typedload
socketify.py
typedload | socketify.py | |
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5 | 38 | |
254 | 1,274 | |
- | - | |
8.1 | 7.7 | |
7 days ago | 3 months ago | |
Python | Python | |
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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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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/
socketify.py
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[Guide] A Tour Through the Python Framework Galaxy: Discovering the Stars
Try BlackSheep | Kore | socketify | baize
- With this, you can outperform Golang Fiber with Python
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Show HN: Python framework is faster than Golang Fiber
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 ...
- Adding better DX to the fastest Python WebFramework
- Adding better DX to my package
- This is how I started the development of the fastest ASGI and WSGI Server in TechEmPower Benchmarks
What are some alternatives?
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BlackSheep - Fast ASGI web framework for Python
pydantic-core - Core validation logic for pydantic written in rust
Robyn - Robyn is a Super Fast Async Python Web Framework with a Rust runtime.
peps - Python Enhancement Proposals
japronto - Screaming-fast Python 3.5+ HTTP toolkit integrated with pipelining HTTP server based on uvloop and picohttpparser.
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koda-validate - Typesafe, Composable Validation
vibora - Fast, asynchronous and elegant Python web framework.