Multiprocessing - Pipe vs Queue

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.

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(): 57.769 ms Sending 10000 numbers to mp.Queue(): 74.844 ms Sending 10000 numbers to mp.SimpleQueue(): 66.662 ms Sending 10000 numbers to queue.Queue(): 8.253 ms Sending 10000 numbers to queue.SimpleQueue(): 0.831 ms Sending 100000 numbers to mp.Pipe(): 421.775 ms Sending 100000 numbers to mp.Queue(): 812.989 ms Sending 100000 numbers to mp.SimpleQueue(): 682.442 ms Sending 100000 numbers to queue.Queue(): 82.091 ms Sending 100000 numbers to queue.SimpleQueue(): 7.831 ms Sending 1000000 numbers to mp.Pipe(): 4198.766 ms Sending 1000000 numbers to mp.Queue(): 8302.404 ms Sending 1000000 numbers to mp.SimpleQueue(): 6845.322 ms Sending 1000000 numbers to queue.Queue(): 840.551 ms Sending 1000000 numbers to queue.SimpleQueue(): 77.338 ms Sending 10000000 numbers to mp.Pipe(): 43341.405 ms Sending 10000000 numbers to mp.Queue(): 85868.946 ms Sending 10000000 numbers to mp.SimpleQueue(): 71669.009 ms Sending 10000000 numbers to queue.Queue(): 8463.520 ms Sending 10000000 numbers to queue.SimpleQueue(): 773.727 ms 

This is on an M1 MacBook Pro running Python 3.11.7.

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 def pretty_print(count: int, name: str, t: float) -> None: text = f"Sending {count} numbers to {name}:" t_text = f"{t*1e3:.3f} ms" print(f"{text:<50}{t_text:>15}") 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() pretty_print(count, "mp.Pipe()", time.time() - _start) # 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() pretty_print(count, "mp.Queue()", time.time() - _start) # third: mp.SimpleQueue p_mpsqueue = mp.SimpleQueue() reader_p = td.Thread(target=reader_queue, args=((p_mpsqueue),)) reader_p.start() _start = time.time() for ii in range(0, count): p_mpsqueue.put(ii) p_mpsqueue.put("DONE") reader_p.join() pretty_print(count, "mp.SimpleQueue()", time.time() - _start) # fourth: 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() pretty_print(count, "queue.Queue()", time.time() - _start) # fifth: 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() pretty_print(count, "queue.SimpleQueue()", time.time() - _start) 

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))) 

NEW UPDATE:

In my application I have multiple processes writing to the queue at once, and one process consuming the results. It turns out that these queues perform VERY differently in this case. The standard multiprocessing.Queue seems to become overwhelmed very easily when multiple processes are writing to it at once, and the read performance drops down by many orders of magnitude. In this situation, there are much faster alternatives to use.

Here I compare the read performance as a function of message size in bytes of three kinds of queues while the queues are being continuously written to by 5 processes. The three kinds of queues are:

1. multiprocessing.Queue
2. multiprocessing.Manager.Queue
3. A custom queue that uses a multiprocessing.Pipe with locks so that it can be used by many processes safely.

Click here to see a plot of the results

The results show that here is a huge difference in performance between the three kinds of queues. The fastest is the one that uses Pipes, followed by a Queue created using a Manager, followed by a standard multiprocessing.Queue. If you care about read performance while the queues are being written to, the best bet is to use a pipe or the managed queue.

