有些情况下，所要完成的工作可以分解并独立地分布到多个工作进程，对于这种简单的情况，可以用Pool类来管理固定数目的工作进程。作业的返回值会收集并作为一个列表返回。（以下程序cpu数量为2，相关函数解释见python 进程池2 - Pool相关函数）。
1 import multiprocessing
2
3 def do_calculation(data):
4 return data*2
5 def start_process():
6 print 'Starting',multiprocessing.current_process().name
7
8 if __name__=='__main__':
9 inputs=list(range(10))
10 print 'Inputs :',inputs
11
12 builtin_output=map(do_calculation,inputs)
13 print 'Build-In :', builtin_output
14
15 pool_size=multiprocessing.cpu_count()*2
16 pool=multiprocessing.Pool(processes=pool_size,
17 initializer=start_process,)
18
19 pool_outputs=pool.map(do_calculation,inputs)
20 pool.close()
21 pool.join()
22
23 print 'Pool :',pool_outputs
运行结果：
1 Inputs : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
2 Build-In : [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
3 Starting PoolWorker-2
4 Starting PoolWorker-1
5 Starting PoolWorker-3
6 Starting PoolWorker-4
7 Pool : [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
默认情况下，Pool会创建固定数目的工作进程，并向这些工作进程传递作业，直到再没有更多作业为止。maxtasksperchild参数为每个进程执行task的最大数目，设置maxtasksperchild参数可以告诉池在完成一定数量任务之后重新启动一个工作进程，来避免运行时间很长的工作进程消耗太多的系统资源。
maxtasksperchild is the number of tasks a worker process can complete before it will exit and be replaced with a fresh worker process, to enable unused resources to be freed. The default maxtasksperchild is None, which means worker processes will live as long as the pool.
Worker processes within a Pool typically live for the complete duration of the Pool’s work queue. A frequent pattern found in other systems (such as Apache, mod_wsgi, etc) to free resources held by workers is to allow a worker within a pool to complete only a set amount of work before being exiting, being cleaned up and a new process spawned to replace the old one. The maxtasksperchild argument to the Pool exposes this ability to the end user.
notice：
python 2.6.6
multiprocessing.Pool没有maxtaskperchild参数，Pool(processes=None, initializer=None, initargs=())
python 2.7.3
Pool(processes=None, initializer=None, initargs=(), maxtasksperchild=None)
1 import multiprocessing
2
3 def do_calculation(data):
4 return data*2
5 def start_process():
6 print 'Starting',multiprocessing.current_process().name
7
8 if __name__=='__main__':
9 inputs=list(range(10))
10 print 'Inputs :',inputs
11
12 builtin_output=map(do_calculation,inputs)
13 print 'Build-In :', builtin_output
14
15 pool_size=multiprocessing.cpu_count()*2
16 pool=multiprocessing.Pool(processes=pool_size,
17 initializer=start_process,maxtasksperchild=2)
18
19 pool_outputs=pool.map(do_calculation,inputs)
20 pool.close()
21 pool.join()
22
23 print 'Pool :',pool_outputs
运行结果：
1 Inputs : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
2 Build-In : [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
3 Starting PoolWorker-1
4 Starting PoolWorker-2
5 Starting PoolWorker-3
6 Starting PoolWorker-4
7 Starting PoolWorker-5
8 Starting PoolWorker-6
9 Starting PoolWorker-7
10 Starting PoolWorker-8
11 Pool : [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
池完成其所分配的任务时，即使没有更多的工作要做，也会重新启动工作进程。从这个输出可以看到，尽管只有10个任务，而且每个工作进程一次可以完成两个任务，但是这里创建了8个工作进程。
更多的时候，我们不仅需要多进程执行，还需要关注每个进程的执行结果。
1 import multiprocessing
2 import time
3
4 def func(msg):
5 for i in xrange(3):
6 print msg
7 time.sleep(1)
8 return "done " + msg
9
10 if __name__ == "__main__":
11 pool = multiprocessing.Pool(processes=4)
12 result = []
13 for i in xrange(10):
14 msg = "hello %d" %(i)
15 result.append(pool.apply_async(func, (msg, )))
16 pool.close()
17 pool.join()
18 for res in result:
19 print res.get()
20 print "Sub-process(es) done."
参考：
《Python 标准库》 10.4.17 进程池(p445)
http://www.coder4.com/archives/3352
原文：http://www.cnblogs.com/congbo/archive/2012/08/23/2652433.html
转载请注明本文地址： python 进程池1 - Pool使用简介