mypy-test
typedload
mypy-test | typedload | |
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6 | 5 | |
5 | 255 | |
- | - | |
3.7 | 8.1 | |
7 months ago | 21 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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mypy-test
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/
What are some alternatives?
refurb - A tool for refurbishing and modernizing Python codebases
codon - A high-performance, zero-overhead, extensible Python compiler using LLVM
infer-types - A CLI tool to automatically add type annotations into Python code. Must have tool for annotating existing code.
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 🗄️
Deal - 🤝 Design by contract for Python. Write bug-free code. Add a few decorators, get static analysis and tests for free.
pydantic-core - Core validation logic for pydantic written in rust
phantom-types - Phantom types for Python.
peps - Python Enhancement Proposals
sqlalchemy-stubs - Mypy plugin and stubs for SQLAlchemy
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
koda-validate - Typesafe, Composable Validation
socketify.py - Bringing Http/Https and WebSockets High Performance servers for PyPy3 and Python3