orjson
ultrajson
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orjson | ultrajson | |
---|---|---|
17 | 3 | |
5,540 | 4,244 | |
- | 0.7% | |
8.3 | 7.0 | |
4 days ago | 22 days ago | |
Python | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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orjson
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
orjson is the fastest JSON library available for python. It natively manages dataclass objects, datetime, numpy and UUID objects.
- Segunda linguagem
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Litestar 2.0
As we began venturing down that road, a few things emerged that would constitute significant changes to some of the core parts of Litestar, but there were two things in particular that started a chain reaction of changes by opening up further possibilities: The new DTOs and our switch from orjson to msgspec.
- orjson: Fast, correct Python JSON lib (supports dataclasses, datetimes, numpy)
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Starlite development updates January ’23
In version 1.45.0, we introduced msgspec as our serialization backend, replacing orjson. This had some immediate performance benefits, but that's not the main reason we made the switch.
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Making Python classes serializable to/from JSON
Doesn't orjson do that already?
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Processing JSON 2.5x faster than simdjson with msgspec
orjson
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Benchmarking Python JSON serializers - json vs ujson vs orjson
For most cases, you would want to go with python’s standard json library which removes dependencies on other libraries. On other hand you could try out ujsonwhich is simple replacement for python’s json library. If you want more speed and also want dataclass, datetime, numpy, and UUID instances and you are ready to deal with more complex code, then you can try your hands on orjson
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Json.dump new line with multiple values in key value pair.
Try https://github.com/ijl/orjson. Not exactly sure if that can help.
ultrajson
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Processing JSON 2.5x faster than simdjson with msgspec
ujson
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Benchmarking Python JSON serializers - json vs ujson vs orjson
For most cases, you would want to go with python’s standard json library which removes dependencies on other libraries. On other hand you could try out ujsonwhich is simple replacement for python’s json library. If you want more speed and also want dataclass, datetime, numpy, and UUID instances and you are ready to deal with more complex code, then you can try your hands on orjson
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The fastest tool for querying large JSON files is written in Python (benchmark)
I asked about this on the Github issue regarding these benchmarks as well.
I'm curious as to why libraries like ultrajson[0] and orjson[1] weren't explored. They aren't command line tools, but neither is pandas right? Is it perhaps because the code required to implement the challenges is large enough that they are considered too inconvenient to use through the same way pandas was used (ie, `python -c "..."`)?
[0] https://github.com/ultrajson/ultrajson
What are some alternatives?
ujson
marshmallow - A lightweight library for converting complex objects to and from simple Python datatypes.
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
greenpass-covid19-qrcode-decoder - An easy tool for decoding Green Pass Covid-19 QrCode
ormsgpack - Msgpack serialization/deserialization library for Python, written in Rust using PyO3 and rust-msgpack. Reboot of orjson. msgpack.org[Python]
Fast JSON schema for Python - Fast JSON schema validator for Python.
pysimdjson - Python bindings for the simdjson project.
python-rapidjson - Python wrapper around rapidjson
cookiecutter-fastapi-firestore
PyLD - JSON-LD processor written in Python
mashumaro - Fast and well tested serialization library