typedload
yyjson
| typedload | yyjson | |
|---|---|---|
| 5 | 5 | |
| 254 | 2,831 | |
| - | - | |
| 8.1 | 7.4 | |
| 7 days ago | 24 days ago | |
| Python | C | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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/
yyjson
- FLaNK Stack Weekly for 07August2023
- yyjson: A high performance C JSON library
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
How does yyjson[0] compare to simdjson? Their benchmarks suggest it could be a positive.
[0] https://github.com/ibireme/yyjson
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Why is my program segfaulting?
Also I am using these libraries: JSON: https://github.com/ibireme/yyjson Networking: https://curl.se/libcurl/
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How to parse JSON in C ?
If you need speed, by far yyjson. But it sounds like you probably don't need speed, so the other suggestions are likely better.
What are some alternatives?
codon - A high-performance, zero-overhead, extensible Python compiler using LLVM
json-c - https://github.com/json-c/json-c is the official code repository for json-c. See the wiki for release tarballs for download. API docs at http://json-c.github.io/json-c/
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 🗄️
cJSON - Ultralightweight JSON parser in ANSI C
pydantic-core - Core validation logic for pydantic written in rust
JSMN - Jsmn is a world fastest JSON parser/tokenizer. This is the official repo replacing the old one at Bitbucket
peps - Python Enhancement Proposals
parson - Lightweight JSON library written in C.
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
ultrajson - Ultra fast JSON decoder and encoder written in C with Python bindings
koda-validate - Typesafe, Composable Validation
gorilla-cli - LLMs for your CLI