["python","multithreading","multiprocessing"]
How to use multiprocessing queue in Python?

I'm having much trouble trying to understand just how the multiprocessing queue works on python and how to implement it. Lets say I have two python modules that access data from a shared file, let's call these two modules a writer and a reader. My plan is to have both the reader and writer put requests into two separate multiprocessing queues, and then have a third process pop these requests in a loop and execute as such. My main problem is that I really don't know how to implement multiprocessing.queue correctly, you cannot really instantiate the object for each process since they will be separate queues, how do you make sure that all processes relate to a shared queue (or in this case, queues)
Short Summary As of CY2023, the technique described in this answer is quite out of date. These days, use concurrent.futures.ProcessPoolExecutor() instead of multiprocessing, below... This answer describes the benefits and shortcomings of using concurrent.futures.ProcessPoolExecutor(). FYI, multiple python processes are sometimes used instead of threading to get the most benefit from concurrency. That said, python threading works pretty well as long as there is sufficient CPU activity to avoid the GIL (activity such as sending / receiving network traffic). Original Answer My main problem is that I really don't know how to implement multiprocessing.queue correctly, you cannot really instantiate the object for each process since they will be separate queues, how do you make sure that all processes relate to a shared queue (or in this case, queues) This is a simple example of a reader and writer sharing a single queue... The writer sends a bunch of integers to the reader; when the writer runs out of numbers, it sends 'DONE', which lets the reader know to break out of the read loop. You can spawn as many reader processes as you like... from multiprocessing import Process, Queue import time import sys def reader_proc(queue): """Read from the queue; this spawns as a separate Process""" while True: msg = queue.get() # Read from the queue and do nothing if msg == "DONE": break def writer(count, num_of_reader_procs, queue): """Write integers into the queue. A reader_proc() will read them from the queue""" for ii in range(0, count): queue.put(ii) # Put 'count' numbers into queue ### Tell all readers to stop... for ii in range(0, num_of_reader_procs): queue.put("DONE") def start_reader_procs(qq, num_of_reader_procs): """Start the reader processes and return all in a list to the caller""" all_reader_procs = list() for ii in range(0, num_of_reader_procs): ### reader_p() reads from qq as a separate process... ### you can spawn as many reader_p() as you like ### however, there is usually a point of diminishing returns reader_p = Process(target=reader_proc, args=((qq),)) reader_p.daemon = True reader_p.start() # Launch reader_p() as another proc all_reader_procs.append(reader_p) return all_reader_procs if __name__ == "__main__": num_of_reader_procs = 2 qq = Queue() # writer() writes to qq from _this_ process for count in [10**4, 10**5, 10**6]: assert 0 < num_of_reader_procs < 4 all_reader_procs = start_reader_procs(qq, num_of_reader_procs) writer(count, len(all_reader_procs), qq) # Queue stuff to all reader_p() print("All reader processes are pulling numbers from the queue...") _start = time.time() for idx, a_reader_proc in enumerate(all_reader_procs): print(" Waiting for reader_p.join() index %s" % idx) a_reader_proc.join() # Wait for a_reader_proc() to finish print(" reader_p() idx:%s is done" % idx) print( "Sending {0} integers through Queue() took {1} seconds".format( count, (time.time() - _start) ) ) print("")