多进程 (一) — 像线程同样管理进程

时间  2019-11-24
标签 进程 线程 同样 管理 繁體版
原文   原文链接

因为和线程类似，这里的前几个示例都是从线程示例中修改的。python

内容目录

  1. multiprocessing基础
  2. 可导入的目标函数
  3. 肯定当前进程
  4. 守护进程 Daemon
  5. 等待进程 join()
  6. 终止进程 terminate()
  7. 进程退出状态
  8. 调试 log_to_stderr
  9. 子类化进程

1.multiprocessing基础

生成进程的最简单的方法是用目标函数实例化一个进程对象，并调用start()来让它开始工做。bootstrap

import multiprocessing


def worker():
    """worker function"""
    print('Worker')


if __name__ == '__main__':
    jobs = []
    for i in range(5):
        p = multiprocessing.Process(target=worker)
        jobs.append(p)
        p.start()

运行结果：并发

Worker
Worker
Worker
Worker
Worker

传递参数：app

import multiprocessing


def worker(num):
    """thread worker function"""
    print('Worker:', num)


if __name__ == '__main__':
    jobs = []
    for i in range(5):
        p = multiprocessing.Process(target=worker, args=(i,))
        jobs.append(p)
        p.start()

运行结果：函数

Worker: 1
Worker: 0
Worker: 3
Worker: 2
Worker: 4

2.可导入的目标函数

# main.py
import multiprocessing
import multiprocessing_import_worker

if __name__ == '__main__':
    jobs = []
    for i in range(5):
        p = multiprocessing.Process(
            target=multiprocessing_import_worker.worker,
        )
        jobs.append(p)
        p.start()

其中worker()函数在multiprocessing_import_worker.py中定义：操作系统

# multiprocessing_import_worker.py
def worker():
    """worker function"""
    print('Worker')
    return

运行结果：线程

Worker
Worker
Worker
Worker
Worker

3.肯定当前进程

import multiprocessing
import time


def worker():
    name = multiprocessing.current_process().name
    print(name, 'Starting')
    time.sleep(2)
    print(name, 'Exiting')


def my_service():
    name = multiprocessing.current_process().name
    print(name, 'Starting')
    time.sleep(3)
    print(name, 'Exiting')


if __name__ == '__main__':
    service = multiprocessing.Process(
        name='my_service',
        target=my_service,
    )
    worker_1 = multiprocessing.Process(
        name='worker 1',
        target=worker,
    )
    worker_2 = multiprocessing.Process(  # default name
        target=worker,
    )

    worker_1.start()
    worker_2.start()
    service.start()

运行结果：能够看到默认的进程名字对应于Process-3, 和线程很相似。调试

worker 1 Starting
Process-3 Starting
my_service Starting
worker 1 Exiting
Process-3 Exiting
my_service Exiting

4.守护进程 Daemon

默认状况下主进程会在子进程所有执行完毕后才退出，若是子进程设置为守护进程，便再也不阻塞主进程退出。
为了将进程标记为守护进程只需将daemon属性设置为True。默认状况下，进程不是守护进程。日志

import multiprocessing
import time
import sys


def daemon():
    p = multiprocessing.current_process()
    print('Starting:', p.name, p.pid)
    sys.stdout.flush()
    time.sleep(2)
    print('Exiting :', p.name, p.pid)
    sys.stdout.flush()


def non_daemon():
    p = multiprocessing.current_process()
    print('Starting:', p.name, p.pid)
    sys.stdout.flush()
    print('Exiting :', p.name, p.pid)
    sys.stdout.flush()


if __name__ == '__main__':
    d = multiprocessing.Process(
        name='daemon',
        target=daemon,
    )
    d.daemon = True

    n = multiprocessing.Process(
        name='non-daemon',
        target=non_daemon,
    )
    n.daemon = False

    d.start()
    time.sleep(1)
    n.start()

运行结果：能够看到d进程还没执行完主进程就退出了。code

Starting: daemon 24852
Starting: non-daemon 23248
Exiting : non-daemon 23248

在主程序退出以前，守护进程会自动终止，这将避免留下孤儿进程。

5.等待进程 join()

要等到进程完成工做再退出，请使用join()方法。

import multiprocessing
import time
import sys


def daemon():
    name = multiprocessing.current_process().name
    print('Starting:', name)
    time.sleep(2)
    print('Exiting :', name)


def non_daemon():
    name = multiprocessing.current_process().name
    print('Starting:', name)
    print('Exiting :', name)


if __name__ == '__main__':
    d = multiprocessing.Process(
        name='daemon',
        target=daemon,
    )
    d.daemon = True

    n = multiprocessing.Process(
        name='non-daemon',
        target=non_daemon,
    )
    n.daemon = False

    d.start()
    time.sleep(1)
    n.start()

    d.join()
    n.join()

运行结果：

Starting: non-daemon
Exiting : non-daemon
Starting: daemon
Exiting : daemon

默认状况下，join()无限期阻塞。也能够使用超时参数。

6.终止进程 terminate()

