ultrajson VS orjson

ujson

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

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

orjson is the fastest JSON library available for python. It natively manages dataclass objects, datetime, numpy and UUID objects.

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.

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.

Doesn't orjson do that already?

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

Try https://github.com/ijl/orjson. Not exactly sure if that can help.