typedload
msgspec
typedload | msgspec | |
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5 | 31 | |
254 | 1,868 | |
- | - | |
8.1 | 8.6 | |
7 days ago | about 1 month ago | |
Python | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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typedload
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
Author of typedload here!
FastAPI relies on (not so fast) pydantic, which is one of the slowest libraries in that category.
Don't expect to find such benchmarks on the pydantic documentation itself, but the competing libraries will have them.
[0] https://ltworf.github.io/typedload/
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Pydantic vs Protobuf vs Namedtuples vs Dataclasses
I wrote typedload, which is significantly faster than pydantic. Just uses normal dataclasses/attrs/NamedTuple, has a better API and is pure Python!
- Informatica serve a qualcosa?
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Show HN: Python framework is faster than Golang Fiber
I read all the perftests in the repo. I think they nearly all parse a structure that contains a repetition of the same or similar thing a couple hundred thousand times times and the timing function returns the min and max of 5 attempts. I just picked one example for posting.
Not a Python expert, but could the Pydantic tests be possibly not realistic and/or misleading because they are using kwargs in __init__ [1] to parse the object instead of calling the parse_obj class method [2]? According to some PEPs [3], isn't Python creating a new dictionary for that parameter which would be included in the timing? That would be unfortunate if that accounted for the difference.
Something else I think about is if a performance test doesn't produce a side effect that is checked, a smart compiler or runtime could optimize the whole benchmark away. Or too easy for the CPU to do branch prediction, etc. I think I recall that happening to me in Java in the past, but probably not happened here in Python.
[1] https://github.com/ltworf/typedload/blob/37c72837e0a8fd5f350...
[2] https://docs.pydantic.dev/usage/models/#helper-functions
[3] https://peps.python.org/pep-0692/
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...
[1]: https://github.com/jcrist/msgspec
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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
- Show HN: Msgspec, a fast serialization/validation library for Python
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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.
What are some alternatives?
codon - A high-performance, zero-overhead, extensible Python compiler using LLVM
pydantic - Data validation using Python type hints
ustore - Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️
orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
pydantic-core - Core validation logic for pydantic written in rust
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
peps - Python Enhancement Proposals
mashumaro - Fast and well tested serialization library
koda-validate - Typesafe, Composable Validation
MessagePack - MessagePack serializer implementation for Java / msgpack.org[Java]
socketify.py - Bringing Http/Https and WebSockets High Performance servers for PyPy3 and Python3
marshmallow - A lightweight library for converting complex objects to and from simple Python datatypes.