pydantic-core

> to also be constrained by a separate set of data types which are legal in rust. This isn't really how writing rust/python iterop works. You tend to have opaque handles you call python methods on. Here's a decent example I found skimming the code. https://github.com/pydantic/pydantic-core/blob/main/src/inpu...

Samuel also presented a slightly altered talk at PyCon US yesterday[0], which was awesome! GitHub link to pydantic[1], and pydantic-core[2]. [0]: https://twitter.com/samuel_colvin/status/1649928041462915072 (slides in comments) [1]: https://github.com/pydantic/pydantic [2]: https://github.com/pydantic/pydantic-core

The Rust re-write is being worked on in a separate repo: https://github.com/pydantic/pydantic-core

In recent years Python ecosystem evolved significantly. From using requirements.txt to dependency management tools like Poetry, from simple typing to Protocols [1] and Variadic Generics [2]. We've got walrus operator, match case statement, and finally even faster Python. More and more low-level libraries are being written in Rust [3]. What new features, tools, libraries (already implemented or being worked on) are the most useful for your work with Python? What are you the most excited about? [1] https://peps.python.org/pep-0544/ [2] https://peps.python.org/pep-0646/ [3] https://github.com/pydantic/pydantic-core

pydandic-core [0] will hopefully solve this issue (written in Rust) [0] -- https://github.com/pydantic/pydantic-core

The Rust is in the `pydantic-core` repository here: https://github.com/pydantic/pydantic-core

I read more about your repo, and it looks like typedload only does Python lists/dicts -> typed python objects(like named tuples, data classes, attrs classes, etc). Ie, the benchmarks don't include the time required to parse the JSON. So I modified the benchmarking code to include loading JSON[0], and your library still came out on top! But, it turns out I was wrong about pydantic using serde under the hood. Pydantic version 2 will. And the maintainer aims for it to be about 10x faster [1] than version 1. Nonetheless, this was definitely a surprise for me, and if I ever go back to using Python, I'll definitely check your library out! I'm curious, why do you think pydantic took off and your library didn't? It looks like your library is both faster and easier to use to me. [0] https://github.com/davidatsurge/typedload/pull/1/files?diff=...

This pales in comparison to Pydantic in terms elegance, flexibility and batteries being included. But pydantic is quite slow. I discovered pydantic-core [1] which is a backend of sorts for pydantic written in Rust. It is yet to be integrated into pydantic, but once the integration is done, the author expects a potential 10x speed improvement. [1] https://github.com/samuelcolvin/pydantic-core

Pydantic is quite popular library. Its author is doing exactly this - rewriting its core [0] in Rust. It's still WIP, but readme mentions that "Pydantic-core is currently around 17x faster than Pydantic Standard." [0] https://github.com/samuelcolvin/pydantic-core