python类库32[多进程通信Queue+Pipe+Value+Array]

多进程通信

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]

参考：
The Python Standard Library By Example

http://www.doughellmann.com/PyMOTW/multiprocessing/communication.html 

 

完！