typedload VS json_benchmark

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/

I wrote typedload, which is significantly faster than pydantic. Just uses normal dataclasses/attrs/NamedTuple, has a better API and is pure Python!

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/

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.

[1]: https://github.com/jcrist/msgspec

[2]: https://github.com/TkTech/json_benchmark

- Adding nvme drive support to SMARTie, https://github.com/tktech/smartie, which is a pure-python cross-platform library for getting disk information like serial number, SMART attributes (like disk temperature) - json_benchmark, https://github.com/tktech/json_benchmark, which is a new benchmark and correctness test for the more modern Python JSON libraries - py_yyjson, https://github.com/tktech/py_yyjson, which is still a WIP and provides Python bindings to the yyjson library, which offers comparable speed to simdjson but more flexibility when parsing (comments, arbitrary sized numbers, Inf/Nan, etc) - And some fixes to https://github.com/TkTech/humanmark, which is a markdown library used to edit the README.md in json_benchmark above.