Here is the sourcecode for this new test with plots:

Source code:

from __future__ import annotations """ queue_comparison_plots.py """ import asyncio import random from dataclasses import dataclass from itertools import groupby from multiprocessing import Process, Queue, Manager import time from matplotlib import pyplot as plt import multiprocessing as mp class PipeQueue(): pipe_in: mp.connection.Connection pipe_out: mp.connection.Connection def __init__(self): self.pipe_out, self.pipe_in = mp.Pipe(duplex=False) self.write_lock = mp.Lock() self.read_lock = mp.Lock() def get(self): with self.read_lock: return self.pipe_out.recv() def put(self, val): with self.write_lock: self.pipe_in.send(val) @dataclass class Result(): queue_type: str data_size_bytes: int num_writer_processes: int num_reader_processes: int msg_read_rate: float class PerfTracker(): def __init__(self): self.running = mp.Event() self.count = mp.Value("i") self.start_time: float | None = None self.end_time: float | None = None @property def rate(self) -> float: return (self.count.value)/(self.end_time-self.start_time) def update(self): if self.running.is_set(): with self.count.get_lock(): self.count.value += 1 def start(self): with self.count.get_lock(): self.count.value = 0 self.running.set() self.start_time = time.time() def end(self): self.running.clear() self.end_time = time.time() def reader_proc(queue, perf_tracker, num_threads = 1): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete(reader_proc_async(queue, perf_tracker, num_threads)) async def reader_proc_async(queue, perf_tracker, num_threads = 1): async def thread(queue, perf_tracker): while True: msg = queue.get() perf_tracker.update() futures = [] for i in range(num_threads): futures.append(thread(queue, perf_tracker)) await asyncio.gather(*futures) def writer_proc(queue, data_size_bytes: int): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete(writer_proc_async(queue, data_size_bytes)) async def writer_proc_async(queue, data_size_bytes: int): val = random.randbytes(data_size_bytes) while True: queue.put(val) async def main(): num_reader_procs = 1 num_reader_threads = 1 num_writer_procs = 5 test_time = 5 results = [] for queue_type in ["Pipe + locks", "Queue using Manager", "Queue"]: for data_size_bytes_order_of_magnitude in range(8): data_size_bytes = 10 ** data_size_bytes_order_of_magnitude perf_tracker = PerfTracker() if queue_type == "Queue using Manager": manager = Manager() pqueue = manager.Queue() elif queue_type == "Pipe + locks": pqueue = PipeQueue() elif queue_type == "Queue": pqueue = Queue() else: raise NotImplementedError() reader_ps = [] for i in range(num_reader_procs): reader_p = Process(target=reader_proc, args=(pqueue, perf_tracker, num_reader_threads)) reader_ps.append(reader_p) writer_ps = [] for i in range(num_writer_procs): writer_p = Process(target=writer_proc, args=(pqueue, data_size_bytes)) writer_ps.append(writer_p) for writer_p in writer_ps: writer_p.start() for reader_p in reader_ps: reader_p.start() await asyncio.sleep(1) print("start") perf_tracker.start() await asyncio.sleep(test_time) perf_tracker.end() print(f"Finished. {queue_type} | {data_size_bytes} | {perf_tracker.rate} msg/sec") results.append( Result( queue_type = queue_type, data_size_bytes = data_size_bytes, num_writer_processes = num_writer_procs, num_reader_processes = num_reader_procs, msg_read_rate = perf_tracker.rate, ) ) for writer_p in writer_ps: writer_p.kill() for reader_p in reader_ps: reader_p.kill() print(results) fig, ax = plt.subplots() count = 0 for queue_type, result_iterator in groupby(results, key=lambda result: result.queue_type): grouped_results = list(result_iterator) x_coords = [x.data_size_bytes for x in grouped_results] y_coords = [x.msg_read_rate for x in grouped_results] ax.plot(x_coords, y_coords, label=f"{queue_type}") count += 1 ax.set_title(f"Queue read performance comparison while writing continuously", fontsize=11) ax.legend(loc='upper right', fontsize=10) ax.set_yscale("log") ax.set_xscale("log") ax.set_xlabel("Message size (bytes)") ax.set_ylabel("Message throughput (messages/second)") plt.show() if __name__=='__main__': loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete(main()) 

Short Summary

As of CY2023, the technique described in this answer is quite out of date. These days, you should use concurrent.futures.ProcessPoolExecutor()...

Regardless of the python concurrency tool you use, an answer for the OP's question is still valid, below.

Original Answer

• 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)))