Python multi-process concurrency (multiprocessing)

A Manager returned by manager () would support types list, dict, Namespace, Lock, Rlock, Semaphore, Boundedsemaphore, Condit Ion, 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

would 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."

Use pool to focus on results

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 ()

Create a process pool with a capacity of 3, and then pass F (i) to it in turn, using PS aux after running the script | grep pool.py looks at the process and finds that only three processes are executing. Pool.apply_async () is used to submit a target request to the process pool, pool.join () is used to wait for the worker process in the process pool to complete, preventing the main process from ending before the worker process ends. However, the Pool.join () must be used after pool.close () or pool.terminate (). The difference between close () and terminate () is that close () waits for the worker process in the pool to end and then close the pool, while terminate () is closed directly. Result.successful () indicates the state of the entire invocation execution, and throws a Assertionerror exception if there are still workers that have not finished executing.

This article is from the "Big Barren Sutra" blog, please be sure to keep this source http://2892931976.blog.51cto.com/5396534/1761762

Python multi-process concurrency (multiprocessing)