Multiprocessing – Pipe vs Queue

Question:

What are the fundamental differences between queues and pipes in Python’s multiprocessing package?

In what scenarios should one choose one over the other? When is it advantageous to use Pipe()? When is it advantageous to use Queue()?

Asked By: Jonathan Livni

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Answers:

  • A Pipe() can only have two endpoints.

  • A Queue() can have multiple producers and consumers.

When to use them

If you need more than two points to communicate, use a Queue().

If you need absolute performance, a Pipe() is much faster because Queue() is built on top of Pipe().

Performance Benchmarking

Let’s assume you want to spawn two processes and send messages between them as quickly as possible. These are the timing results of a drag race between similar tests using Pipe() and Queue()

FYI, I threw in results for SimpleQueue() and JoinableQueue() as a bonus.

  • JoinableQueue() accounts for tasks when queue.task_done() is called (it doesn’t even know about the specific task, it just counts unfinished tasks in the queue), so that queue.join() knows the work is finished.

The code for each at bottom of this answer…

# This is on a Thinkpad T430, VMWare running Debian 11 VM, and Python 3.9.2

$ python multi_pipe.py
Sending 10000 numbers to Pipe() took 0.14316844940185547 seconds
Sending 100000 numbers to Pipe() took 1.3749017715454102 seconds
Sending 1000000 numbers to Pipe() took 14.252539157867432 seconds
$  python multi_queue.py
Sending 10000 numbers to Queue() took 0.17014789581298828 seconds
Sending 100000 numbers to Queue() took 1.7723784446716309 seconds
Sending 1000000 numbers to Queue() took 17.758610725402832 seconds
$ python multi_simplequeue.py
Sending 10000 numbers to SimpleQueue() took 0.14937686920166016 seconds
Sending 100000 numbers to SimpleQueue() took 1.5389132499694824 seconds
Sending 1000000 numbers to SimpleQueue() took 16.871352910995483 seconds
$ python multi_joinablequeue.py
Sending 10000 numbers to JoinableQueue() took 0.15144729614257812 seconds
Sending 100000 numbers to JoinableQueue() took 1.567549228668213 seconds
Sending 1000000 numbers to JoinableQueue() took 16.237736225128174 seconds



# This is on a Thinkpad T430, VMWare running Debian 11 VM, and Python 3.7.0

(py37_test) [[email protected] ~]$ python multi_pipe.py
Sending 10000 numbers to Pipe() took 0.13469791412353516 seconds
Sending 100000 numbers to Pipe() took 1.5587594509124756 seconds
Sending 1000000 numbers to Pipe() took 14.467186689376831 seconds
(py37_test) [[email protected] ~]$ python multi_queue.py
Sending 10000 numbers to Queue() took 0.1897726058959961 seconds
Sending 100000 numbers to Queue() took 1.7622203826904297 seconds
Sending 1000000 numbers to Queue() took 16.89015531539917 seconds
(py37_test) [[email protected] ~]$ python multi_joinablequeue.py
Sending 10000 numbers to JoinableQueue() took 0.2238149642944336 seconds
Sending 100000 numbers to JoinableQueue() took 1.4744081497192383 seconds
Sending 1000000 numbers to JoinableQueue() took 15.264554023742676 seconds


# This is on a ThinkpadT61 running Ubuntu 11.10, and Python 2.7.2

[email protected]:~$ python multi_pipe.py 
Sending 10000 numbers to Pipe() took 0.0369849205017 seconds
Sending 100000 numbers to Pipe() took 0.328398942947 seconds
Sending 1000000 numbers to Pipe() took 3.17266988754 seconds
[email protected]:~$ python multi_queue.py 
Sending 10000 numbers to Queue() took 0.105256080627 seconds
Sending 100000 numbers to Queue() took 0.980564117432 seconds
Sending 1000000 numbers to Queue() took 10.1611330509 seconds
[email protected]:~$ python multi_joinablequeue.py 
Sending 10000 numbers to JoinableQueue() took 0.172781944275 seconds
Sending 100000 numbers to JoinableQueue() took 1.5714070797 seconds
Sending 1000000 numbers to JoinableQueue() took 15.8527247906 seconds
[email protected]:~$

In summary:

  • Under python 2.7, Pipe() is about 300% faster than a Queue(). Don’t even think about the JoinableQueue() unless you really must have the benefits.
  • Under python 3.x, Pipe() still has a (roughly 20%) edge over the Queue()s, but the performance gaps between Pipe() and Queue() are not as dramatic as they were in python 2.7. The various Queue() implementations are within roughly 15% of each other. Also my tests use integer data. Some people commented that they found performance differences in the data-types used with multiprocessing.

