AND ( span_name = 'runner.execute' OR span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'set.permanent.compute', 'create.non.permanent.compute', 'runner.killed') ) ), base_data_time_fixed AS ( SELECT trace_id, span_name, CASE WHEN span_name = 'runner.killed' THEN MIN(CASE WHEN span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'runner.killed') THEN start_time END) OVER(PARTITION BY trace_id) ELSE start_time END AS start_time, CASE WHEN span_name = 'runner.execute' THEN MAX(CASE WHEN span_name IN ('runner.execute', 'runner.killed') THEN end_time END) OVER(PARTITION BY trace_id) ELSE end_time END AS end_time, datawd_project_id, datawd_task_id, datawd_task_instance_id, compute_type, status_code FROM base_data_raw ), base_data AS ( SELECT trace_id, span_name, start_time, end_time, datawd_project_id, datawd_task_id, datawd_task_instance_id, compute_type, status_code, DATE(FROM_UNIXTIME(CAST(start_time AS SIGNED) / 1000)) as start_date, DATE(FROM_UNIXTIME(CAST(end_time AS SIGNED) / 1000)) as end_date, DATEDIFF(DATE(FROM_UNIXTIME(CAST(end_time AS SIGNED) / 1000)), DATE(FROM_UNIXTIME(CAST(start_time AS SIGNED) / 1000))) AS diff_days FROM base_data_time_fixed ), pos_series AS ( SELECT 0 AS pos UNION ALL SELECT 1 AS pos UNION ALL SELECT 2 AS pos UNION ALL SELECT 3 AS pos ), daily_split_spans AS ( SELECT trace_id, span_name, datawd_project_id, datawd_task_id, datawd_task_instance_id, compute_type, status_code, start_date, end_date, diff_days, DATE_ADD(b.start_date, INTERVAL s.pos DAY) AS calc_date, CASE WHEN s.pos = 0 THEN CAST(b.start_time AS SIGNED) ELSE UNIX_TIMESTAMP(CAST(DATE_ADD(b.start_date, INTERVAL s.pos DAY) AS DATETIME)) * 1000 END AS start_time, CASE WHEN s.pos = b.diff_days THEN CAST(b.end_time AS SIGNED) ELSE (UNIX_TIMESTAMP(CAST(DATE_ADD(b.start_date, INTERVAL s.pos + 1 DAY) AS DATETIME)) * 1000) - 1 END AS end_time, CAST(b.start_time AS SIGNED) AS span_start_time, CAST(b.end_time AS SIGNED) AS span_end_time FROM base_data b INNER JOIN pos_series s ON s.pos <= b.diff_days ), trace_time_metrics AS ( SELECT trace_id, calc_date, MAX(datawd_project_id) AS datawd_project_id, MAX(datawd_task_id) AS datawd_task_id, MAX(datawd_task_instance_id) AS datawd_task_instance_id, MAX(compute_type) AS compute_type, MAX(MAX(CASE WHEN span_name = 'runner.execute' THEN status_code END)) OVER(PARTITION BY trace_id) AS status_code, ROUND((MAX(CASE WHEN span_name = 'runner.execute' THEN end_time END) - MIN(CASE WHEN span_name = 'runner.execute' THEN start_time END)) / 1000.0) AS instance_run_time, ROUND((MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed') THEN end_time END) - 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, ROUND((MAX(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN end_time END) - MIN(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN start_time END)) / 1000.0) AS resource_wait_time, 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, 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, FROM_UNIXTIME(MIN(CASE WHEN span_name = 'runner.execute' THEN span_start_time END) / 1000) AS instance_start_time, FROM_UNIXTIME(MAX(CASE WHEN span_name = 'runner.execute' THEN span_end_time END) / 1000) AS instance_end_time FROM daily_split_spans GROUP BY trace_id, calc_date ) SELECT '20260507' AS dt, DATE_FORMAT(t1.calc_date, '%Y-%m-%d') AS p_date, t1.trace_id, t1.datawd_project_id, t1.datawd_task_id, t1.datawd_task_instance_id, t1.compute_type, t1.status_code, t1.instance_run_time, t1.code_run_time, t1.resource_wait_time, DATE_FORMAT(t1.code_start_time, '%Y-%m-%d %H:%i:%s') AS code_start_time, DATE_FORMAT(t1.code_end_time, '%Y-%m-%d %H:%i:%s') AS code_end_time, DATE_FORMAT(t1.instance_start_time, '%Y-%m-%d %H:%i:%s') AS instance_start_time, DATE_FORMAT(t1.instance_end_time, '%Y-%m-%d %H:%i:%s') AS instance_end_time, t2.serving_id, t2.is_permanent, t2.apply_for_gpu_count FROM trace_time_metrics t1 LEFT JOIN ( SELECT trace_id, MAX(serving_id) AS serving_id, MAX(is_permanent) AS is_permanent, SUM(CAST(replicas AS SIGNED) * CAST(num_gpu AS SIGNED)) AS apply_for_gpu_count FROM {DB_NAME}.{INPUT_TABLE_2} WHERE databus_imp_date >= '2026050400' AND databus_imp_date <= '2026050700' AND compute_type = 'ray' AND service_name = 'notebook-runner' GROUP BY trace_id ) t2 ON t1.trace_id = t2.trace_id """ def main(): conn = pymysql.connect(**MYSQL_CONFIG) try: with conn.cursor() as cur: # Set timezone to Asia/Shanghai so FROM_UNIXTIME aligns with expected dates cur.execute("SET time_zone = '+08:00'") # Ensure output table exists cur.execute(f""" CREATE TABLE IF NOT EXISTS {DB_NAME}.{OUTPUT_TABLE} ( `dt` VARCHAR(256) COMMENT '天分区', `p_date` VARCHAR(256) COMMENT '拆分后日期', `trace_id` VARCHAR(256) COMMENT 'trace_id', `datawd_project_id` VARCHAR(256) COMMENT '项目ID', `datawd_task_id` VARCHAR(256) COMMENT '任务ID', `datawd_task_instance_id` VARCHAR(256) COMMENT '任务实例ID', `compute_type` VARCHAR(256) COMMENT '计算类型', `status_code` INT COMMENT '状态码', `instance_run_time` INT COMMENT '实例运行时长(秒)', `code_run_time` INT COMMENT '代码执行时长(秒)', `resource_wait_time` INT COMMENT '资源等待时长(秒)', `code_start_time` VARCHAR(256) COMMENT '代码开始时间', `code_end_time` VARCHAR(256) COMMENT '代码结束时间', `instance_start_time` VARCHAR(256) COMMENT '实例开始时间', `instance_end_time` VARCHAR(256) COMMENT '实例结束时间', `serving_id` VARCHAR(256) COMMENT 'serving_id', `is_permanent` VARCHAR(256) COMMENT '是否永久引擎', `apply_for_gpu_count` INT COMMENT 'GPU申请数量' ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 """) # Truncate + insert cur.execute(f"TRUNCATE TABLE {DB_NAME}.{OUTPUT_TABLE}") cur.execute(gt_sql) conn.commit() # Verify row count with conn.cursor() as cur: cur.execute(f"SELECT COUNT(*) FROM {DB_NAME}.{OUTPUT_TABLE}") count = cur.fetchone()[0] print(f"mysql_014 ground_truth done: {count} 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()