Pipe Vs Msg Queue

What's the advantage of queues over pipes when communicating between processes?

The big win is that queues are process- and thread- safe. Pipes are not: if two different processes try to read from or write to the same end of a pipe, bad things happen. Queues are also at a somewhat higher level of abstraction than pipes, which may or may not be an advantage in your specific case.

Multiprocessing - Pipe vs Queue

• 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) [mpenning@mudslide ~]$ 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) [mpenning@mudslide ~]$ 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) [mpenning@mudslide ~]$ 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

mpenning@mpenning-T61:~$ 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
mpenning@mpenning-T61:~$ 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
mpnening@mpenning-T61:~$ 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
mpenning@mpenning-T61:~$

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)))

difference between message queue and shared memory?

Both shared memory and message queues can be used to exchange information between processes. The difference is in how they are used.

Shared memory is exactly what you'd think: it's an area of storage that can be read and written by more than one process. It provides no inherent synchronization; in other words, it's up to the programmer to ensure that one process doesn't clobber another's data. But it's efficient in terms of throughput: reading and writing are relatively fast operations.

A message queue is a one-way pipe: one process writes to the queue, and another reads the data in the order it was written until an end-of-data condition occurs. When the queue is created, the message size (bytes per message, usually fairly small) and queue length (maximum number of pending messages) are set. Access is slower than shared memory because each read/write operation is typically a single message. But the queue guarantees that each operation will either processes an entire message successfully or fail without altering the queue. So the writer can never fail after writing only a partial message, and the reader will either retrieve a complete message or nothing at all.

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