Python自定义进程池实例分析【生产者、消费者模型问题】
本文实例分析了Python自定义进程池。分享给大家供大家参考，具体如下：
代码说明一切：
#encoding=utf-8
#author: walker
#date: 2014-05-21
#function: 自定义进程池遍历目录下文件
from multiprocessing import Process, Queue, Lock
import time, os
#消费者
class Consumer(Process):
def __init__(self, queue, ioLock):
super(Consumer, self).__init__()
self.queue = queue
self.ioLock = ioLock
def run(self):
while True:
task = self.queue.get() #队列中无任务时，会阻塞进程
if isinstance(task, str) and task == 'quit':
break;
time.sleep(1) #假定任务处理需要1秒钟
self.ioLock.acquire()
print( str(os.getpid()) + ' ' + task)
self.ioLock.release()
self.ioLock.acquire()
print 'Bye-bye'
self.ioLock.release()
#生产者
def Producer():
queue = Queue() #这个队列是进程/线程安全的
ioLock = Lock()
subNum = 4 #子进程数量
workers = build_worker_pool(queue, ioLock, subNum)
start_time = time.time()
for parent, dirnames, filenames in os.walk(r'D:\test'):
for filename in filenames:
queue.put(filename)
ioLock.acquire()
print('qsize:' + str(queue.qsize()))
ioLock.release()
while queue.qsize() > subNum * 10: #控制队列中任务数量
time.sleep(1)
for worker in workers:
queue.put('quit')
for worker in workers:
worker.join()
ioLock.acquire()
print('Done! Time taken: {}'.format(time.time() - start_time))
ioLock.release()
#创建进程池
def build_worker_pool(queue, ioLock, size):
workers = []
for _ in range(size):
worker = Consumer(queue, ioLock)
worker.start()
workers.append(worker)
return workers
if __name__ == '__main__':
Producer()
ps：
self.ioLock.acquire() ... self.ioLock.release()
可用：
with self.ioLock: ...
替代。
再来一个好玩的例子：
#encoding=utf-8
#author: walker
#date: 2016-01-06
#function: 一个多进程的好玩例子
import os, sys, time
from multiprocessing import Pool
cur_dir_fullpath = os.path.dirname(os.path.abspath(__file__))
g_List = ['a']
#修改全局变量g_List
def ModifyDict_1():
global g_List
g_List.append('b')
#修改全局变量g_List
def ModifyDict_2():
global g_List
g_List.append('c')
#处理一个
def ProcOne(num):
print('ProcOne ' + str(num) + ', g_List:' + repr(g_List))
#处理所有
def ProcAll():
pool = Pool(processes = 4)
for i in range(1, 20):
#ProcOne(i)
#pool.apply(ProcOne, (i,))
pool.apply_async(ProcOne, (i,))
pool.close()
pool.join()
ModifyDict_1() #修改全局变量g_List
if __name__ == '__main__':
ModifyDict_2() #修改全局变量g_List
print('In main g_List :' + repr(g_List))
ProcAll()
Windows7 下运行的结果：
λ python3 demo.py In main g_List :['a', 'b', 'c'] ProcOne 1, g_List:['a', 'b'] ProcOne 2, g_List:['a', 'b'] ProcOne 3, g_List:['a', 'b'] ProcOne 4, g_List:['a', 'b'] ProcOne 5, g_List:['a', 'b'] ProcOne 6, g_List:['a', 'b'] ProcOne 7, g_List:['a', 'b'] ProcOne 8, g_List:['a', 'b'] ProcOne 9, g_List:['a', 'b'] ProcOne 10, g_List:['a', 'b'] ProcOne 11, g_List:['a', 'b'] ProcOne 12, g_List:['a', 'b'] ProcOne 13, g_List:['a', 'b'] ProcOne 14, g_List:['a', 'b'] ProcOne 15, g_List:['a', 'b'] ProcOne 16, g_List:['a', 'b'] ProcOne 17, g_List:['a', 'b'] ProcOne 18, g_List:['a', 'b'] ProcOne 19, g_List:['a', 'b']
Ubuntu 14.04下运行的结果：
In main g_List :['a', 'b', 'c'] ProcOne 1, g_List:['a', 'b', 'c'] ProcOne 2, g_List:['a', 'b', 'c'] ProcOne 3, g_List:['a', 'b', 'c'] ProcOne 5, g_List:['a', 'b', 'c'] ProcOne 4, g_List:['a', 'b', 'c'] ProcOne 8, g_List:['a', 'b', 'c'] ProcOne 9, g_List:['a', 'b', 'c'] ProcOne 7, g_List:['a', 'b', 'c'] ProcOne 11, g_List:['a', 'b', 'c'] ProcOne 6, g_List:['a', 'b', 'c'] ProcOne 12, g_List:['a', 'b', 'c'] ProcOne 13, g_List:['a', 'b', 'c'] ProcOne 10, g_List:['a', 'b', 'c'] ProcOne 14, g_List:['a', 'b', 'c'] ProcOne 15, g_List:['a', 'b', 'c'] ProcOne 16, g_List:['a', 'b', 'c'] ProcOne 17, g_List:['a', 'b', 'c'] ProcOne 18, g_List:['a', 'b', 'c'] ProcOne 19, g_List:['a', 'b', 'c']
可以看见Windows7下第二次修改没有成功，而Ubuntu下修改成功了。据uliweb作者limodou讲，原因是Windows下是充重启实现的子进程；Linux下是fork实现的。
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