• 多进程Process


    多进程旧式写法
    from multiprocessing import Pool
    def f(x):
        return x*x
    
    if __name__ == '__main__':
        p = Pool(5)
        print(p.map(f,[1,2,3]))
    

      

    多进程新式写法
    from multiprocessing import Process
    def run(num):
        print 'this is ',num
    
    for i in range(10):
        t = Process(target=run,args=(i,))
        t.start()
    

      

    多进程-父子进程

    from multiprocessing import Process
    import os
    def info(title):
            print title
            print 'module name:',__name__
            if hasattr(os,'getppid'):
                    print 'parent process:',os.getppid()    #获取父进程PID
    print 'process id:',os.getpid()                 #获取子进程PID
    def f(name):
            info('function f')  #子进程调用info函数
    print 'hello',name
    if __name__ == '__main__':
            info('main line')   #父进程调用info函数
    print '---------------'
    p = Process(target=f,args=('bob',))
            p.start()
            p.join()
    

      

    进程数据共享

    from multiprocessing import Process
    li = []
    def run(num):
        li.append(num)
        print 'say hi',li
    
    for i in range(10):
        t = Process(target=run,args=(i,))
        t.start()
    
    print 'ending',li
    

      

    进程各自持有一份数据,默认无法共享数据,要使进程间可以共享数据,则

    #方法一,Array
    from multiprocessing import Process,Array
    #创建一个只包含数字类型的一个数组/列表,并且个数不可变
    temp = Array('i', [11,22,33,44])
    
    def Foo(i):
        temp[i] = 100+i
        for item in temp:
            print i,'----->',item
    
    for i in range(2):
        p = Process(target=Foo,args=(i,))
        p.start()
    

      

    #方法二:manage.dict()共享数据
    from multiprocessing import Process,Manager
    manage = Manager()
    dic = manage.dict()
    def Foo(i):
        dic[i] = 100+i
        print dic.values()
    for i in range(2):
        p = Process(target=Foo,args=(i,))
        p.start()
        p.join()
    

      

    #方法3Queue
    from multiprocessing import Process,Queue
    def f(q,n):
        q.put([n,'hello'])
    if __name__ == '__main__':
        q = Queue()
        for i in range(5):
            p = Process(target=f,args=(q,i))
            p.start()
        while True:
            print q.get()
    

      

    #方法4,Manager
    from multiprocessing import Process,Manager
    def f(d,l):
        d[1] = '1'
    d['2'] = 2
    d[0.25] = None
    l.reverse()     #反向列表
    if __name__ == '__main__':
        manager = Manager()
        d = manager.dict()
        #d = {}
    l = manager.list(range(10))
        #l = [0,1,2,4,5,6,7,8,9]
    p = Process(target=f,args=(d,l))
        p.start()
        p.join()
        print d
        print l
    

      

    
    
    进程锁
    from multiprocessing import Process, Array, RLock
    def Foo(lock,temp,i):
        """
    将第0个数加100
        """
    lock.acquire()      #加锁
    temp[0] = 100+i
        for item in temp:
            print i,'----->',item
        lock.release()      #解锁
    
    lock = RLock()
    temp = Array('i', [11, 22, 33, 44])
    for i in range(20):
        p = Process(target=Foo,args=(lock,temp,i,))
        p.start()
    

      

    进程池

    进程池内部维护一个进程序列,当使用时,则去进程池中获取一个进程,如果进程池序列中没有可供使用的进进程,那么程序就会等待,直到进程池中有可用进程为止。

    进程池中有两个方法:

    • apply
    • apply_async
    from  multiprocessing import Process,Pool
    import time
    
    def Foo(i):
        time.sleep(2)
        return i+100
    
    def Bar(arg):
        print arg
    
    pool = Pool(5)
    #print pool.apply(Foo,(1,))
    #print pool.apply_async(func =Foo, args=(1,)).get()
    for i in range(10):
        pool.apply_async(func=Foo, args=(i,),callback=Bar)
    
    print 'end'
    pool.close()
    pool.join()#进程池中进程执行完毕后再关闭,如果注释,那么程序直接关闭。
    

      

    from multiprocessing import Pool
    import time
    def f(x):
        print x*x
        #time.sleep(2)
    return x*x
    
    if __name__ == '__main__':
        pool = Pool(processes=2)   #同时5个进程
    res_list = []
        for i in range(5):
            res = pool.apply_async(f,[i,])
            #res =  Process(target=f,args=[i,])
    print '-----------',i
            res_list.append(res)
        for r in res_list:
            print 'res_list:',r.get()
    

      


     

     

    协程

    线程和进程的操作是由程序触发系统接口,最后的执行者是系统;协程的操作则是程序员。

    协程存在的意义:对于多线程应用,CPU通过切片的方式来切换线程间的执行,线程切换时需要耗时(保存状态,下次继续)。协程,则只使用一个线程,在一个线程中规定某个代码块执行顺序。

    协程的适用场景:当程序中存在大量不需要CPU的操作时(IO),适用于协程;

    greenlet

     

    #!/usr/bin/env python
    # -*- coding:utf-8 -*-
     
     
    from greenlet import greenlet
     
     
    def test1():
        print 12
        gr2.switch()
        print 34
        gr2.switch()
     
     
    def test2():
        print 56
        gr1.switch()
        print 78
     
    gr1 = greenlet(test1)
    gr2 = greenlet(test2)
    gr1.switch()
    

     

      

     

    gevent

    import gevent
     
    def foo():
        print('Running in foo')
        gevent.sleep(0)
        print('Explicit context switch to foo again')
     
    def bar():
        print('Explicit context to bar')
        gevent.sleep(0)
        print('Implicit context switch back to bar')
     
    gevent.joinall([
        gevent.spawn(foo),
        gevent.spawn(bar),
    ])
    

      

    遇到IO操作自动切换:

     

    from gevent import monkey; monkey.patch_all()
    import gevent
    import urllib2
    
    def f(url):
        print('GET: %s' % url)
        resp = urllib2.urlopen(url)
        data = resp.read()
        print('%d bytes received from %s.' % (len(data), url))
    
    gevent.joinall([
            gevent.spawn(f, 'https://www.python.org/'),
            gevent.spawn(f, 'https://www.yahoo.com/'),
            gevent.spawn(f, 'https://github.com/'),
    ])
    

     

      

     

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  • 原文地址:https://www.cnblogs.com/yangmv/p/5169909.html
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