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Parse, Don't Validate (2019)

(lexi-lambda.github.io)
389 points melse | 1 comments | 26 Jun 21 07:23 UTC | HN request time: 0.247s | source
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kortex ◴[26 Jun 21 14:15 UTC] No.27642049[source]▶
This principle is how pydantic[0] utterly revolutionized my python development experience. I went from constantly having to test functions in repls, writing tons of validation boilerplate, and still getting TypeErrors and NoneTypeErrors and AttributeErrors left and right to like...just writing code. And it working! Like one time I wrote a few hundred lines of python over the course of a day and then just ran it... and it worked. I just sat there shocked, waiting for the inevitable crash and traceback to dive in and fix something, but it never came. In Python! Incredible.

[0] https://pydantic-docs.helpmanual.io/

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JPKab ◴[26 Jun 21 16:49 UTC] No.27643474[source]▶
Curious, but how does pydantic compare to marshmallow?

I'm currently using marshmallow in a project, specifically using the functionality that builds parsers from dataclasses.

I was curious what the differences were.

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1. kortex ◴[27 Jun 21 14:55 UTC] No.27652057[source]▶
Personal opinion: pydantic crushes Marshmallow. Not even a fair fight. Pydantic is more performant, has better mypy/linter integration, and more powerful data model. We had a project where we pre-emptively used marshmallow to marshall/validate data. Had to remove that and solely use it at the ORM layer because of performance (and it still struggles).

I haven't used pydantic's ORM integration, but I don't hesitate to use pydantic models everywhere as business logic classes unless I need ludicrous speed.

That's all opinion, but I'd definitely give pydantic a swing.