pysimdjson VS marshmallow

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

simdjson

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?

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`.

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.

Somehow get a serializer going for your database models. I used marshmallow and flask-marshmallow

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.

Either using meta programming: https://github.com/marshmallow-code/marshmallow/issues/585

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 was looking at https://github.com/marshmallow-code/marshmallow That's a nice library to use to parsing?