使用多处理队列、池和锁定的简单示例

Dead simple example of using Multiprocessing Queue, Pool and Locking(使用多处理队列、池和锁定的简单示例)

问题描述

我尝试在 http://docs.python.org/dev 阅读文档/library/multiprocessing.html 但我仍在为多处理队列、池和锁定而苦苦挣扎.现在我能够构建下面的示例.

I tried to read the documentation at http://docs.python.org/dev/library/multiprocessing.html but I'm still struggling with multiprocessing Queue, Pool and Locking. And for now I was able to build the example below.

关于队列和池，我不确定我是否以正确的方式理解了这个概念，如果我错了，请纠正我.我想要实现的是一次处理 2 个请求(本例中数据列表有 8 个)那么，我应该使用什么?池来创建可以处理两个不同队列(最多 2 个)的 2 个进程，还是我应该每次只使用 Queue 来处理 2 个输入?锁定将正确打印输出.

Regarding Queue and Pool, I'm not sure if I understood the concept in the right way, so correct me if I'm wrong. What I'm trying to achieve is to process 2 requests at time ( data list have 8 in this example ) so, what should I use? Pool to create 2 processes that can handle two different queues ( 2 at max ) or should I just use Queue to process 2 inputs each time? The lock would be to print the outputs correctly.

import multiprocessing
import time

data = (['a', '2'], ['b', '4'], ['c', '6'], ['d', '8'],
        ['e', '1'], ['f', '3'], ['g', '5'], ['h', '7']
)


def mp_handler(var1):
    for indata in var1:
        p = multiprocessing.Process(target=mp_worker, args=(indata[0], indata[1]))
        p.start()


def mp_worker(inputs, the_time):
    print " Processs %s	Waiting %s seconds" % (inputs, the_time)
    time.sleep(int(the_time))
    print " Process %s	DONE" % inputs

if __name__ == '__main__':
    mp_handler(data)

推荐答案

解决问题的最佳方法是利用 Pool.使用 Queue 并拥有单独的队列馈送"功能可能是矫枉过正.

The best solution for your problem is to utilize a Pool. Using Queues and having a separate "queue feeding" functionality is probably overkill.

这是您的程序的一个稍微重新排列的版本，这一次 只有 2 个进程 位于 Pool 中.我相信这是最简单的方法，对原始代码的改动很小:

Here's a slightly rearranged version of your program, this time with only 2 processes coralled in a Pool. I believe it's the easiest way to go, with minimal changes to original code:

import multiprocessing
import time

data = (
    ['a', '2'], ['b', '4'], ['c', '6'], ['d', '8'],
    ['e', '1'], ['f', '3'], ['g', '5'], ['h', '7']
)

def mp_worker((inputs, the_time)):
    print " Processs %s	Waiting %s seconds" % (inputs, the_time)
    time.sleep(int(the_time))
    print " Process %s	DONE" % inputs

def mp_handler():
    p = multiprocessing.Pool(2)
    p.map(mp_worker, data)

if __name__ == '__main__':
    mp_handler()

请注意，mp_worker() 函数现在接受一个参数(前两个参数的元组)，因为 map() 函数将您的输入数据分块到子列表中，每个子列表作为一个参数提供给您的工作函数.

Note that mp_worker() function now accepts a single argument (a tuple of the two previous arguments) because the map() function chunks up your input data into sublists, each sublist given as a single argument to your worker function.

输出:

Processs a  Waiting 2 seconds
Processs b  Waiting 4 seconds
Process a   DONE
Processs c  Waiting 6 seconds
Process b   DONE
Processs d  Waiting 8 seconds
Process c   DONE
Processs e  Waiting 1 seconds
Process e   DONE
Processs f  Waiting 3 seconds
Process d   DONE
Processs g  Waiting 5 seconds
Process f   DONE
Processs h  Waiting 7 seconds
Process g   DONE
Process h   DONE

<小时>

根据下面的@Thales 评论进行

如果您想要为每个池限制锁定"，以便您的进程以串联对运行，ala:

If you want "a lock for each pool limit" so that your processes run in tandem pairs, ala:

A 等待 B 等待 |A 完成，B 完成 |C等待，D等待|C 完成，D 完成 |...

A waiting B waiting | A done , B done | C waiting , D waiting | C done, D done | ...

然后将处理函数更改为为每对数据启动池(2 个进程):

then change the handler function to launch pools (of 2 processes) for each pair of data:

def mp_handler():
    subdata = zip(data[0::2], data[1::2])
    for task1, task2 in subdata:
        p = multiprocessing.Pool(2)
        p.map(mp_worker, (task1, task2))

现在你的输出是:

 Processs a Waiting 2 seconds
 Processs b Waiting 4 seconds
 Process a  DONE
 Process b  DONE
 Processs c Waiting 6 seconds
 Processs d Waiting 8 seconds
 Process c  DONE
 Process d  DONE
 Processs e Waiting 1 seconds
 Processs f Waiting 3 seconds
 Process e  DONE
 Process f  DONE
 Processs g Waiting 5 seconds
 Processs h Waiting 7 seconds
 Process g  DONE
 Process h  DONE

这篇关于使用多处理队列、池和锁定的简单示例的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持编程学习网！