MongoDB with Async Python Transcripts
Chapter: Foundations: Pydantic
Lecture: Built on Pydantic
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Let's survey the landscape of things that use Pydantic. Pydantic is awesome on its own and you can use it in even say web frameworks that have
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no idea what Pydantic is and have no dependency on it. Like a Flask website that exchanges JSON, you could manually use Pydantic.
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But there are many frameworks out there that are Pydantic at their core. So let's talk about a couple of those.
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We have Beanie, obviously one of the centerpieces of this course, right? That's how we're modeling our data in MongoDB and doing our queries.
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All of those models are based on Pydantic. So Pydantic is at the very center of working with MongoDB using Beanie.
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We also have in the relational side, we're not going to use in this course for sure because MongoDB is not a RDBMS.
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We have SQL model and SQL model is basically take SQL alchemy and replace the SQL alchemy models with Pydantic models and then that's more or less it.
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But really really cool that you have these nice Pydantic models to model your data in relational databases such as Postgres or SQLite.
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This is also created by Sebastian Ramirez, the creator of FastAPI. Speaking of FastAPI, FastAPI is the most well known use case for pydantic.
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And it plugs right into the API data exchange in super clever ways.
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Here is a pydantic class modeling an item, I guess, and that's what we're calling it as a name, a description, a price and a tax.
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We're going to get into this, but the name is a string and the price is a float and they
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have to be a float or parsable as a float in the data exchange for this to be valid.
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If we want to say we're going to have an API endpoint, and that API endpoint accepts a JSON
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post body or something like that, that has a name, description, price and tax, and we want to
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automatically convert that, well, all we have to do is create a function here we have create item,
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and it's just a post operation to slash items. And look in the parameter here, it just says
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we take an item of type item and FastAPI will say I see what's coming in. This is the pedantic
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model. And we're going to use that pedantic model to parse and validate the JSON coming in.
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So FastAPI, absolutely pedantic at the core. There's many other uses for the pedantic models
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here as well, such as response model for open API documentation, but not a class on fast
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API. So we're not getting into that. But right here, specifying the type as a pedantic model
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is not just a hint to the editor, it changes how FastAPI processes the response that comes
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to it. That was a bunch of awesome projects built on top of pedantic, right? Well, let's
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Let's look at an awesome list to round out this small section here. Over on GitHub, we can find github.com/cludexawesome-pydantic.
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It's a curated list of awesome things related to, well, Pydantic.
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You can scroll through here and there's things on machine learning that are based on Pydantic. You can go down here and locate.
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There's Beanie, but also Piccolo, which is an async query builder in ORM, which can auto-generate
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Pydantic models from database tables, or gigantic Pydantic model support for Django or HTTP
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runner, which is a HTTP testing framework, strawberry graph, and so on.
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I'm not going to go through this whole list, just kind of scroll through it here. There's a bunch more awesome things to find around Pydantic.
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And if you know one that's not on the list, do these folks a favor and submit a PR.