pydantic-core VS fastapi

Funny that you ask... https://github.com/pydantic/pydantic-core Unfortunately it seems that the functionality you ask for is not (yet) part of this ...

I encourage you to checkout the official benchmarks for more realistic and detailed examples, and, as always, YMMV.

> to also be constrained by a separate set of data types which are legal in rust.

This isn't really how writing rust/python iterop works. You tend to have opaque handles you call python methods on. Here's a decent example I found skimming the code.

https://github.com/pydantic/pydantic-core/blob/main/src/inpu...

Thanks for pointing out to that, I did not know about it. Also attaching repo in case someone would be interested as well - https://github.com/pydantic/pydantic-core

pydantic-core: The core validation logic for pydantic, a Python data parsing and validation library.

pydandic-core [0] will hopefully solve this issue (written in Rust)

[0] -- https://github.com/pydantic/pydantic-core

LinkedIn influencers are weird lol. Rust v Python is apples and oranges. Rust would be glued together by python just like it does with C/C++ and Java/Spark today. We’re already seeing some packages go this direction, like pydantic v2 is rewriting its core validation in rust.

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.

We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include:

FastAPI is a modern, fast, web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, fast to run, and secure. In this blog post, we’ll explore the key features of FastAPI and walk through the process of creating a simple API using this powerful framework.

FastAPI is a modern, fast web framework for building APIs with Python 3.7+ that automatically generates OpenAPI and JSON Schema documentation. While FastAPI simplifies API development, manually creating and updating API documentation can still be a time-consuming task. In this blog post, we’ll explore how to leverage FastAPI’s automatic documentation generation capabilities, specifically focusing on Swagger and ReDoc, and how to streamline the process of documenting your APIs.

TiTiler is a dynamic tile server built on FastAPI and Rasterio/GDAL. Its main features include support for Cloud Optimized GeoTIFF(COG), multiple projection methods, various output formats (JPEG, JP2, PNG, WEBP, GTIFF, NumpyTile), WMTS, and virtual mosaic. It also provides Lambda and ECS deployment environments using AWS CDK.

To make it a bit more realistic, we’re going to use a FastAPI route as an example, and we’re also going to use FastAPI’s dependency injection, which can really help with readability (and testability, but more on that later).

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