instaunit VS fastapi

Shameless plug: for REST APIs, I've written a tool called Instaunit which combines HTTP API integration tests with documentation generation, since these two things must always be maintained in lockstep.

It's got a ways to go before it generates output that looks as good as Stripe's documentation, but it makes it dead simple to create API documentation that's guaranteed to be in sync with your service, because it was generated by your tests when they ran.

https://github.com/instaunit/instaunit

Check out https://github.com/instaunit/instaunit. It lets you define the calls to be made and expected responses and will compare them semantically so you can match on expected fields without worrying about things like dates and IDs that you can't predict easily. It can also be used to generate documentation for your APIs which is awesome because your docs are then built from your tests so you know they are current and accurate.

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.