Python中用Ctrl+C终止多线程程序的问题解决
#!/bin/env python
# -*- coding: utf-8 -*-
#filename: peartest.py
import threading, signal
is_exit = False
def doStress(i, cc):
global is_exit
idx = i
while not is_exit:
if (idx print "thread[%d]: idx=%d"%(i, idx)
idx = idx + cc
else:
break
print "thread[%d] complete."%i
def handler(signum, frame):
global is_exit
is_exit = True
print "receive a signal %d, is_exit = %d"%(signum, is_exit)
if __name__ == "__main__":
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
cc = 5
for i in range(cc):
t = threading.Thread(target=doStress, args=(i,cc))
t.start()
上面是一个模拟程序,并不真正向服务发送请求,而代之以在一千万以内,每个线程每隔并发数个(cc个)打印一个整数。很明显,当所有线程都完成自己的任务后,进程会正常退出。但如果我们中途想退出(试想一个压力测试程序,在中途已经发现了问题,需要停止测试),该肿么办?你当然可以用ps查找到进程号,然后kill -9杀掉,但这样太繁琐了,捕捉Ctrl+C是最自然的想法。上面示例程序中已经捕捉了这个信号,并修改全局变量is_exit,线程中会检测这个变量,及时退出。
但事实上这个程序并不work,当你按下Ctrl+C时,程序照常运行,并无任何响应。网上搜了一些资料,明白是python的子线程如果不是daemon的话,主线程是不能响应任何中断的。但设为daemon后主线程会随之退出,接着整个进程很快就退出了,所以还需要在主线程中检测各个子线程的状态,直到所有子线程退出后自己才退出,因此上例29行之后的代码可以修改为:
threads=[]
for i in range(cc):
t = threading.Thread(target=doStress, args=(i, cc))
t.setDaemon(True)
threads.append(t)
t.start()
for i in range(cc):
threads[i].join()
重新试一下,问题依然没有解决,进程还是没有响应Ctrl+C,这是因为join()函数同样会waiting在一个锁上,使主线程无法捕获信号。因此继续修改,调用线程的isAlive()函数判断线程是否完成:
while 1:
alive = False
for i in range(cc):
alive = alive or threads[i].isAlive()
if not alive:
break
这样修改后,程序完全按照预想运行了:可以顺利的打印每个线程应该打印的所有数字,也可以中途用Ctrl+C终结整个进程。完整的代码如下:
#!/bin/env python
# -*- coding: utf-8 -*-
#filename: peartest.py
import threading, signal
is_exit = False
def doStress(i, cc):
global is_exit
idx = i
while not is_exit:
if (idx print "thread[%d]: idx=%d"%(i, idx)
idx = idx + cc
else:
break
if is_exit:
print "receive a signal to exit, thread[%d] stop."%i
else:
print "thread[%d] complete."%i
def handler(signum, frame):
global is_exit
is_exit = True
print "receive a signal %d, is_exit = %d"%(signum, is_exit)
if __name__ == "__main__":
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
cc = 5
threads = []
for i in range(cc):
t = threading.Thread(target=doStress, args=(i,cc))
t.setDaemon(True)
threads.append(t)
t.start()
while 1:
alive = False
for i in range(cc):
alive = alive or threads[i].isAlive()
if not alive:
break
其实,如果用python写一个服务,也需要这样,因为负责服务的那个线程是永远在那里接收请求的,不会退出,而如果你想用Ctrl+C杀死整个服务,跟上面的压力测试程序是一个道理。总结一下,python多线程中要响应Ctrl+C的信号以杀死整个进程,需要:
1.把所有子线程设为Daemon;
2.使用isAlive()函数判断所有子线程是否完成,而不是在主线程中用join()函数等待完成;
3.写一个响应Ctrl+C信号的函数,修改全局变量,使得各子线程能够检测到,并正常退出。

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



Solution to permission issues when viewing Python version in Linux terminal When you try to view Python version in Linux terminal, enter python...

When using Python's pandas library, how to copy whole columns between two DataFrames with different structures is a common problem. Suppose we have two Dats...

How to teach computer novice programming basics within 10 hours? If you only have 10 hours to teach computer novice some programming knowledge, what would you choose to teach...

How to avoid being detected when using FiddlerEverywhere for man-in-the-middle readings When you use FiddlerEverywhere...

Regular expressions are powerful tools for pattern matching and text manipulation in programming, enhancing efficiency in text processing across various applications.

How does Uvicorn continuously listen for HTTP requests? Uvicorn is a lightweight web server based on ASGI. One of its core functions is to listen for HTTP requests and proceed...

The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H

In Python, how to dynamically create an object through a string and call its methods? This is a common programming requirement, especially if it needs to be configured or run...
