问题描述

我正在尝试实现这个多处理 教程 用于我自己的目的.起初我认为它不能很好地扩展，但是当我做了一个可重现的例子时，我发现如果项目列表超过 124，它似乎永远不会返回答案.在 x = 124 处，它在 0.4 秒内运行，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

好的！来自文档:

警告如上所述，如果子进程已将项目放入队列中(并且它没有使用 JoinableQueue.cancel_join_thread)，那么该进程将不会终止，直到所有缓冲的项目已被刷新到管道.这意味着如果您尝试加入该进程，除非您确定，否则您可能会遇到死锁已放入队列的所有项目都已被消耗.同样，如果子进程是非守护进程，然后父进程可能会在尝试退出时挂起加入其所有非恶魔的孩子.请注意，使用管理器创建的队列确实没有这个问题.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，代码尝试 .join() 处理 before done_queue 队列耗尽 - 并且在以一种时髦的方式更改代码以确保在 .join() 之前耗尽了 done_queue 之后，代码对一百万个项目运行良好.

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.

所以这是一个飞行员错误的例子，虽然很模糊.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:它取决于内部缓冲是如何完成的.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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