How I Tried To Reduce Pylint Memory Usage
Through work I have to deal with a pretty huge codebase, and run Pylint on it during our continuous integration process. It's always been a bit slow, but recently I also noticed that it was consuming a lot of memory, causing OOM failures if we tried to run it too much in parallel.
I decided to roll up my sleeves and figure out:
- What exactly was consuming so much memory?
- Is there a way to avoid doing this?
This is a log of how I went about these two questions, as future reference for when I do other profiling work in Python.
Source: How I Tried To Reduce Pylint Memory Usage, an article by Raphael Gaschignard.