Python多处理> = 125列表永远不会完成 [英] Python multiprocessing >= 125 list never finishes

问题描述

我正在出于自己的目的尝试实现此多处理教程.起初，我认为它的伸缩性不好，但是当我举一个可复制的示例时，我发现如果项目列表超过124，则似乎永远不会返回答案.在x = 124时，它的运行时间为.4秒，但是当我将其设置为x = 125时，它永远不会完成.我在Windows 7上运行Python 2.7.

I am trying to implement this multiprocessing tutorial for my own purposes. At first I thought it did not scale well, but when I made a reproducible example I found that if the list of items goes above 124, it seems to never return an answer. At x = 124 it runs in .4 seconds, but when I set it to x = 125 it never finishes. I am running Python 2.7 on Windows 7.

from multiprocessing import Lock, Process, Queue, current_process
import time

class Testclass(object):
    def __init__(self, x):
        self.x = x

def toyfunction(testclass):
    testclass.product = testclass.x * testclass.x
    return testclass


def worker(work_queue, done_queue):
    try:
        for testclass in iter(work_queue.get, 'STOP'):
            print(testclass.counter)
            newtestclass = toyfunction(testclass)
            done_queue.put(newtestclass)

    except:
        print('error')

    return True

def main(x):

    counter = 1

    database = []
    while counter <= x:
        database.append(Testclass(10))
        counter += 1
        print(counter)



    workers = 8
    work_queue = Queue()
    done_queue = Queue()
    processes = []

    start = time.clock()
    counter = 1

    for testclass in database:
        testclass.counter = counter
        work_queue.put(testclass)
        counter += 1
        print(counter)


    print('items loaded')
    for w in range(workers):
        p = Process(target=worker, args=(work_queue, done_queue))
        p.start()
        processes.append(p)
        work_queue.put('STOP')

    for p in processes:
        p.join()

    done_queue.put('STOP')

    newdatabase = []
    for testclass in iter(done_queue.get, 'STOP'):
        newdatabase.append(testclass)

    print(time.clock()-start)
    print("Done")
    return(newdatabase)

if __name__ == '__main__':
    database = main(124)
    database2 = main(125)

推荐答案

好！来自文档:

警告如上所述，如果子进程已将项目放入队列中(但尚未 使用JoinableQueue.cancel_join_thread)，则该过程直到所有 缓冲的项目已被冲洗到管道中. 这意味着，如果您尝试加入该过程，除非您确定，否则可能会陷入僵局. 放在队列中的所有项目都已消耗完.同样，如果 子进程是非守护进程，则父进程在尝试时可能会在退出时挂起 加入其所有非守护神的孩子.请注意，使用管理器创建的队列不会 没有这个问题.请参阅编程指南.

Warning As mentioned above, if a child process has put items on a queue (and it has not used JoinableQueue.cancel_join_thread), then that process will not terminate until all buffered items have been flushed to the pipe. This means that if you try joining that process you may get a deadlock unless you are sure that all items which have been put on the queue have been consumed. Similarly, if the child process is non-daemonic then the parent process may hang on exit when it tries to join all its non-daemonic children. Note that a queue created using a manager does not have this issue. See Programming guidelines.

正如我在前面的评论中指出的那样，代码尝试.join()在done_queue队列被清空之前之前进行处理-并且在以一种时髦的方式更改代码后确保在.join()之前就被清空了，该代码可以很好地处理一百万个项目.

As I noted in a comment earlier, the code attempts to .join() processes before the done_queue Queue is drained - and that after changing the code in a funky way to be sure done_queue was drained before .join()'ing, the code worked fine for a million items.

因此，这是一个飞行员错误的案例，尽管很模糊.至于为什么行为取决于传递给main(x)的数字，这是不可预测的:它取决于内部缓冲的方式.真有趣;-)

So this is a case of pilot error, although quite obscure. As to why behavior depends on the number passed to main(x), it's unpredictable: it depends on how buffering is done internally. Such fun ;-)

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