marshmallow VS ultrajson

Uses a robust data validation library: validating payloads is a complex business. Your data validation library must handle optional and required properties, string formats like ISO dates and UUIDs (both dates and UUIDs are string types in OpenAPI), and strict vs loose type validation (should a string pass as an integer if it can be casted?). Also, in the case of Python, you need to make sure 1 and 0 don’t pass for True and False when it comes to boolean properties. In my experience, the best data validation libraries in the Python ecosystem are pydantic and marshmallow. From the above-mentioned libraries, flasgger and flask-smorest work with marshmallow.

I also came across things like Marsmallow and Blueprints, but don't know what these are, still reading about this as I write.

Pydantic is a data validation library, marshmallow is a data validation library. None of the other libraries in the list of pydantic alternatives is a data validation library.

I have been working on a package that is very similar in concept to marshmallow (https://marshmallow.readthedocs.io), but which adds a versioning mechanism to track changes in object structure across time, allowing you to migrate objects between different versions.

You might be surprised how much I agree - I recently opened an issue there hoping to discuss something like this (still awaiting response). https://github.com/marshmallow-code/marshmallow/issues/2000

Marshmallow

I created a shallow fork of everyone's favourite marshmallow, to work around some performance issues while dumping data. The performance gain I measured is around 45%, but since it's a bad idea to rely on one's own testing, I was hoping that there are some folks here who use marshmallow in their projects, and who would be willing to try it out. Doubly so if your project has some unit tests in it, to confirm that nothing is broken due to my patches.

I found a recommendation to use marshmallow to parse dict generated from JSON object. I decided that these cases are the same as mine only uses JSON instead of YAML. And so I tried to use class_schema generator for dataclass schema:

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