Trafaret
pysimdjson
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| Trafaret | pysimdjson | |
|---|---|---|
| - | 6 | |
| 176 | 629 | |
| - | - | |
| 0.0 | 5.3 | |
| about 2 years ago | 3 months ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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Trafaret
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pysimdjson
- Analyzing multi-gigabyte JSON files locally
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I Use C When I Believe in Memory Safety
Its magic function wrapping comes at a cost, trading ease of use for runtime performance. When you have a single C++ function to call that will run for a "long" time, pybind all the way. But pysimdjson tends to call a single function very quickly, and the overhead of a single function call is orders of magnitude slower than with cython when being explit with types and signatures. Wrap a class in pybind11 and cython and compare the stack trace between the two, and the difference is startling.
Ex: https://github.com/TkTech/pysimdjson/issues/73
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Processing JSON 2.5x faster than simdjson with msgspec
simdjson
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[package-find] lsp-bridge
You are aware of simdjson being available in python if you really need some json crunching, albeit json module in Python is implemented in C itself, so I don't think understand why do you think Python is slow there?
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The fastest tool for querying large JSON files is written in Python (benchmark)
json: 113.79130696877837 ms
While `orjson`, is faster than `ujson`/`json` here, it's only ~6% faster (in this benchmark). `simdjson` and `msgspec` (my library, see https://jcristharif.com/msgspec/) are much faster due to them avoiding creating PyObjects for fields that are never used.
If spyql's query engine can determine the fields it will access statically before processing, you might find using `msgspec` for JSON gives a nice speedup (it'll also type check the JSON if you know the type of each field). If this information isn't known though, you may find using `pysimdjson` (https://pysimdjson.tkte.ch/) gives an easy speed boost, as it should be more of a drop-in for `orjson`.
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How I cut GTA Online loading times by 70%
I don't think JSON is really the problem - parsing 10MB of JSON is not so slow. For example, using Python's json.load takes about 800ms for a 47MB file on my system, using something like simdjson cuts that down to ~70ms.
What are some alternatives?
marshmallow - A lightweight library for converting complex objects to and from simple Python datatypes.
orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
serpy - ridiculously fast object serialization
cysimdjson - Very fast Python JSON parsing library
Fast JSON schema for Python - Fast JSON schema validator for Python.
ultrajson - Ultra fast JSON decoder and encoder written in C with Python bindings
lupin is a Python JSON object mapper - Python document object mapper (load python object from JSON and vice-versa)
PyValico - Small python wrapper around https://github.com/rustless/valico
pySHACL - A Python validator for SHACL