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ThreadPoolExecutor alway alive after close batching API and database client

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评估

难度
3/5
预计耗时
1-2 天
新手友好度
38/100
Issue 类型
缺陷
描述清晰度
基本清楚
活跃度
停滞
技术栈
python
领域
databases

调研方向

首先跟踪使用 WriteOptions 和 WriteType.batching 的 InfluxDBClient.write_api(),然后继续跟踪 ThreadPoolExecutor 的 close() 和 flush() 路径。使用提供的脚本重现该问题;在关闭 batching API 和客户端后,executor thread 不再列出即表示完成。

由索引模型根据 Issue 内容生成。

描述

bug
Specifications
  • Client Version: 1.40.0
  • InfluxDB Version: 2.7.1
  • Platform: Ubuntu
  • Python Version: 3.10.9
Code sample to reproduce problem
import threading
import time
from influxdb_client import (
    InfluxDBClient
)
from influxdb_client.client.write_api import (
    WriteOptions,
    WriteType,
)


def check_thread_alive():
    """Check if all thread are correctly closed."""
    msg: str = f"{len(threading.enumerate())} threads running"
    for thread in threading.enumerate():
        msg += f"\n > {thread.name}"
    print(msg)


data_points = [
    {
        "measurement": "temperature",
        "tags": {"location": "room1"},
        "time": "2024-02-02T12:00:00Z",
        "fields": {"value": 25.5}
    },
    {
        "measurement": "humidity",
        "tags": {"location": "room1"},
        "time": "2024-02-02T12:00:00Z",
        "fields": {"value": 60}
    },
]

print(">>>>>> START")
check_thread_alive()

print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> create InfluxDBClient")
client = InfluxDBClient(
    url="localhost:8086",
    org="test",
    token="LLxHucOTfD1zSHVSadNRJRzZh_nNZGNf1KrHCieOj847ucB2RcLCBJZogP2zNtMxvAZMlAWsSOHgSJChaO7b3A==")
check_thread_alive()

print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> create write_api")
batch_write_api = client.write_api(
    write_options=WriteOptions(
        write_type=WriteType.batching))
check_thread_alive()

print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> close write_api")
batch_write_api.close()
batch_write_api.flush()
check_thread_alive()

print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> close InfluxDBClient")
client.close()
check_thread_alive()

print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> wait 5 s")
time.sleep(5)
check_thread_alive()
print(">>>>>> STOP")
Expected behavior

When we close the batching API, associated threads are closed

Actual behavior

When we close the batching API, the associated thread ThreadPoolExecutor is alway alive

Additional info

My output

>>>>>> START
1 threads running
 > MainThread
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> create InfluxDBClient
1 threads running
 > MainThread
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> create write_api
2 threads running
 > MainThread
 > ThreadPoolExecutor-1_0
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> close write_api
2 threads running
 > MainThread
 > ThreadPoolExecutor-1_0
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> close InfluxDBClient
2 threads running
 > MainThread
 > ThreadPoolExecutor-1_0
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> wait 5 s
2 threads running
 > MainThread
 > ThreadPoolExecutor-1_0
>>>>>> STOP
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