本文实例讲述了Python多进程机制。分享给大家供大家参考。具体如下：

在以前只是接触过PYTHON的多线程机制，今天搜了一下多进程，相关文章好像不是特别多。看了几篇，小试了一把。程序如下，主要内容就是通过PRODUCER读一个本地文件，一行一行的放到队列中去。然后会有相应的WORKER从队列中取出这些行。

import multiprocessing

import os

import sys

import Queue

import time

def writeQ(q,obj):

q.put(obj,True,None)

print "put size: ",q.qsize()

def readQ(q):

ret = q.get(True,1)

print "get size: ",q.qsize()

return ret

def producer(q):

time.sleep(5) #让进行休息几秒 方便ps命令看到相关内容

pid = os.getpid()

handle_file = '/home/dwapp/joe.wangh/test/multiprocess/datafile'

with open(handle_file,'r') as f: #with...as... 这个用法今天也是第一次看到的

for line in f:

print "producer <" ,pid , "> is doing: ",line

writeQ(q,line.strip())

q.close()

def worker(q):

time.sleep(5) #让进行休息几秒 方便ps命令看到相关内容

pid = os.getpid()

empty_count = 0

while True:

try:

task = readQ(q)

print "worker <" , pid , "> is doing: " ,task

'''

如果这里不休眠的话 一般情况下所有行都会被同一个子进程读取到 为了使实验效果更加清楚 在这里让每个进程读取完

一行内容时候休眠5s 这样就可以让其他的进程到队列中进行读取

'''

time.sleep(5)

except Queue.Empty:

empty_count += 1

if empty_count == 3:

print "queue is empty, quit"

q.close()

sys.exit(0)

def main():

concurrence = 3

q = multiprocessing.Queue(10)

funcs = [producer , worker]

for i in range(concurrence-1):

funcs.append(worker)

for item in funcs:

print str(item)

nfuncs = range( len(funcs) )

processes = []

for i in nfuncs:

p = multiprocessing.Process(target=funcs[i] , args=(q,))

processes.append(p)

print "concurrence worker is : ",concurrence," working start"

for i in nfuncs:

processes[i].start()

for i in nfuncs:

processes[i].join()

print "all DONE"

if __name__ == '__main__':

main()

实验结果如下：

dwapp@pttest1:/home/dwapp/joe.wangh/test/multiprocess>python 1.py

<function producer at 0xb7b9141c>

<function worker at 0xb7b91454>

<function worker at 0xb7b91454>

<function worker at 0xb7b91454>

concurrence worker is : 3 working start

producer < 28320 > is doing: line 1

put size: 1

producer < 28320 > is doing: line 2

put size: 2

producer < 28320 > is doing: line 3

put size: 3

producer < 28320 > is doing: line 4

put size: 3

producer < 28320 > is doing: line 5

get size: 3

put size: 4

worker < 28321 > is doing: line 1

get size: 3

worker < 28322 > is doing: line 2

get size: 2

worker < 28323 > is doing: line 3

get size: 1

worker < 28321 > is doing: line 4

get size: 0

worker < 28322 > is doing: line 5

queue is empty, quit

queue is empty, quit

queue is empty, quit

all DONE

程序运行期间在另外一个窗口进行ps命令 可以观测到一些进程的信息

dwapp@pttest1:/home/dwapp/joe.wangh/test/multiprocess>ps -ef | grep python

dwapp 13735 11830 0 Nov20 pts/12 00:00:05 python

dwapp 28319 27481 8 14:04 pts/0 00:00:00 python 1.py

dwapp 28320 28319 0 14:04 pts/0 00:00:00 python 1.py

dwapp 28321 28319 0 14:04 pts/0 00:00:00 python 1.py

dwapp 28322 28319 0 14:04 pts/0 00:00:00 python 1.py

dwapp 28323 28319 0 14:04 pts/0 00:00:00 python 1.py

dwapp 28325 27849 0 14:04 pts/13 00:00:00 grep python

dwapp@pttest1:/home/dwapp/joe.wangh/test/multiprocess>ps -ef | grep python

dwapp 13735 11830 0 Nov20 pts/12 00:00:05 python #此时28320进程 也就是PRODUCER进程已经结束

dwapp 28319 27481 1 14:04 pts/0 00:00:00 python 1.py

dwapp 28321 28319 0 14:04 pts/0 00:00:00 python 1.py

dwapp 28322 28319 0 14:04 pts/0 00:00:00 python 1.py

dwapp 28323 28319 0 14:04 pts/0 00:00:00 python 1.py

dwapp 28328 27849 0 14:04 pts/13 00:00:00 grep python

dwapp@pttest1:/home/dwapp/joe.wangh/test/multiprocess>ps -ef | grep python

dwapp 13735 11830 0 Nov20 pts/12 00:00:05 python

dwapp 28319 27481 0 14:04 pts/0 00:00:00 python 1.py

dwapp 28321 28319 0 14:04 pts/0 00:00:00 python 1.py

dwapp 28322 28319 0 14:04 pts/0 00:00:00 python 1.py

dwapp 28323 28319 0 14:04 pts/0 00:00:00 [python] <defunct> #这里应该是代表28323进程（WORKER）已经运行结束了

dwapp 28331 27849 0 14:04 pts/13 00:00:00 grep python

dwapp@pttest1:/home/dwapp/joe.wangh/test/multiprocess>ps -ef | grep python

dwapp 13735 11830 0 Nov20 pts/12 00:00:05 python

dwapp 28337 27849 0 14:05 pts/13 00:00:00 grep python

希望本文所述对大家的Python程序设计有所帮助。