I'm definitely interested in a simple type-safe validator library, but that planned performance boost is pretty tempting.
- I haven't benchmarked this against pydantic... I guess I should!
- I would _hope_ that Koda Validate is at least competitive with Pydantic... because Koda Validate's main core improvement (IMO) is a consistent idea of what a validator is, validators in Koda Validate are actually much simpler than in Pydantic, meaning I would generally expect fewer instructions to be executed
- 3.11 (and 3.12) are both focused on performance. Both pydantic and Koda Validate might get some "free" performance boosts anyway (Thanks CPython devs!)
- it is typesafe without any plugins or type-hint hacks
- exceptions are not raised as part of validation
- there is a consistent, type-enforced notion of what a validator is
- it's explicit, no implicit type coercions
- it's meant to be easier to build complex validators by combining validators
- validators are meant to be easily inspectable, meaning you can create things like API schemas from them
- errors are json/yaml serializable, so if needed they can be passed over the wire directly (or modified, if desired)
One thing that occurs to me: I don't want to have to describe my data in two places. Like how in the first example the dataclass Person has all the information needed to define the person_validator.
I'm probably just going to use pydantic though.
