How to Deal With Concurrency in Python

There are 3 common ways to deal with concurrency in Python: threading, asyncio and multiprocessing. Watch the short to find out when to which.

#arjancodes #softwaredesign #python

[abdelghafourfid]

I'd really love a full example showing all those three methods. maybe an example with two groups of tasks (IO bound and CPU bound), then showing how we could improve the code performance starting by multithreading, asyncio, and then multiprocessing

[lawrencedoliveiro]

asyncio is for when the bottlenecks in your code lie in the I/O, the network, or in a GUI waiting for user responses, that kind of thing. Threads and processes are for handling CPU-intensive activities: you’re not going to take advantage of multiple CPUs without them.

Currently threads are mainly useful if the heavy work is being done in an extension module, since native Python code itself is hampered by the GIL. But this might improve in future, thanks to things like the “nogil” project.

[sENkyyy]

Highly appreciate your content. Thanks!
Those shutterstock snippets are more distracting than entertaining. I would leave it - simplify your edit and makes it better digestible for the viewer.

[Spitfire]

You should do a video on asyncio, subprocess, threading, and subinterpretors

[dharikarsath]

I don't understand asyncio, since it needs async def to be defined. since I have large code base with sync it becomes difficult to use asyncio.

[kayakMike]

I thought concurrency is a memory thing, not necessarily a parallel thread concept with multiple threads.

[utkarshsharma]

But switching between multiple processes isn’t that efficient in comparison to threads switching.
All this is coming at the cost of efficiency 🤔