rules_python
pyparsing
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rules_python | pyparsing | |
---|---|---|
7 | 0 | |
493 | 1,000 | |
2.2% | - | |
9.5 | 7.3 | |
6 days ago | about 3 years ago | |
Starlark | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
rules_python
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Things I've learned about building CLI tools in Python
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.
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.
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Build faster with Buck2: Our open source build system
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).
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Incremental Builds for Haskell with Bazel
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.
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Advantages of Monorepos
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).
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Experimentations on Bazel: Python & FastAPI (1)
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", )
pyparsing
We haven't tracked posts mentioning pyparsing yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
Lark - Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.
PLY - Python Lex-Yacc
pydantic - Data validation using Python type hints
sqlparse - A non-validating SQL parser module for Python
Pygments
python-user-agents - A Python library that provides an easy way to identify devices like mobile phones, tablets and their capabilities by parsing (browser) user agent strings.
Python Left-Right Parser - Python Parser
Construct - Construct: Declarative data structures for python that allow symmetric parsing and building
python-nameparser - A simple Python module for parsing human names into their individual components
simplematch - Minimal, super readable string pattern matching for python.
phonenumbers - Python port of Google's libphonenumber
Atoma - Atom, RSS and JSON feed parser for Python 3