It's kinda funny how uv is written in Rust and many Python libraries where performance is expected to matter (NumPy, Pandas, PyTorch, re, etc.) are implemented in C. Even if you call into fast code from Python you still have to contend with the GIL which I find very limiting for anything resembling performance.
Python's strong native story has always been one of its biggest draws: people find it ironic that so much of the Python ecosystem is native code, but it plays to Python's strength (native code where performance matters, Python for developer joy/ergonomics/velocity).
> Even if you call into fast code from Python you still have to contend with the GIL which I find very limiting for anything resembling performance.
It depends. A lot of native extension code can run without the GIL; the normal trick is to "detach" from the GIL for critical sections and only reconnect to it once Python needs to see your work. PyO3 has a nice collection of APIs for holding/releasing the GIL and for detaching from it entirely[1].
I didn't know about detaching from the GIL... I'll look into that.
> native code where performance matters, Python for developer joy/ergonomics/velocity
Makes sense, but I guess I just feel like you can eat your cake and have it too by using another language. Maybe in the past there was a serious argument to be made about the productivity benefits of Python, but I feel like that is becoming less and less the case. People may slow down (a lot) writing Rust for the first time, but I think that writing JavaScript or Groovy or something should be just as simple, but more performant, do multi-threading out of the box, and generally not require you to use other languages to implement performance critical sections as much. The primary advantage that Python has in my eyes is: there are a lot of libraries. The reason why there are a lot of libraries written in Python? I think it's because Python is the number 1 language taught to people that aren't specifically pursuing computer science / engineering or something in a closely related field.
Yes, I think Python is excellent evidence that developer ecosystems (libraries, etc.) are paramount. Developer ergonomics are important, but I think one of the most interesting lessons from the last decade is that popular languages/ecosystems will converge onto desirable ergonomics.
Python is the ultimate (for now) glue language. I'd much rather write a Python script to glue together a CLI utility & a C library with a remote database than try to do that all in C or Rust or BASH.
In my analysis, the lion's share of uv's performance improvement over pip is not due to being written in Rust. Pip just has horrible internal architecture that can't be readily fixed because of all the legacy cruft.
And for numerical stuff it's absolutely possible to completely trash performance by naively assuming that C/Rust/Fortran etc. will magically improve everything. I saw an example in a talk once where it superficially seemed obvious that the Rust code would implement a much more efficient (IIRC) binary search (at any rate, some sub-linear algorithm on an array), but making the data available to Rust; as a native Rust data structure, required O(N) serialization work.
> So they should be able to get similar results in Python then?
I'm making PAPER (https://github.com/zahlman/paper) which is intended to prove as much, while also filling some under-served niches (and ignoring or at least postponing some legacy features to stay small and simple). Although I procrastinated on it for a while and have recently been distracted with factoring out a dependency... I don't want to give too much detail until I have a reasonable Show HN ready.
But yeah, a big deal with uv is the caching it does. It can look up wheels by name and find already-unpacked data, which it hard-links into the target environment. Pip unpacks from the wheel each time (which also entails copying the data rather than doing fast filesystem operations, and its cache is an HTTP cache, which just intercepts the attempt to contact PyPI (or whatever other specified index).
Python offers access to hard links (on systems that support them) in the standard library. All the filesystem-related stuff is already implemented in C under the hood, and a lot of the remaining slowness of I/O is due to unavoidable system calls.
Another big deal is that when uv is asked to precompile .pyc files for the installation, it uses multiple cores. The standard library also has support for this (and, of course, all of the creation of .pyc files in CPython is done at the C level); it's somewhat naive, but can still get most of the benefit. Plus, for the most part the precompiled files are also eligible for caching, and last time I checked even uv didn't do that. (I would not be at all surprised to hear that it does now!)
> It totally depends on the problem that you're trying to solve.
My point was more that even when you have a reasonable problem, you have to be careful about how you interface to the compiled code. It's better to avoid "crossing the boundary" any more than absolutely necessary, which often means designing an API explicitly around batch requests. And even then your users will mess it up. See: explicit iteration over Numpy/Pandas data in a Python loop, iterative `putpixel` with PIL, any number of bad ways to use OpenGL bindings....
> explicit iteration over Numpy/Pandas data in a Python loop
Yeah, I get it. I see the same thing pretty often... The loop itself is slow in Python so you have APIs that do batch processing all in C. Eventually I think to myself, "All this glue code is really slowing down my C." haha
