graph_wrap VS fastapi

This doesn't directly answer the question, but I wrote a library which uses meta programming to build a GraphQL Graphene API from a DRF API https://github.com/PaulGilmartin/graph_wrap. Maybe it's the type of thing which would interest you.

Enter GraphQL: GraphQL is designed so that the client decides what info it receives from the server, not the other way around. Whilst many great packages exist to create a GraphQL API from scratch, migrating an mature production REST API to use one of these frameworks is not so simple. It may also be that our REST API has functionality which is not available on a GraphQL specific API. This is where GrapWrap comes in: by adding two lines of code to your project, GraphWrap exposes a GraphQL schema which has the same "shape" as your existing REST API. With this new endpoint, we can now stop overexposing the author fields and instead simply expose author as a URL:

I attempt to give a use case for user defined queries here: https://github.com/PaulGilmartin/graph_wrap#which-problems-does-graphwrap-address

See Which problems does GraphWrap address for more.

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.

FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.

In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

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.