Bottom line for python 3.x: YMMV… consider running your own tests with your own data-types (i.e. integer / string / objects) to form conclusions about your own platforms of interest and use-cases.

I should also mention that my python3.x performance tests are inconsistent and vary somewhat. I ran multiple tests over several minutes to get the best results for each case. I suspect these differences have something to do with running my python3 tests under VMWare / virtualization; however, the virtualization diagnosis is speculation.

*** RESPONSE TO A COMMENT ABOUT TEST TECHNIQUES ***

In the comments, @JJC said:

a more fair comparison would be running N workers, each communicating with main thread via point-to-point pipe compared to performance of running N workers all pulling from a single point-to-multipoint queue.

Originally, this answer only considered the performance of one worker and one producer; that’s the baseline use-case for Pipe(). Your comment requires adding different tests for multiple worker processes. While this is a valid observation for common Queue() use-cases, it could easily explode the test matrix along a completely new axis (i.e. adding tests with various numbers of worker processes).

BONUS MATERIAL 2

Multiprocessing introduces subtle changes in information flow that make debugging hard unless you know some shortcuts. For instance, you might have a script that works fine when indexing through a dictionary in under many conditions, but infrequently fails with certain inputs.

Normally we get clues to the failure when the entire python process crashes; however, you don’t get unsolicited crash tracebacks printed to the console if the multiprocessing function crashes. Tracking down unknown multiprocessing crashes is hard without a clue to what crashed the process.

The simplest way I have found to track down multiprocessing crash informaiton is to wrap the entire multiprocessing function in a try / except and use traceback.print_exc():

import traceback
def run(self, args):
    try:
        # Insert stuff to be multiprocessed here
        return args[0]['that']
    except:
        print "FATAL: reader({0}) exited while multiprocessing".format(args) 
        traceback.print_exc()

Now, when you find a crash you see something like:

FATAL: reader([{'crash': 'this'}]) exited while multiprocessing
Traceback (most recent call last):
  File "foo.py", line 19, in __init__
    self.run(args)
  File "foo.py", line 46, in run
    KeyError: 'that'

Source Code:


"""
multi_pipe.py
"""
from multiprocessing import Process, Pipe
import time

def reader_proc(pipe):
    ## Read from the pipe; this will be spawned as a separate Process
    p_output, p_input = pipe
    p_input.close()    # We are only reading
    while True:
        msg = p_output.recv()    # Read from the output pipe and do nothing
        if msg=='DONE':
            break

def writer(count, p_input):
    for ii in range(0, count):
        p_input.send(ii)             # Write 'count' numbers into the input pipe
    p_input.send('DONE')

if __name__=='__main__':
    for count in [10**4, 10**5, 10**6]:
        # Pipes are unidirectional with two endpoints:  p_input ------> p_output
        p_output, p_input = Pipe()  # writer() writes to p_input from _this_ process
        reader_p = Process(target=reader_proc, args=((p_output, p_input),))
        reader_p.daemon = True
        reader_p.start()     # Launch the reader process

        p_output.close()       # We no longer need this part of the Pipe()
        _start = time.time()
        writer(count, p_input) # Send a lot of stuff to reader_proc()
        p_input.close()
        reader_p.join()
        print("Sending {0} numbers to Pipe() took {1} seconds".format(count,
            (time.time() - _start)))

"""
multi_queue.py
"""

from multiprocessing import Process, Queue
import time
import sys

def reader_proc(queue):
    ## Read from the queue; this will be spawned as a separate Process
    while True:
        msg = queue.get()         # Read from the queue and do nothing
        if (msg == 'DONE'):
            break

def writer(count, queue):
    ## Write to the queue
    for ii in range(0, count):
        queue.put(ii)             # Write 'count' numbers into the queue
    queue.put('DONE')

if __name__=='__main__':
    pqueue = Queue() # writer() writes to pqueue from _this_ process
    for count in [10**4, 10**5, 10**6]:             
        ### reader_proc() reads from pqueue as a separate process
        reader_p = Process(target=reader_proc, args=((pqueue),))
        reader_p.daemon = True
        reader_p.start()        # Launch reader_proc() as a separate python process

        _start = time.time()
        writer(count, pqueue)    # Send a lot of stuff to reader()
        reader_p.join()         # Wait for the reader to finish
        print("Sending {0} numbers to Queue() took {1} seconds".format(count, 
            (time.time() - _start)))

