Python多进程通信Queue、Pipe、Value、Array实例

queue和pipe的区别： pipe用来在两个进程间通信。queue用来在多个进程间实现通信。 此两种方法为所有系统多进程通信的基本方法，几乎所有的语言都支持此两种方法。

1）Queue & JoinableQueue

queue用来在进程间传递消息，任何可以pickle-able的对象都可以在加入到queue。

multiprocessing.JoinableQueue 是 Queue的子类，增加了task_done()和join()方法。

task_done()用来告诉queue一个task完成。一般地在调用get()获得一个task，在task结束后调用task_done()来通知Queue当前task完成。

join() 阻塞直到queue中的所有的task都被处理（即task_done方法被调用）。

代码：

import multiprocessing

import time
class Consumer(multiprocessing.Process):

    

    def __init__(self, task_queue, result_queue):

        multiprocessing.Process.__init__(self)

        self.task_queue = task_queue

        self.result_queue = result_queue
    def run(self):

        proc_name = self.name

        while True:

            next_task = self.task_queue.get()

            if next_task is None:

                # Poison pill means shutdown

                print ('%s: Exiting' % proc_name)

                self.task_queue.task_done()

                break

            print ('%s: %s' % (proc_name, next_task))

            answer = next_task() # __call__()

            self.task_queue.task_done()

            self.result_queue.put(answer)

        return
class Task(object):

    def __init__(self, a, b):

        self.a = a

        self.b = b

    def __call__(self):

        time.sleep(0.1) # pretend to take some time to do the work

        return '%s * %s = %s' % (self.a, self.b, self.a * self.b)

    def __str__(self):

        return '%s * %s' % (self.a, self.b)
if __name__ == '__main__':

    # Establish communication queues

    tasks = multiprocessing.JoinableQueue()

    results = multiprocessing.Queue()

    

    # Start consumers

    num_consumers = multiprocessing.cpu_count()

    print ('Creating %d consumers' % num_consumers)

    consumers = [ Consumer(tasks, results)

                  for i in range(num_consumers) ]

    for w in consumers:

        w.start()

    

    # Enqueue jobs

    num_jobs = 10

    for i in range(num_jobs):

        tasks.put(Task(i, i))

    

    # Add a poison pill for each consumer

    for i in range(num_consumers):

        tasks.put(None)
    # Wait for all of the tasks to finish

    tasks.join()

    

    # Start printing results

    while num_jobs:

        result = results.get()

        print ('Result:', result)

        num_jobs -= 1

注意小技巧： 使用None来表示task处理完毕。

运行结果：


2）pipe

pipe()返回一对连接对象，代表了pipe的两端。每个对象都有send()和recv()方法。

代码：

from multiprocessing import Process, Pipe
def f(conn):

    conn.send([42, None, 'hello'])

    conn.close()
if __name__ == '__main__':

    parent_conn, child_conn = Pipe()

    p = Process(target=f, args=(child_conn,))

    p.start()

    p.join()

    print(parent_conn.recv())   # prints "[42, None, 'hello']"

3）Value + Array

Value + Array 是python中共享内存 映射文件的方法，速度比较快。

from multiprocessing import Process, Value, Array
def f(n, a):

    n.value = n.value + 1

    for i in range(len(a)):

        a[i] = a[i] * 10
if __name__ == '__main__':

    num = Value('i', 1)

    arr = Array('i', range(10))
    p = Process(target=f, args=(num, arr))

    p.start()

    p.join()
    print(num.value)

    print(arr[:])

    

    p2 = Process(target=f, args=(num, arr))

    p2.start()

    p2.join()
    print(num.value)

    print(arr[:])
# the output is :

# 2

# [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]

# 3

# [0, 100, 200, 300, 400, 500, 600, 700, 800, 900]