Disclosure: Meta employee, but these views are my own, and not all the experiences are from Meta. Type annotation have made Python much more scalable in terms

Disclosure: Meta employee, but these views are my own, and not all the experiences are from Meta. Type annotation have made Python much more scalable in terms of engineers and codebase size. It still has other scale problems, especially if you actually need threads. One project I worked on managed Python worker tasks, and we resorted to subprocesses (within subprocesses!) because what we thought was IO-bound became CPU-bound, and workers started timing out on RPC calls. I also worked on a Python API service that scaled beautifully horizontally, but we had to manage extra logic for spinning up one worker per CPU. At some point, you actually start caring about performance, but you're more likely to hit other issues before you care about the extra hardware cost.