"""
multi_simplequeue.py
"""

from multiprocessing import Process, SimpleQueue
import time
import sys

def reader_proc(queue):
    ## Read from the queue; this will be spawned as a separate Process
    while True:
        msg = queue.get()         # Read from the queue and do nothing
        if (msg == 'DONE'):
            break

def writer(count, queue):
    ## Write to the queue
    for ii in range(0, count):
        queue.put(ii)             # Write 'count' numbers into the queue
    queue.put('DONE')

if __name__=='__main__':
    pqueue = SimpleQueue() # writer() writes to pqueue from _this_ process
    for count in [10**4, 10**5, 10**6]:
        ### reader_proc() reads from pqueue as a separate process
        reader_p = Process(target=reader_proc, args=((pqueue),))
        reader_p.daemon = True
        reader_p.start()        # Launch reader_proc() as a separate python process

        _start = time.time()
        writer(count, pqueue)    # Send a lot of stuff to reader()
        reader_p.join()         # Wait for the reader to finish
        print("Sending {0} numbers to SimpleQueue() took {1} seconds".format(count,
            (time.time() - _start)))

"""
multi_joinablequeue.py
"""
from multiprocessing import Process, JoinableQueue
import time

def reader_proc(queue):
    ## Read from the queue; this will be spawned as a separate Process
    while True:
        msg = queue.get()         # Read from the queue and do nothing
        queue.task_done()

def writer(count, queue):
    for ii in range(0, count):
        queue.put(ii)             # Write 'count' numbers into the queue

if __name__=='__main__':
    for count in [10**4, 10**5, 10**6]:
        jqueue = JoinableQueue() # writer() writes to jqueue from _this_ process
        # reader_proc() reads from jqueue as a different process...
        reader_p = Process(target=reader_proc, args=((jqueue),))
        reader_p.daemon = True
        reader_p.start()     # Launch the reader process
        _start = time.time()
        writer(count, jqueue) # Send a lot of stuff to reader_proc() (in different process)
        jqueue.join()         # Wait for the reader to finish
        print("Sending {0} numbers to JoinableQueue() took {1} seconds".format(count, 
            (time.time() - _start)))
Answered By: Mike Pennington

One additional feature of Queue() that is worth noting is the feeder thread. This section notes “When a process first puts an item on the queue a feeder thread is started which transfers objects from a buffer into the pipe.” An infinite number of (or maxsize) items can be inserted into Queue() without any calls to queue.put() blocking. This allows you to store multiple items in a Queue(), until your program is ready to process them.

Pipe(), on the other hand, has a finite amount of storage for items that have been sent to one connection, but have not been received from the other connection. After this storage is used up, calls to connection.send() will block until there is space to write the entire item. This will stall the thread doing the writing until some other thread reads from the pipe. Connection objects give you access to the underlying file descriptor. On *nix systems, you can prevent connection.send() calls from blocking using the os.set_blocking() function. However, this will cause problems if you try to send a single item that does not fit in the pipe’s file. Recent versions of Linux allow you to increase the size of a file, but the maximum size allowed varies based on system configurations. You should therefore never rely on Pipe() to buffer data. Calls to connection.send could block until data gets read from the pipe somehwere else.

In conclusion, Queue is a better choice than pipe when you need to buffer data. Even when you only need to communicate between two points.

Answered By: akindofyoga

If – like me – you are wondering whether to use a multiprocessing construct (Pipe or Queue) in your threading programs for performance, I have adapted Mike Pennington‘s script to compare against queue.Queue and queue.SimpleQueue:

Sending 10000 numbers to mp.Pipe() took 65.051 ms
Sending 10000 numbers to mp.Queue() took 78.977 ms
Sending 10000 numbers to queue.Queue() took 14.781 ms
Sending 10000 numbers to queue.SimpleQueue() took 0.939 ms
Sending 100000 numbers to mp.Pipe() took 449.564 ms
Sending 100000 numbers to mp.Queue() took 811.938 ms
Sending 100000 numbers to queue.Queue() took 149.387 ms
Sending 100000 numbers to queue.SimpleQueue() took 9.264 ms
Sending 1000000 numbers to mp.Pipe() took 4660.451 ms
Sending 1000000 numbers to mp.Queue() took 8499.743 ms
Sending 1000000 numbers to queue.Queue() took 1490.062 ms
Sending 1000000 numbers to queue.SimpleQueue() took 91.238 ms
Sending 10000000 numbers to mp.Pipe() took 45095.935 ms
Sending 10000000 numbers to mp.Queue() took 84829.042 ms
Sending 10000000 numbers to queue.Queue() took 15179.356 ms
Sending 10000000 numbers to queue.SimpleQueue() took 917.562 ms

Unsurprisingly, using the queue package yields much better results if all you have are threads.
That said, I was surprised how performant queue.SimpleQueue is.


