python multiprocessing
multiprocessing 在2.6才开始使用 multiprocessing 是一个使用方法类似threading模块的进程模块。允许程序员做并行开发。并且可以在UNIX和Windows下运行。 通过创建一个Process 类型并且通过调用call()方法spawn一个进程。 一个比较简单的例子： #!/usr/bin/env python from multiprocessing import Process import time def f(name): time.sleep(1) print 'hello ',name print os.getppid() #取得父进程ID print os.getpid() #取得进程ID process_list = [] if __name__ == '__main__': for i in range(10): p = Process(target=f,args=(i,)) p.start() process_list.append(p) for j in process_list: j.join() 进程间通信： 有两种主要的方式：Queue、Pipe 1- Queue类几乎就是Queue.Queue的复制，示例： #!/usr/bin/env python from multiprocessing import Process,Queue import time def f(name): time.sleep(1) q.put(['hello' str(name)]) process_list = [] q = Queue() if __name__ == '__main__': for i in range(10): p = Process(target=f,args=(i,)) p.start() process_list.append(p) for j in process_list: j.join() for i in range(10): print q.get() 2- Pipe 管道 #!/usr/bin/env python from multiprocessing import Process,Pipe import time import os def f(conn,name): time.sleep(1) conn.send(['hello' str(name)]) print os.getppid(),'-----------',os.getpid() process_list = [] parent_conn,child_conn = Pipe() if __name__ == '__main__': for i in range(10): p = Process(target=f,args=(child_conn,i)) p.start() process_list.append(p) for j in process_list: j.join() for p in range(10): print parent_conn.recv() Pipe()返回两个连接类，代表两个方向。如果两个进程在管道的两边同时读或同时写，会有可能造成corruption. 进程间同步 multiprocessing contains equivalents of all the synchronization primitives from threading. 例如，可以加一个锁，以使某一时刻只有一个进程print #!/usr/bin/env python from multiprocessing import Process,Lock import time import os def f(name): lock.acquire() time.sleep(1) print 'hello--' str(name) print os.getppid(),'-----------',os.getpid() lock.release() process_list = [] lock = Lock() if __name__ == '__main__': for i in range(10): p = Process(target=f,args=(i,)) p.start() process_list.append(p) for j in process_list: j.join() 进程间共享状态 Sharing state between processes 当然尽最大可能防止使用共享状态，但最终有可能会使用到. 1-共享内存 可以通过使用Value或者Array把数据存储在一个共享的内存表中 #!/usr/bin/env python from multiprocessing import Process,Value,Array import time import os def f(n,a,name): time.sleep(1) n.value = name * name for i in range(len(a)): a[i] = -i process_list = [] if __name__ == '__main__': num = Value('d',0.0) arr = Array('i',range(10)) for i in range(10): p = Process(target=f,args=(num,arr,i)) p.start() process_list.append(p) for j in process_list: j.join() print num.value print arr[:] 输出： jimin@Jimin:~/projects$ python pp.py 81.0 [0, -1, -2, -3, -4, -5, -6, -7, -8, -9] 'd'和'i'参数是num和arr用来设置类型，d表示一个双精浮点类型，i表示一个带符号的整型。 更加灵活的共享内存可以使用multiprocessing.sharectypes模块 Server process Manager()返回一个manager类型，控制一个server process，可以允许其它进程通过代理复制一些python objects 支持list,dict,Namespace,Lock,Semaphore,BoundedSemaphore,Condition,Event,Queue,Ｖalue,Array 例如： #!/usr/bin/env python from multiprocessing import Process,Manager import time import os def f(d,name): time.sleep(1) d[name] = name * name print d process_list = [] if __name__ == '__main__': manager = Manager() d = manager.dict() for i in range(10): p = Process(target=f,args=(d,i)) p.start() process_list.append(p) for j in process_list: j.join() print d 输出结果： {2: 4} {2: 4, 3: 9} {2: 4, 3: 9, 4: 16} {1: 1, 2: 4, 3: 9, 4: 16} {1: 1, 2: 4, 3: 9, 4: 16, 5: 25} {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25} {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36} {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 8: 64} {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64} {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81} {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81} Server process managers比共享内存方法更加的灵活，一个单独的manager可以被同一网络的不同计算机的多个进程共享。 比共享内存更加的缓慢 使用工作池 Using a pool of workers Pool类代表 a pool of worker processes. It has methods which allows tasks to be offloaded to the worker processes in a few different ways.
