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#!/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()