typedload
data-analysis
| typedload | data-analysis | |
|---|---|---|
| 5 | 6 | |
| 254 | 44 | |
| - | - | |
| 8.1 | 7.3 | |
| 7 days ago | 10 months ago | |
| Python | Jupyter Notebook | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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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/
data-analysis
- Why a public database of hospital prices doesn't exist yet
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Open Database of Hospital Prices
https://github.com/dolthub/data-analysis/tree/main/transpare...
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
Absolutely interested, on my end at least. I wrote this to manage the transparency in coverage files: https://github.com/dolthub/data-analysis/tree/main/transpare... but I'm always looking for better techniques.
Oh wow, I see you used it on those exact files. How about that.
- Healthcare datasets with multiple continuous variables
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Beyond the trillion prices: pricing C-sections in America
Details: data repository, code repository, and notebook. The linked GitHub repo gives you the tools you need to reproduce this analysis or create your own.
- I wrote some tools to find the prices of C-sections in America. Context in README
What are some alternatives?
codon - A high-performance, zero-overhead, extensible Python compiler using LLVM
json_benchmark - Python JSON benchmarking and "correctness".
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 🗄️
synthea - Synthetic Patient Population Simulator
pydantic-core - Core validation logic for pydantic written in rust
simdjson-go - Golang port of simdjson: parsing gigabytes of JSON per second
peps - Python Enhancement Proposals
jsplit - A Go program to split large JSON files into many jsonl files
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
japronto - Screaming-fast Python 3.5+ HTTP toolkit integrated with pipelining HTTP server based on uvloop and picohttpparser.
koda-validate - Typesafe, Composable Validation