Python多進程併發(multiprocessing)


 A manager returned by Manager() will support types list, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore, Condition, Event, Queue, Value and Array. For example,


from multiprocessing import Process, Manager


def f(d, l):

    d[1] = '1'

    d['2'] = 2

    d[0.25] = None

    l.reverse()


if __name__ == '__main__':

    manager = Manager()


    d = manager.dict()

    l = manager.list(range(10))


    p = Process(target=f, args=(d, l))

    p.start()

    p.join()


    print d

    print l

will print


{0.25: None, 1: '1', '2': 2}

[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]



import multiprocessing

import time

def func(msg):

  for i in xrange(3):

    print msg

    time.sleep(1)

if __name__ == "__main__":

  pool = multiprocessing.Pool(processes=4)

  for i in xrange(10):

    msg = "hello %d" %(i)

    pool.apply_async(func, (msg, ))

  pool.close()

  pool.join()

  print "Sub-process(es) done."


使用Pool,關注結果


import multiprocessing

import time

def func(msg):

  for i in xrange(3):

    print msg

    time.sleep(1)

  return "done " + msg

if __name__ == "__main__":

  pool = multiprocessing.Pool(processes=4)

  result = []

  for i in xrange(10):

    msg = "hello %d" %(i)

    result.append(pool.apply_async(func, (msg, )))

  pool.close()

  pool.join()

  for res in result:

    print res.get()

  print "Sub-process(es) done."



#!/usr/bin/env python

#coding=utf-8

"""

Author: Squall

Last modified: 2011-10-18 16:50

Filename: pool.py

Description: a simple sample for pool class

"""


from multiprocessing import Pool

from time import sleep


def f(x):

    for i in range(10):

        print '%s --- %s ' % (i, x)

        #sleep(1)



def main():

    pool = Pool(processes=3)    # set the processes max number 3

    for i in range(11,20):

        result = pool.apply_async(f, (i,))

    pool.close()

    pool.join()

    if result.successful():

        print 'successful'



if __name__ == "__main__":

    main()


 先創建容量爲3的進程池,然後將f(i)依次傳遞給它,運行腳本後利用ps aux | grep pool.py查看進程情況,會發現最多隻會有三個進程執行。pool.apply_async()用來向進程池提交目標請求,pool.join()是用來等待進程池中的worker進程執行完畢,防止主進程在worker進程結束前結束。但必pool.join()必須使用在pool.close()或者pool.terminate()之後。其中close()跟terminate()的區別在於close()會等待池中的worker進程執行結束再關閉pool,而terminate()則是直接關閉。result.successful()表示整個調用執行的狀態,如果還有worker沒有執行完,則會拋出AssertionError異常。


 


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