rules_cc VS rules_python

Not that I'd recommend it, but if you symlink your system library into the bazel build area, as long as your sandboxing setup don't hose you (or you just turn it off), bazel will track system tools/library in the same way as everything else.

Bazel's rules_cc even has a system_library.bzl you can import a `system_library` from that automates this for you. https://github.com/bazelbuild/rules_cc/blob/main/cc/system_l...

I'd still recommend building everything from scratch (and understanding the relationships and graph of your dependencies), but if your build isn't that complicated and you want to role the dice on UB, this isn't that hard.

As an aside, the most galling part of bazel's cache key calculations has to be that it's up to the individual rules to implement this how they see fit. The rules native to bazel written in java vary wildly compared to starlark-written rules. On thing you (or someone in your org) end up becoming pretty comfortable with while using bazel in anger is RTFC.

I found several associated issues and a closed PR that supposedly solved this issue. However, it seems that cc_rules has undergone several changes and this PR likely wouldn't function.

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.

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).

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).

load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") #------------------------------------------------------------------------------ # Python #------------------------------------------------------------------------------ # enable python rules http_archive( name = "rules_python", url = "https://github.com/bazelbuild/rules_python/releases/download/0.2.0/rules_python-0.2.0.tar.gz", sha256 = "778197e26c5fbeb07ac2a2c5ae405b30f6cb7ad1f5510ea6fdac03bded96cc6f", )