[nicolaslem]

As a developer working mostly with Python this comment makes no sense to me.

There are hundreds of libraries to deal with concurrency and/or parallelism in Python, asyncio, Celery and PySpark being the common ones.

All of them provide different approaches to concurrency because the language itself is not tight to one in particular.

[weberc2]

These are all quite a lot harder to use than Go, and often they don't play well together. For example, there are lots of sync libraries (the Docker API, the AWS SDK, etc) that can't be turned async, so other folks have had to go through the trouble of forking and porting to async and since those other folks are often not affiliated with the original dev teams, who knows what the quality level of those libraries may be? We've also had a lot of problems with asyncio alone--often developers forgetting to await an async call or doing something (I'm not sure what exactly) that causes processes to hang indefinitely. It's all quite a lot more complex than Go's concurrency model.

And all of that is really just I/O parallelization; there's also CPU parallelization, and I don't believe Python has anything that's quite as easy as "Do these two things in parallel". Pretty much everything requires a lot of marshalling and process management which can easily slow a program down instead of improving it.

Python is great for a lot of things, and the community has found many creative workarounds for its shortcomings, but Go beats Python in I/O and CPU parallelism handily.