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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()
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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</div>
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</div>
希望本文所述对大家的Python程序设计有所帮助。
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