this post was submitted on 15 Jun 2024
78 points (91.5% liked)

Python

6375 readers
54 users here now

Welcome to the Python community on the programming.dev Lemmy instance!

πŸ“… Events

PastNovember 2023

October 2023

July 2023

August 2023

September 2023

🐍 Python project:
πŸ’“ Python Community:
✨ Python Ecosystem:
🌌 Fediverse
Communities
Projects
Feeds

founded 1 year ago
MODERATORS
erlingur@programming.dev
jnovinger@programming.dev
norambna@programming.dev
78
Why Python Is So Slow (And What Is Being Done About It) (thenewstack.io)
submitted 5 months ago by dominiquec@lemmy.world to c/python@programming.dev
47 comments fedilink hide all child comments
you are viewing a single comment's thread
view the rest of the comments
[–] sugar_in_your_tea@sh.itjust.works 2 points 5 months ago* (last edited 5 months ago) (1 children)

Then you need to break up your problem into processes. Python doesn't really do multi-threading (hopefully that changes with the GIL going away), but most things can scale reasonably well in a process pool if you manage the worker queue properly (e.g. RabbitMQ works well).

It's not as good as proper threadimg, but it's a lot simpler and easier to scale horizontally. You can later rewrite certain parts if hosting costs become a larger issue than dev costs.

[–] hark@lemmy.world 3 points 5 months ago (1 children)

A process pool means extra copying of data around which incurs a huge cost and this is made worse by the tendency for parallel-processing-friendly workloads often consisting of large amounts of data.

[–] sugar_in_your_tea@sh.itjust.works 2 points 5 months ago

Yup, which is why you should try to limit the copying by designing your parallel processing algorithm around it. If you can't, you would handle threading with a native library or something and scale vertical instead of horizontal. Or pick a different language if it's a huge part of your app.

But in a lot of cases, it's reasonable to stick with Python and scale horizontally. That has value if you're otherwise a Python shop.