pydantic-core
cannoli
Our great sponsors
| pydantic-core | cannoli | |
|---|---|---|
| 18 | 1 | |
| 1,257 | 771 | |
| 2.1% | - | |
| 9.6 | 10.0 | |
| 5 days ago | over 5 years ago | |
| Python | Rust | |
| MIT License | 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.
pydantic-core
-
Is there a pydantic.BaseSettings equivalent in rust?
Funny that you ask... https://github.com/pydantic/pydantic-core Unfortunately it seems that the functionality you ask for is not (yet) part of this ...
-
Investigating Pydantic v2's Bold Performance Claims
I encourage you to checkout the official benchmarks for more realistic and detailed examples, and, as always, YMMV.
-
Pydantic V2 leverages Rust's Superpowers [video]
> 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...
-
Pydantic vs Protobuf vs Namedtuples vs Dataclasses
Thanks for pointing out to that, I did not know about it. Also attaching repo in case someone would be interested as well - https://github.com/pydantic/pydantic-core
-
Introducing CodSpeed: Continuous Performance Measurement
pydantic-core: The core validation logic for pydantic, a Python data parsing and validation library.
-
Show HN: Python framework is faster than Golang Fiber
pydandic-core [0] will hopefully solve this issue (written in Rust)
- Scala or Rust? which one will rule in future?
-
Pydantic 2 rewritten in Rust was merged
Here's the Rust library: https://github.com/pydantic/pydantic-core
cannoli
-
Modern Python Performance Considerations
This is a great read, and it's fantastic to see all the work being done to evaluate and improve the language!
Shameless plug alert: the dynamic-nature of the language is actually something that I had studied a few years back [1]. Particularly the variable and object attribute look ups! My work was just a master's thesis, so we didn't go too deep into more tricky dynamic aspects of the language (e.g. eval, which we restricted entirely). But we did see performance improvements by restricting the language in certain ways that aid in static analysis, which allowed for more performant runtime code. But for those interested, the abstract of my thesis [2] gives more insight into what we were evaluating.
Our results showed that restricting dynamic code (code that is constructed at run time from other source code) and dynamic objects (mutation of the structure of classes and objects at run time) significantly improved the performance of our benchmarks.
What are some alternatives?
aiohttp-apispec - Build and document REST APIs with aiohttp and apispec
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
pymartini - A Cython port of Martini for fast RTIN terrain mesh generation
koda-validate - Typesafe, Composable Validation
typedload - Python library to load dynamically typed data into statically typed data structures
uvloop - Ultra fast asyncio event loop.
modin - Modin: Scale your Pandas workflows by changing a single line of code
pytest-benchmark - py.test fixture for benchmarking code
pydantic - Data validation using Python type hints
pytype - A static type analyzer for Python code
gnet - 🚀 gnet is a high-performance, lightweight, non-blocking, event-driven networking framework written in pure Go./ gnet 是一个高性能、轻量级、非阻塞的事件驱动 Go 网络框架。