typedload VS socketify.py

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/

Try BlackSheep | Kore | socketify | baize

When I see that: https://github.com/cirospaciari/socketify.py/blob/main/bench...

It's kind of hopeless, Python still needs to fork per core to get any performance? So if you have 8 cores you're actually running 8 processes, so 8 DB pool etc ...