"""
pipe_performance.py
"""
import threading as td
import queue
import multiprocessing as mp
import multiprocessing.connection as mp_connection
import time
import typing

def reader_pipe(p_out: mp_connection.Connection) -> None:
    while True:
        msg = p_out.recv()
        if msg=='DONE':
            break

def reader_queue(p_queue: "queue.Queue[typing.Union[str, int]]") -> None:
    while True:
        msg = p_queue.get()
        if msg=='DONE':
            break

if __name__=='__main__':
    for count in [10**4, 10**5, 10**6, 10**7]:
    # first: mp.pipe
        p_mppipe_out, p_mppipe_in = mp.Pipe()
        reader_p = td.Thread(target=reader_pipe, args=((p_mppipe_out),))
        reader_p.start()
        _start = time.time()
        for ii in range(0, count):
            p_mppipe_in.send(ii)
        p_mppipe_in.send('DONE')
        reader_p.join()
        print(f"Sending {count} numbers to mp.Pipe() took {(time.time() - _start)*1e3:.3f} ms")

    # second: mp.Queue
        p_mpqueue  = mp.Queue()
        reader_p = td.Thread(target=reader_queue, args=((p_mpqueue),))
        reader_p.start()
        _start = time.time()
        for ii in range(0, count):
            p_mpqueue.put(ii)
        p_mpqueue.put('DONE')
        reader_p.join()
        print(f"Sending {count} numbers to mp.Queue() took {(time.time() - _start)*1e3:.3f} ms")

    # third: queue.Queue
        p_queue = queue.Queue()
        reader_p = td.Thread(target=reader_queue, args=((p_queue),))
        reader_p.start()
        _start = time.time()
        for ii in range(0, count):
            p_queue.put(ii)
        p_queue.put('DONE')
        reader_p.join()
        print(f"Sending {count} numbers to queue.Queue() took {(time.time() - _start)*1e3:.3f} ms")

    # fourth: queue.SimpleQueue
        p_squeue = queue.SimpleQueue()
        reader_p = td.Thread(target=reader_queue, args=((p_squeue),))
        reader_p.start()
        _start = time.time()
        for ii in range(0, count):
            p_squeue.put(ii)
        p_squeue.put('DONE')
        reader_p.join()
        print(f"Sending {count} numbers to queue.SimpleQueue() took {(time.time() - _start)*1e3:.3f} ms")
Answered By: Tobias Pfandzelter

When using a concurrent.futures.ProcessPoolExecutor to execute your child processes in python, you cannot pass a multiprocessing.Queue as an argument. If you do, you will get an error like:

RuntimeError: Queue objects should only be shared between processes through inheritance

In this situation, one workaround is to use a multiprocessing.Manager to create a queue that you can pass to the process as an argument. However, I have found that this kind of queue is much slower than the standard multiprocessing.Queue. I have not found any benchmarks for this kind of queue so I ran them myself. I have modified Mike Pennington’s test code to benchmark this Manager kind of queue.

Here are the results. I start by re-running the standard Queue test as a reference:

Sending 10000 numbers to Queue() took 0.12702512741088867 seconds
Sending 100000 numbers to Queue() took 0.9972114562988281 seconds
Sending 1000000 numbers to Queue() took 9.9016695022583 seconds

Sending 10000 numbers to manager.Queue() took 1.0181043148040771 seconds
Sending 100000 numbers to manager.Queue() took 10.438829898834229 seconds
Sending 1000000 numbers to manager.Queue() took 102.3624701499939 seconds

The results show that the queue created by the multiprocessing.Manager is approximately 10x slower than the standard multiprocessing.Queue. Pretty huge difference. Don’t use this kind of queue if you care about performance.

Source code:

"""
manager_multi_queue.py
"""

from multiprocessing import Process, Queue, Manager
import time
import sys

def reader_proc(queue):
    ## Read from the queue; this will be spawned as a separate Process
    while True:
        msg = queue.get()         # Read from the queue and do nothing
        if (msg == 'DONE'):
            break

def writer(count, queue):
    ## Write to the queue
    for ii in range(0, count):
        queue.put(ii)             # Write 'count' numbers into the queue
    queue.put('DONE')

if __name__=='__main__':
    manager = Manager()
    pqueue = manager.Queue() # writer() writes to pqueue from _this_ process
    for count in [10**4, 10**5, 10**6]:
        ### reader_proc() reads from pqueue as a separate process
        reader_p = Process(target=reader_proc, args=((pqueue),))
        reader_p.daemon = True
        reader_p.start()        # Launch reader_proc() as a separate python process

        _start = time.time()
        writer(count, pqueue)    # Send a lot of stuff to reader()
        reader_p.join()         # Wait for the reader to finish
        print("Sending {0} numbers to manager.Queue() took {1} seconds".format(count, (time.time() - _start)))
Answered By: hyperevo