一个多处理管理器中的多个队列 [英] Multiple queues from one multiprocessing Manager

问题描述

我正在编写一个脚本，该脚本将使用python的多处理和线程模块. 为了您的理解，我产生了尽可能多的可用内核进程，并且在每个进程内部都启动了例如25个线程. 每个线程从input_queue消耗并产生为output_queue. 对于队列对象，我使用multiprocessing.Queue.

I'm writing a script that will use python's multiprocessing and threading module. For your understanding, I spawn as much processes as cores are available and inside each process I start e.g. 25 threads. Each thread consumes from an input_queue and produces to an output_queue. For the queue object I use multiprocessing.Queue.

在我的第一次测试后，我陷入了死锁，因为负责馈送和刷新Queue的线程已挂起.一段时间后，我发现可以使用Queue().cancel_join_thread()解决此问题.

After my first tests I got a deadlock because the the thread responsible to feed and flush the Queue was hanging. After a while I found that I can use Queue().cancel_join_thread() to work around this problem.

但是由于可能会丢失数据，我想使用:multiprocessing.Manager().Queue()

But because of the possibility of data loss I would like to use: multiprocessing.Manager().Queue()

现在的实际问题是: 每个队列使用一个管理器对象更好吗?还是应该创建一个管理器并从同一管理器对象获得两个问题?

Now actual question: Is it better to use one manager object for each queue? Or should I create one manager and get two quese from the same manager object?

# One manager for all queues
import multiprocessing

manager = multiprocessing.Manager()
input_queue = manager.Queue()
output_queue = manager.Queue()

...Magic...

# As much managers as queues
manager_in = multiprocessing.Manager()
queue_in = manager_in.Queue()

manager_out = multiprocessing.Manager()
queue_out = manager_out.Queue()

...Magic...

谢谢您的帮助.

推荐答案

不需要使用两个单独的Manager对象.如您所见，Manager对象允许在多个进程之间共享对象.来自文档:

There is no need to use two separate Manager objects. As you have already seen the Manager object allows sharing objects among multiple processes; from the docs:

管理器提供了一种创建可以在不同进程之间共享的数据的方法.管理器对象控制管理共享对象的服务器进程.其他进程可以使用代理访问共享对象.

Managers provide a way to create data which can be shared between different processes. A manager object controls a server process which manages shared objects. Other processes can access the shared objects by using proxies.

因此，如果您有两个不同的队列，则仍然可以使用同一管理器.如果它对某人有所帮助，这是一个简单的示例，其中一个管理员使用两个队列:

Therefore if you have two different queues you can still use the same manager. In case it helps someone, here is a simple example using two queues with one manager:

from multiprocessing import Manager, Process
import time


class Worker(Process):
    """
    Simple worker.
    """

     def __init__(self, name, in_queue, out_queue):
        super(Worker, self).__init__()
        self.name = name
        self.in_queue = in_queue
        self.out_queue = out_queue

    def run(self):
        while True:
            # grab work; do something to it (+1); then put the result on the output queue
            work = self.in_queue.get()
            print("{} got {}".format(self.name, work))
            work += 1

            # sleep to allow the other workers a chance (b/c the work action is too simple)
            time.sleep(1)

            # put the transformed work on the queue
            print("{} puts {}".format(self.name, work))
            self.out_queue.put(work)


if __name__ == "__main__":
    # construct the queues
    manager = Manager()
    inq = manager.Queue()
    outq = manager.Queue()

    # construct the workers
    workers = [Worker(str(name), inq, outq) for name in range(3)]
    for worker in workers:
        worker.start()

    # add data to the queue for processing
    work_len = 10
    for x in range(work_len):
        inq.put(x)

    while outq.qsize() != work_len:
        # waiting for workers to finish
        print("Waiting for workers. Out queue size {}".format(outq.qsize()))
        time.sleep(1)

    # clean up
    for worker in workers:
        worker.terminate()

    # print the outputs
    while not outq.empty():
        print(outq.get())

改为使用两名经理:

# construct the queues
manager1 = Manager()
inq = manager1.Queue()
manager2 = Manager()
outq = manager2.Queue()

可以，但是没有必要.

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