rules_python

sorry, you're right and I didn't express my idea well > For python - https://github.com/aspect-build/rules_py parses a UV lockfile and does not use either UV or pip at build time. rules_python does for example call into piptools to do installation https://github.com/bazel-contrib/rules_python/blob/main/pyth... To be honest this stuff feels like it's gotten soooo much better in the years since I've used Bazel. (one can easily argue that it's been thanks to language ecosystems getting their things better) compared to when I've last had to use Bazel back in 2021-2022 (I had initially set up Bazel in ... 2018 for an existing Python project). So this might be a bit like relitigating Python 3.0. The lessons are learned perhaps! If I want to be very cynical... I think there is a universe where uv could have existed 5 years earlier if Google had

What's SV? I honestly don't know why anyone would use that... as in what does Bazel do better than virtually anything else that can provide this functionality. But, I used to be an ops engineer in a big company which wanted everything to be Maven, regardless of whether it does it well or not. So we built and deployed with Maven a lot of weird and unrelated stuff. Not impossible, but not anything I'd advise anyone to do on their free time. Specifically wrt' the link you posted, if you look here: https://github.com/bazelbuild/rules_python/blob/main/python/... it says that only pure Python wheels are supported, but that's also a lie, they don't support half of the functionality of pure Python wheels. So, definitely not worth using, since lots of functionality is simply not there.

Using Python with Bazel is fairly common at big SV companies -- they use rules_python with it (https://github.com/bazelbuild/rules_python). It does rely on pip for grabbing dependencies but handles building modules and can integrates well with rules_docker/rules_oci for building container images from your code.

Regarding bazel, the rules_python has a py_wheel rule that helps you creating wheels that you can upload to pypi (https://github.com/bazelbuild/rules_python/blob/52e14b78307a...). If you want to see an approach of bazel to pypi taken a bit to the extreme you can have a look at tensorflow on GitHub to see how they do it. They don't use the above-mentioned building rule because I think their build step is quite complicated (C/C++ stuff, Vida/ROCm support, python bindings, and multiOS support all in one before you can publish to pypi).

The setup instructions for almost [1] every [2] major [3] rule set [4] only provide one (GitHub) url in the Starlark blob you're supposed to copy and paste, so hard to blame users here. [1] https://github.com/bazelbuild/rules_jvm_external/releases/ta... [2] https://github.com/bazelbuild/rules_python/releases/tag/0.17... [3] https://github.com/bazelbuild/rules_java/releases/tag/5.4.0 [4] https://github.com/bazelbuild/rules_scala

Python support in Bazel now looks more promising with `rules_python`: https://github.com/bazelbuild/rules_python `rules_go` to my understanding is great too. Over years, Bazel is not as opinionated as before, mostly because adoptions in different orgs force it to be so.

I have personally run converted build systems to Bazel, and use it for personal projects as well. Bazel 1.0 was released in October 2019. If you were using it "a few years ago", I'm guessing you were using a pre-1.0 version. There's not some cutoff where Bazel magically got easy to use, and I still wouldn't describe it as "easy", but the problem it solves is hard to solve well, and the community support for Bazel has gotten a lot better over the past years. https://github.com/bazelbuild/rules_python The difficulty and complexity of using Bazel is highly variable. I've seen some projects where using Bazel is just super simple and easy, and some projects where using Bazel required a massive effort (custom toolchains and the like).