Python多进程并发(multiprocessing)用法实例详解
本文实例讲述了Python多进程并发(multiprocessing)用法。分享给大家供大家参考。具体分析如下:
由于Python设计的限制(我说的是咱们常用的CPython)。最多只能用满1个CPU核心。
Python提供了非常好用的多进程包multiprocessing,你只需要定义一个函数,Python会替你完成其他所有事情。借助这个包,可以轻松完成从单进程到并发执行的转换。
1、新建单一进程
如果我们新建少量进程,可以如下:
import multiprocessing import time def func(msg): for i in xrange(3): print msg time.sleep(1) if __name__ == "__main__": p = multiprocessing.Process(target=func, args=("hello", )) p.start() p.join() print "Sub-process done."
2、使用进程池
是的,你没有看错,不是线程池。它可以让你跑满多核CPU,而且使用方法非常简单。
注意要用apply_async,如果落下async,就变成阻塞版本了。
processes=4是最多并发进程数量。
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."
3、使用Pool,并需要关注结果
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,如下:
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."
希望本文所述对大家的Python程序设计有所帮助。

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