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Ask HN
Ask HN: Whose building on Python NoGIL?
I am interested in knowing the things the community is building specifically taking into account the NoGIL aspect of python. Like is someone building frameworks around using Threads instead of Async?
PyO3 0.23.0 was a big release I’ve been tinkering with extensively. Support for “free-threaded Python” is a headline feature, and I imagine NoGIL Python will be extremely nice for Rust interoperability, so there is definitely interest in that crate. Also could be huge for queueing data for GPUs, api servers, and bulk data fetching.

For whatever reason (maybe post 2to3 PTSD), Python community seems not extremely eager to jump on latest versions of Python and it often takes a long time for popular libraries to support the latest and greatest, so I’d recommend patience and baby steps

https://github.com/PyO3/pyo3/releases/tag/v0.23.0

I've always used threads despite the GIL. I haven't tried NoGIL and am waiting to find out how many bugs it surfaces. I do get the impression that multi-threaded Python code is full of hazards that the GIL covers up. There will have to be locks inserted all over the place. CPython should have simply been retired as part of the 2 to 3 transition. It was great in its day, on 1-core machines with the constraints of that era. I have a feeling of tragedy that this didn't happen and now it can never be repaired. I probably wouldn't use Python for web projects these days. I haven't done anything in Elixir yet but it looks like about the best option. (I've used Erlang so I think I have a decent idea of what I'd be getting into with Elixir).
  • tgma
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  • 1 day ago
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In a strange way, Python being so bad at interpreting bytecodes and limited by GIL, plus being good at interfacing with C cheaply (unlike Go and Java,) induced a programming style that is extremely suited for data-parallel computing which is the way to efficiently scale compute in today's SIMD/GPU world. If you wanted to be efficient, you had to prepare your data ahead of time and hand it off. Any intermediate interaction with that data would ruin your performance. That's mostly how efficient Python libraries and ecosystem are built.

Weakness may have turned into a strength.

Can you elaborate more on the data-parallel computing part?
  • tgma
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  • 21 hours ago
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Pretty much the entire Data Science/Machine Learning landscape from numpy, etc. to tensorflow and alike are thin wrappers over C code and if you want performance, you better batch structure your operation beforehand and minimize back and forth from Python.
He's probably talking about libraries like PySpark or PyFlink which are used a lot
So what would the alternative history have been if CPython was retired after Python 3 came out in 2008, what would we be using now? IronPython or GraalPy?
I have the same question! I love Python and asynchronous stuff, and I do not know too much about threading.

Is threading potentially better for IO bound tasks than async?

Potentially, but probably not. The benefit of a parallel-enabled interpreter would be two CPU cores executing bytecode instructions at the same time in the same interpreter. So, you could have one python thread working on one set of data, and another python thread working on another set of data, and the two threads would not interfere with each other much or at all. Today, with the global interpreter lock, only one of those threads can be executing bytecode at a time.
> Today, with the global interpreter lock, only one of those threads can be executing bytecode at a time.

Yes, but Python now has a version without GIL, which prompted this post in the first place. So my question is: Now, if I use a version of Python 3.13 without GIL, can a threaded Flask app do better than an AIOHTTP server.

  • tgma
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  • 1 day ago
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Started working on a no-gil gRPC Python implementation but super low priority.

Has anyone started deploying nogil at scale in prod?

No, I am not personally aware of anyone using it prod.