python - How do you pass a Queue reference to a function managed by pool.map_async()?

42
votes

I want a long-running process to return its progress over a Queue (or something similar) which I will feed to a progress bar dialog. I also need the result when the process is completed. A test example here fails with a RuntimeError: Queue objects should only be shared between processes through inheritance.

import multiprocessing, time

def task(args):
    count = args[0]
    queue = args[1]
    for i in xrange(count):
        queue.put("%d mississippi" % i)
    return "Done"

def main():
    q = multiprocessing.Queue()
    pool = multiprocessing.Pool()
    result = pool.map_async(task, [(x, q) for x in range(10)])
    time.sleep(1)
    while not q.empty():
        print q.get()
    print result.get()

if __name__ == "__main__":
    main()

I've been able to get this to work using individual Process objects (where I am alowed to pass a Queue reference) but then I don't have a pool to manage the many processes I want to launch. Any advise on a better pattern for this?

2
pythonqueuemultiprocessingpool
It's not an answer to your question, but try the execnet library <codespeak.net/execnet> for multi-process mappings. The built-in multiprocessing has some issues still to be fixed (see the Python tracker). Besides that its source code is quite large and complicated. The execnet library looks much better to me than multiprocessing. - Andrey Vlasovskikh

2 Answers

56
votes

The following code seems to work:

import multiprocessing, time

def task(args):
    count = args[0]
    queue = args[1]
    for i in xrange(count):
        queue.put("%d mississippi" % i)
    return "Done"


def main():
    manager = multiprocessing.Manager()
    q = manager.Queue()
    pool = multiprocessing.Pool()
    result = pool.map_async(task, [(x, q) for x in range(10)])
    time.sleep(1)
    while not q.empty():
        print q.get()
    print result.get()

if __name__ == "__main__":
    main()

Note that the Queue is got from a manager.Queue() rather than multiprocessing.Queue(). Thanks Alex for pointing me in this direction.

8
votes

Making q global works...:

import multiprocessing, time

q = multiprocessing.Queue()

def task(count):
    for i in xrange(count):
        q.put("%d mississippi" % i)
    return "Done"

def main():
    pool = multiprocessing.Pool()
    result = pool.map_async(task, range(10))
    time.sleep(1)
    while not q.empty():
        print q.get()
    print result.get()

if __name__ == "__main__":
    main()

If you need multiple queues, e.g. to avoid mixing up the progress of the various pool processes, a global list of queues should work (of course, each process will then need to know what index in the list to use, but that's OK to pass as an argument;-).