Usually, but when it isn't then you've got a bottleneck. Multithreaded performance is a major weak point if you need to do any processing that isn't handled by one of the libraries.

[–] [email protected] 2 points 4 months ago* (last edited 4 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.

[–] [email protected] 3 points 4 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.

[–] [email protected] 2 points 4 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.