multiprocessing vs multithreading vs asyncio in Python 3

TL;DR

Making the Right Choice:

We have walked through the most popular forms of concurrency. But the question remains - when should choose which one? It really depends on the use cases. From my experience (and reading), I tend to follow this pseudo code:

if io_bound:    if io_very_slow:        print("Use Asyncio")    else:        print("Use Threads")else:    print("Multi Processing")

• CPU Bound => Multi Processing
• I/O Bound, Fast I/O, Limited Number of Connections => Multi Threading
• I/O Bound, Slow I/O, Many connections => Asyncio

Reference

[NOTE]:

• If you have a long call method (i.e. a method that contained with a sleep time or lazy I/O), the best choice is asyncio, Twisted or Tornado approach (coroutine methods), that works with a single thread as concurrency.
• asyncio works on Python3.4 and later.
• Tornado and Twisted are ready since Python2.7
• uvloop is ultra fast asyncio event loop (uvloop makes asyncio 2-4x faster).

[UPDATE (2019)]:

• Japranto ^(GitHub) is a very fast pipelining HTTP server based on uvloop.

They are intended for (slightly) different purposes and/or requirements. CPython (a typical, mainline Python implementation) still has the global interpreter lock so a multi-threaded application (a standard way to implement parallel processing nowadays) is suboptimal. That's why multiprocessing may be preferred over threading. But not every problem may be effectively split into [almost independent] pieces, so there may be a need in heavy interprocess communications. That's why multiprocessing may not be preferred over threading in general.

asyncio (this technique is available not only in Python, other languages and/or frameworks also have it, e.g. Boost.ASIO) is a method to effectively handle a lot of I/O operations from many simultaneous sources w/o need of parallel code execution. So it's just a solution (a good one indeed!) for a particular task, not for parallel processing in general.

In multiprocessing you leverage multiple CPUs to distribute your calculations. Since each of the CPUs runs in parallel, you're effectively able to run multiple tasks simultaneously. You would want to use multiprocessing for CPU-bound tasks. An example would be trying to calculate a sum of all elements of a huge list. If your machine has 8 cores, you can "cut" the list into 8 smaller lists and calculate the sum of each of those lists separately on separate core and then just add up those numbers. You'll get a ~8x speedup by doing that.

In (multi)threading you don't need multiple CPUs. Imagine a program that sends lots of HTTP requests to the web. If you used a single-threaded program, it would stop the execution (block) at each request, wait for a response, and then continue once received a response. The problem here is that your CPU isn't really doing work while waiting for some external server to do the job; it could have actually done some useful work in the meantime! The fix is to use threads - you can create many of them, each responsible for requesting some content from the web. The nice thing about threads is that, even if they run on one CPU, the CPU from time to time "freezes" the execution of one thread and jumps to executing the other one (it's called context switching and it happens constantly at non-deterministic intervals). So if your task is I/O bound - use threading.

asyncio is essentially threading where not the CPU but you, as a programmer (or actually your application), decide where and when does the context switch happen. In Python you use an await keyword to suspend the execution of your coroutine (defined using async keyword).