python多处理队列不在共享内存中

Question

I tried to run the following codes:

import multiprocessing
import time

def init_queue():
    print("init g_queue start")
    while not g_queue.empty():
        g_queue.get()
    for _index in range(10):
        g_queue.put(_index)
    print("init g_queue end")
    return

def task_io(task_id):
    print("IOTask[%s] start" % task_id)
    print("the size of queue is %s" % g_queue.qsize())
    while not g_queue.empty():
        time.sleep(1)
        try:
            data = g_queue.get(block=True, timeout=1)
            print("IOTask[%s] get data: %s" % (task_id, data))
        except Exception as excep:
            print("IOTask[%s] error: %s" % (task_id, str(excep)))
    print("IOTask[%s] end" % task_id)
    return

g_queue = multiprocessing.Queue()

if __name__ == '__main__':
    print("the size of queue is %s" % g_queue.qsize())
    init_queue()
    print("the size of queue is %s" % g_queue.qsize())
    time_0 = time.time()
    process_list = [multiprocessing.Process(target=task_io, args=(i,)) for i in range(multiprocessing.cpu_count())]
    for p in process_list:
        p.start()
    for p in process_list:
        if p.is_alive():
            p.join()
    print("End:", time.time() - time_0, "\n")

what I got was the following:

the size of queue is 0
init g_queue start
init g_queue end
the size of queue is 10
IOTask[0] start
the size of queue is 0
IOTask[0] end
IOTask[1] start
the size of queue is 0
IOTask[1] end
('End:', 0.6480000019073486, '\n')

What I was expecting was

IOTask[0] start
the size of queue is 10

Because after initialization of g_queue, the size of queue was supposed to be 10, not 0. It seems like the queue is not in the shared memory. When the sub process starts, a copy of g_queue is created and its size is 0.

Why multiprocessing.queue is not in the shared memory? Please advise. Many thanks!

Answer 1

You should pass your g_queue as a parameter, then it will work.

demo for using multiprocessing with queue

import multiprocessing
import time


def long_time_calculate(n, result_queue):
 time.sleep(1)
 result_queue.put(n)


if __name__ == '__main__':
 result_queue = multiprocessing.Queue()
 pool_size = multiprocessing.cpu_count() * 2
 pool = multiprocessing.Pool(processes=pool_size, maxtasksperchild=4)

 manager = multiprocessing.Manager()
 result_queue = manager.Queue()

 inputs = [(1, result_queue), (2, result_queue), (3, result_queue), (4, result_queue)]

 for input in inputs:
     pool.apply_async(long_time_calculate, input)

 pool.close()
 pool.join()

 print(list(result_queue.get() for _ in inputs))