Home Backend Development Python Tutorial 初步解析Python下的多进程编程

初步解析Python下的多进程编程

Jun 10, 2016 pm 03:14 PM
python

要让Python程序实现多进程(multiprocessing),我们先了解操作系统的相关知识。

Unix/Linux操作系统提供了一个fork()系统调用,它非常特殊。普通的函数调用,调用一次,返回一次,但是fork()调用一次,返回两次,因为操作系统自动把当前进程(称为父进程)复制了一份(称为子进程),然后,分别在父进程和子进程内返回。

子进程永远返回0,而父进程返回子进程的ID。这样做的理由是,一个父进程可以fork出很多子进程,所以,父进程要记下每个子进程的ID,而子进程只需要调用getppid()就可以拿到父进程的ID。

Python的os模块封装了常见的系统调用,其中就包括fork,可以在Python程序中轻松创建子进程:

# multiprocessing.py
import os

print 'Process (%s) start...' % os.getpid()
pid = os.fork()
if pid==0:
  print 'I am child process (%s) and my parent is %s.' % (os.getpid(), os.getppid())
else:
  print 'I (%s) just created a child process (%s).' % (os.getpid(), pid)

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运行结果如下:

Process (876) start...
I (876) just created a child process (877).
I am child process (877) and my parent is 876.

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由于Windows没有fork调用,上面的代码在Windows上无法运行。由于Mac系统是基于BSD(Unix的一种)内核,所以,在Mac下运行是没有问题的,推荐大家用Mac学Python!

有了fork调用,一个进程在接到新任务时就可以复制出一个子进程来处理新任务,常见的Apache服务器就是由父进程监听端口,每当有新的http请求时,就fork出子进程来处理新的http请求。
multiprocessing

如果你打算编写多进程的服务程序,Unix/Linux无疑是正确的选择。由于Windows没有fork调用,难道在Windows上无法用Python编写多进程的程序?

由于Python是跨平台的,自然也应该提供一个跨平台的多进程支持。multiprocessing模块就是跨平台版本的多进程模块。

multiprocessing模块提供了一个Process类来代表一个进程对象,下面的例子演示了启动一个子进程并等待其结束:

from multiprocessing import Process
import os

# 子进程要执行的代码
def run_proc(name):
  print 'Run child process %s (%s)...' % (name, os.getpid())

if __name__=='__main__':
  print 'Parent process %s.' % os.getpid()
  p = Process(target=run_proc, args=('test',))
  print 'Process will start.'
  p.start()
  p.join()
  print 'Process end.'

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执行结果如下:

Parent process 928.
Process will start.
Run child process test (929)...
Process end.

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创建子进程时,只需要传入一个执行函数和函数的参数,创建一个Process实例,用start()方法启动,这样创建进程比fork()还要简单。

join()方法可以等待子进程结束后再继续往下运行,通常用于进程间的同步。
Pool

如果要启动大量的子进程,可以用进程池的方式批量创建子进程:

from multiprocessing import Pool
import os, time, random

def long_time_task(name):
  print 'Run task %s (%s)...' % (name, os.getpid())
  start = time.time()
  time.sleep(random.random() * 3)
  end = time.time()
  print 'Task %s runs %0.2f seconds.' % (name, (end - start))

if __name__=='__main__':
  print 'Parent process %s.' % os.getpid()
  p = Pool()
  for i in range(5):
    p.apply_async(long_time_task, args=(i,))
  print 'Waiting for all subprocesses done...'
  p.close()
  p.join()
  print 'All subprocesses done.'

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执行结果如下:

Parent process 669.
Waiting for all subprocesses done...
Run task 0 (671)...
Run task 1 (672)...
Run task 2 (673)...
Run task 3 (674)...
Task 2 runs 0.14 seconds.
Run task 4 (673)...
Task 1 runs 0.27 seconds.
Task 3 runs 0.86 seconds.
Task 0 runs 1.41 seconds.
Task 4 runs 1.91 seconds.
All subprocesses done.

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代码解读:

对Pool对象调用join()方法会等待所有子进程执行完毕,调用join()之前必须先调用close(),调用close()之后就不能继续添加新的Process了。

请注意输出的结果,task 0,1,2,3是立刻执行的,而task 4要等待前面某个task完成后才执行,这是因为Pool的默认大小在我的电脑上是4,因此,最多同时执行4个进程。这是Pool有意设计的限制,并不是操作系统的限制。如果改成:

p = Pool(5)

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就可以同时跑5个进程。

由于Pool的默认大小是CPU的核数,如果你不幸拥有8核CPU,你要提交至少9个子进程才能看到上面的等待效果。
进程间通信

Process之间肯定是需要通信的,操作系统提供了很多机制来实现进程间的通信。Python的multiprocessing模块包装了底层的机制,提供了Queue、Pipes等多种方式来交换数据。

我们以Queue为例,在父进程中创建两个子进程,一个往Queue里写数据,一个从Queue里读数据:

from multiprocessing import Process, Queue
import os, time, random

# 写数据进程执行的代码:
def write(q):
  for value in ['A', 'B', 'C']:
    print 'Put %s to queue...' % value
    q.put(value)
    time.sleep(random.random())

# 读数据进程执行的代码:
def read(q):
  while True:
    value = q.get(True)
    print 'Get %s from queue.' % value

if __name__=='__main__':
  # 父进程创建Queue,并传给各个子进程:
  q = Queue()
  pw = Process(target=write, args=(q,))
  pr = Process(target=read, args=(q,))
  # 启动子进程pw,写入:
  pw.start()
  # 启动子进程pr,读取:
  pr.start()
  # 等待pw结束:
  pw.join()
  # pr进程里是死循环,无法等待其结束,只能强行终止:
  pr.terminate()

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运行结果如下:

Put A to queue...
Get A from queue.
Put B to queue...
Get B from queue.
Put C to queue...
Get C from queue.

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在Unix/Linux下,multiprocessing模块封装了fork()调用,使我们不需要关注fork()的细节。由于Windows没有fork调用,因此,multiprocessing需要“模拟”出fork的效果,父进程所有Python对象都必须通过pickle序列化再传到子进程去,所有,如果multiprocessing在Windows下调用失败了,要先考虑是不是pickle失败了。
小结

在Unix/Linux下,可以使用fork()调用实现多进程。

要实现跨平台的多进程,可以使用multiprocessing模块。

进程间通信是通过Queue、Pipes等实现的。

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