jsplit
typedload
jsplit | typedload | |
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2 | 5 | |
59 | 255 | |
- | - | |
10.0 | 8.1 | |
over 1 year ago | 26 days ago | |
Go | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
jsplit
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
Regarding the hard way, this little utility does a great job of splitting larger than memory JSON documents into collections of NDJSON files:
https://github.com/dolthub/jsplit
- [OC] The ridiculously absurd amount of pricing data that insurance companies just publicly dumped
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?
data-analysis
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json_benchmark - Python JSON benchmarking and "correctness".
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
json-buffet
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
price-transparency-guide - The technical implementation guide for the tri-departmental price transparency rule.
koda-validate - Typesafe, Composable Validation