mccabe VS fastapi

mccabe for Ned’s script to check McCabe complexity

Cyclomatic Complexity is a metric used to determine the stability of your code. It basically boils down to the more code you have the more problems that can arise in said code. There are even modules for python to check your cyclomatic complexity. It goes hand in hand with separating your code out into modules. I work for a FAANG company and we usually want to keep our cyclomatic complexity less than 10 with that tool above.

Mccabe—a Python complexity checker;

A more advanced best practice would be separating different functions of your code into different files to keep Cyclomatic Complexity low. More code usually = more problems can be in said code. There’s even a tool you can use to determine how complex your code is called mccabe. Lower is better with that

Flake8: a combination of following linters: PyFlakes, pycodestyle, Ned Batchelder’s McCabe script

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.