在一个进程调用terminate()会杀死子进程

import multiprocessing
import time


def slow_worker():
    print('Starting worker')
    time.sleep(0.1)
    print('Finished worker')


if __name__ == '__main__':
    p = multiprocessing.Process(target=slow_worker)
    print('BEFORE:', p, p.is_alive())

    p.start()
    print('DURING:', p, p.is_alive())

    p.terminate()
    print('TERMINATED:', p, p.is_alive())

    p.join()
    print('JOINED:', p, p.is_alive())

运行结果：在终止它以后，使用join()是很重要的，以便让进程管理代码有时间来更新对象的状态以反映终止。

BEFORE: <Process(Process-1, initial)> False
DURING: <Process(Process-1, started)> True
TERMINATED: <Process(Process-1, started)> True
JOINED: <Process(Process-1, stopped[SIGTERM])> False

7.进程退出状态

当进程退出时产生的状态码能够经过exitcode属性访问。容许的范围列在下面的表格中。

Exit Code Meaning
== 0 row 1 col 2
> 0 the process had an error, and exited with that code
< 0 the process had an error, and exited with that code 
import multiprocessing
import sys
import time


def exit_error():
    sys.exit(1)


def exit_ok():
    return


def return_value():
    return 1


def raises():
    raise RuntimeError('There was an error!')


def terminated():
    time.sleep(3)


if __name__ == '__main__':
    jobs = []
    funcs = [
        exit_error,
        exit_ok,
        return_value,
        raises,
        terminated,
    ]
    for f in funcs:
        print('Starting process for', f.__name__)
        j = multiprocessing.Process(target=f, name=f.__name__)
        jobs.append(j)
        j.start()

    jobs[-1].terminate()

    for j in jobs:
        j.join()
        print('{:>15}.exitcode = {}'.format(j.name, j.exitcode))

运行结果：

Starting process for exit_error
Starting process for exit_ok
Starting process for return_value
Starting process for raises
Starting process for terminated
Process raises:
     exit_error.exitcode = 1
        exit_ok.exitcode = 0
Traceback (most recent call last):
  File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\multiprocessing\process.py", line 258, in _bootstrap
    self.run()
  File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\multiprocessing\process.py", line 93, in run
    self._target(*self._args, **self._kwargs)
  File "E:\MyPython\image.py", line 19, in raises
    raise RuntimeError('There was an error!')
RuntimeError: There was an error!
   return_value.exitcode = 0
         raises.exitcode = 1
     terminated.exitcode = -15

exit(0)：无错误退出
exit(1)：有错误退出
退出代码是告诉解释器的（或操做系统）

8.调试 log_to_stderr

在调试并发性问题时，能够使用multiprocessing所提供的对象的内部构件。有一个方便的模块级函数为logtostderr()。它使用logging设置一个记录器对象，并添加一个处理程序，以便将日志消息发送到标准错误通道。

import multiprocessing
import logging
import sys


def worker():
    print('Doing some work')
    sys.stdout.flush()


if __name__ == '__main__':
    multiprocessing.log_to_stderr(logging.DEBUG)
    p = multiprocessing.Process(target=worker)
    p.start()
    p.join()

运行结果：认状况下，logging级别被设置为NOTSET不会产生任何消息。

Doing some work
[INFO/Process-1] child process calling self.run()
[INFO/Process-1] process shutting down
[DEBUG/Process-1] running all "atexit" finalizers with priority >= 0
[DEBUG/Process-1] running the remaining "atexit" finalizers
[INFO/Process-1] process exiting with exitcode 0
[INFO/MainProcess] process shutting down
[DEBUG/MainProcess] running all "atexit" finalizers with priority >= 0
[DEBUG/MainProcess] running the remaining "atexit" finalizers

若想直接操做日志请使用get_logger()获取

import multiprocessing
import logging
import sys


def worker():
    print('Doing some work')
    sys.stdout.flush()


if __name__ == '__main__':
    multiprocessing.log_to_stderr()
    logger = multiprocessing.get_logger()
    logger.setLevel(logging.INFO)
    p = multiprocessing.Process(target=worker)
    p.start()
    p.join()

运行结果：

[INFO/Process-1] child process calling self.run()
Doing some work
[INFO/Process-1] process shutting down
[INFO/Process-1] process exiting with exitcode 0
[INFO/MainProcess] process shutting down

9.子类化进程

经过multiprocessing.Process能够建立进程，也能够经过自定义子类建立进程。

import multiprocessing


class Worker(multiprocessing.Process):

    def run(self):
        print('In {}'.format(self.name))
        return


if __name__ == '__main__':
    jobs = []
    for i in range(5):
        p = Worker()
        jobs.append(p)
        p.start()
    for j in jobs:
        j.join()

运行结果：

In Worker-2
In Worker-4
In Worker-3
In Worker-1
In Worker-5
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