使用多处理队列,池和锁定的简单示例

Question

我试着阅读http://docs.python.org/dev/library/multiprocessing.html上的文档,但我仍然在努力处理多处理队列,池和锁定.现在我能够构建下面的示例.

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,所以如果我错了,请纠正我.我想要实现的是在时间处理2个请求(在这个例子中数据列表有8个)所以,我应该使用什么？池创建2个进程,可以处理两个不同的队列(最多2个)或者我应该只使用Queue每次处理2个输入？锁定将正确打印输出.

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\tWaiting %s seconds" % (inputs, the_time)
    time.sleep(int(the_time))
    print " Process %s\tDONE" % inputs

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

Answer 1

解决问题的最佳方法是使用a Pool.使用Queues并具有单独的"队列馈送"功能可能是过度的.

这是你的程序的一个稍微重新安排的版本,这次只有2个进程在一个Pool.我认为这是最简单的方法,对原始代码的改动很小:

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\tWaiting %s seconds" % (inputs, the_time)
    time.sleep(int(the_time))
    print " Process %s\tDONE" % inputs

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

if __name__ == '__main__':
    mp_handler()

请注意,mp_worker()函数现在接受单个参数(前两个参数的元组),因为该map()函数将输入数据分块为子列表,每个子列表作为工作函数的单个参数给出.

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

根据@Thales评论编辑如下:

如果你想"锁定每个池限制",以便你的进程以串联方式运行,ala:

等待B等待| 完成,B完成| C等待,D等待| C完成,D完成| ...

然后将处理程序函数更改为为每对数据启动池(2个进程):

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))

现在您的输出是:

 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

Answer 2

这可能与问题没有100%的关联,但在我搜索使用队列的多处理的示例时,这首先出现在谷歌上.

这是一个基本的示例类,您可以实例化并将项目放入队列中,并且可以等到队列完成.这就是我所需要的一切.

from multiprocessing import JoinableQueue
from multiprocessing.context import Process


class Renderer:
    queue = None

    def __init__(self, nb_workers=2):
        self.queue = JoinableQueue()
        self.processes = [Process(target=self.upload) for i in range(nb_workers)]
        for p in self.processes:
            p.start()

    def render(self, item):
        self.queue.put(item)

    def upload(self):
        while True:
            item = self.queue.get()
            if item is None:
                break

            # process your item here

            self.queue.task_done()

    def terminate(self):
        """ wait until queue is empty and terminate processes """
        self.queue.join()
        for p in self.processes:
            p.terminate()

r = Renderer()
r.render(item1)
r.render(item2)
r.terminate()

Answer 3

以下是我对此主题的个人转到:

请点击这里,(拉请求欢迎!):https: //gist.github.com/thorsummoner/b5b1dfcff7e7fdd334ec

import multiprocessing
import sys

THREADS = 3

# Used to prevent multiple threads from mixing thier output
GLOBALLOCK = multiprocessing.Lock()


def func_worker(args):
    """This function will be called by each thread.
    This function can not be a class method.
    """
    # Expand list of args into named args.
    str1, str2 = args
    del args

    # Work
    # ...



    # Serial-only Portion
    GLOBALLOCK.acquire()
    print(str1)
    print(str2)
    GLOBALLOCK.release()


def main(argp=None):
    """Multiprocessing Spawn Example
    """
    # Create the number of threads you want
    pool = multiprocessing.Pool(THREADS)

    # Define two jobs, each with two args.
    func_args = [
        ('Hello', 'World',), 
        ('Goodbye', 'World',), 
    ]


    try:
        # Spawn up to 9999999 jobs, I think this is the maximum possible.
        # I do not know what happens if you exceed this.
        pool.map_async(func_worker, func_args).get(9999999)
    except KeyboardInterrupt:
        # Allow ^C to interrupt from any thread.
        sys.stdout.write('\033[0m')
        sys.stdout.write('User Interupt\n')
    pool.close()

if __name__ == '__main__':
    main()