msgspec
pydantic
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msgspec | pydantic | |
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31 | 166 | |
1,804 | 18,226 | |
- | 4.2% | |
8.9 | 9.8 | |
18 days ago | about 16 hours ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" 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.
msgspec
- Htmx, Rust and Shuttle: A New Rapid Prototyping Stack
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Litestar 2.0
Full support for validation and serialisation of attrs classes and msgspec Structs. Where previously only Pydantic models and types where supported, you can now mix and match any of these three libraries. In addition to this, adding support for another modelling library has been greatly simplified with the new plugin architecture
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FastAPI 0.100.0:Release Notes
> Maybe it was very slow before
That is at least partly the case. I maintain msgspec[1], another Python JSON validation library. Pydantic V1 was ~100x slower at encoding/decoding/validating JSON than msgspec, which was more a testament to Pydantic's performance issues than msgspec's speed. Pydantic V2 is definitely faster than V1, but it's still ~10x slower than msgspec, and up to 2x slower than other pure-python implementations like mashumaro.
Recent benchmark here: https://gist.github.com/jcrist/d62f450594164d284fbea957fd48b...
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Pydantic 2.0
While it's definitely much faster than pydantic V1 (which is a huge accomplishment!), it's still not exactly what I'd call "fast".
I maintain msgspec (https://github.com/jcrist/msgspec), a serialization/validation library which provides similar functionality to pydantic. Recent benchmarks of pydantic V2 against msgspec show msgspec is still 15-30x faster at JSON encoding, and 6-15x faster at JSON decoding/validating.
Benchmark (and conversation with Samuel) here: https://gist.github.com/jcrist/d62f450594164d284fbea957fd48b...
This is not to diminish the work of the pydantic team! For many users pydantic will be more than fast enough, and is definitely a more feature-filled tool. It's a good library, and people will be happy using it! But pydantic is not the only tool in this space, and rubbing some rust on it doesn't necessarily make it "fast".
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Need help developing a high performance Redis ORM for Python
https://github.com/jcrist/msgspec so I am using this instead of Pydantic.
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Blog post: Writing Python like it’s Rust
Another thing: why pyserde rather than stuff like msgspec? https://github.com/jcrist/msgspec
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[Guide] A Tour Through the Python Framework Galaxy: Discovering the Stars
Try msgspec | Maat | turbo for fast serialization and validation
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Pydantic V2 rewritten in Rust is 5-50x faster than Pydantic V1
Congratulations to the team, Pydantic is an amazing library.
If you find JSON serialization/deserialization a bottleneck, another interesting library (with much less features) for Python is msgspec: https://github.com/jcrist/msgspec
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Starlite updates March '22 | 2.0 is coming
This feature is yet to be released, but it will allow you to seamlessly use data modelled with for example Pydantic, SQLAlchemy, msgspec or dataclasses in your route handlers, without the need for an intermediary model; The conversion will be handled by the specific DTO "backend" implementation. This new paradigm also makes it trivial to add support for any such modelling library, by simply implementing an appropriate backend.
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
If you're primarily targeting Python as an application layer, you may also want to check out my msgspec library[1]. All the perf benefits of e.g. yyjson, but with schema validation like pydantic. It regularly benchmarks[2] as the fastest JSON library for Python. Much of the overhead of decoding JSON -> Python comes from the python layer, and msgspec employs every trick I know to minimize that overhead.
pydantic
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utype VS pydantic - a user suggested alternative
2 projects | 15 Feb 2024
utype is a concise alternative of pydantic with simplified parameters and usages, supporting both sync/async functions and generators parsing, and capable of using native logic operators to define logical types like AND/OR/NOT, also provides custom type parsing by register mechanism that supports libraries like pydantic, attrs and dataclasses
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🍹GinAI - Cocktails mixed with generative AI
The easiest implementation I found was to use a PyDantic class for my target schema — and use that as a parameter for the method call to “ChatCompletion.create()”. Here’s a fragment of the GinAI Python classes used.
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FastStream: Python's framework for Efficient Message Queue Handling
Also, FastStream uses Pydantic to parse input JSON-encoded data into Python objects, making it easy to work with structured data in your applications, so you can serialize your input messages just using type annotations.
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Introducing FastStream: the easiest way to write microservices for Apache Kafka and RabbitMQ in Python
Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validate incoming messages
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FastAPI 0.100.0:Release Notes
Well the performance increase is so huge because pydantic1 is really really slow. And for using rust, I'd have expected more tbh…
I've been benchmarking pydantic v2 against typedload (which I write) and despite the rust, it still manages to be slower than pure python in some benchmarks.
The ones on the website are still about comparing to v1 because v2 was not out yet at the time of the last release.
pydantic's author will refuse to benchmark any library that is faster (https://github.com/pydantic/pydantic/pull/3264 https://github.com/pydantic/pydantic/pull/1525 https://github.com/pydantic/pydantic/pull/1810) and keep boasting about amazing performances.
On pypy, v2 beta was really really really slow.
- Pydantic 2.0
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[DISCUSSION] What's your favorite Python library, and how has it helped you in your projects?
As for the most utilized and still loved library, that would probably be pydantic, it helps declaring types so convenient - be it dto's, models or just complex arguments - and plays nice with bunch of other libraries from it's own ecosystem.
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popularity behind pydantic
I did read this ... Pydantic Docs.
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Guide to Serverless & Lambda Testing — Part 2 — Testing Pyramid
Schema validations logic — I use Pydantic for input validation and schema validation (boto responses, API responses, input validation, etc.) use cases. The Pydantic schema can contain type and value constraint checks or even more complicated logic with the custom validator code.
What are some alternatives?
Cerberus - Lightweight, extensible data validation library for Python
nexe - 🎉 create a single executable out of your node.js apps
SQLAlchemy - The Database Toolkit for Python
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
mypy - Optional static typing for Python
pyparsing - Python library for creating PEG parsers [Moved to: https://github.com/pyparsing/pyparsing]
orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
phonenumbers - Python port of Google's libphonenumber
dacite - Simple creation of data classes from dictionaries.
Lark - Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.
beanie - Asynchronous Python ODM for MongoDB
beartype - Unbearably fast near-real-time hybrid runtime-static type-checking in pure Python.