I have a question understanding the queue in the multiprocessing
module in python 3
This is what they say in the programming guidelines:
Bear in mind that a process that has put items in a queue will wait before terminating until all the buffered items are fed by the “feeder” thread to the underlying pipe. (The child process can call the Queue.cancel_join_thread method of the queue to avoid this behaviour.)
This means that whenever you use a queue you need to make sure that all items which have been put on the queue will eventually be removed before the process is joined. Otherwise you cannot be sure that processes which have put items on the queue will terminate. Remember also that non-daemonic processes will be joined automatically.
An example which will deadlock is the following:
from multiprocessing import Process, Queue def f(q): q.put('X' * 1000000) if __name__ == '__main__': queue = Queue() p = Process(target=f, args=(queue,)) p.start() p.join() # this deadlocks obj = queue.get()A fix here would be to swap the last two lines (or simply remove the p.join() line).
So apparently, queue.get()
should not be called after a join()
.
However there are examples of using queues where get
is called after a join
like:
import multiprocessing as mp import random import string # define a example function def rand_string(length, output): """ Generates a random string of numbers, lower- and uppercase chars. """ rand_str = ''.join(random.choice( string.ascii_lowercase + string.ascii_uppercase + string.digits) for i in range(length)) output.put(rand_str) if __name__ == "__main__": # Define an output queue output = mp.Queue() # Setup a list of processes that we want to run processes = [mp.Process(target=rand_string, args=(5, output)) for x in range(2)] # Run processes for p in processes: p.start() # Exit the completed processes for p in processes: p.join() # Get process results from the output queue results = [output.get() for p in processes] print(results)
I've run this program and it works (also posted as a solution to the StackOverFlow question Python 3 - Multiprocessing - Queue.get() does not respond).
Could someone help me understand what the rule for the deadlock is here?
Yes, it is. From https://docs.python.org/3/library/multiprocessing.html#exchanging-objects-between-processes: Queues are thread and process safe.
Python provides a mutual exclusion lock for use with processes via the multiprocessing. Lock class. An instance of the lock can be created and then acquired by processes before accessing a critical section, and released after the critical section. Only one process can have the lock at any time.
In other words, Multiprocess queue is pretty slow putting and getting individual data, then QuickQueue wrap several data in one list, this list is one single data that is enqueue in the queue than is more quickly than put one individual data.
A queue is a data structure on which items can be added by a call to put() and from which items can be retrieved by a call to get(). The multiprocessing. Queue provides a first-in, first-out FIFO queue, which means that the items are retrieved from the queue in the order they were added.
The queue implementation in multiprocessing that allows data to be transferred between processes relies on standard OS pipes.
OS pipes are not infinitely long, so the process which queues data could be blocked in the OS during the put()
operation until some other process uses get()
to retrieve data from the queue.
For small amounts of data, such as the one in your example, the main process can join()
all the spawned subprocesses and then pick up the data. This often works well, but does not scale, and it is not clear when it will break.
But it will certainly break with large amounts of data. The subprocess will be blocked in put()
waiting for the main process to remove some data from the queue with get()
, but the main process is blocked in join()
waiting for the subprocess to finish. This results in a deadlock.
Here is an example where a user had this exact issue. I posted some code in an answer there that helped him solve his problem.
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