NGINX Unit VS fastapi

Nginx Unit application server.

This is great to read! Thanks for all the great input!

I check the comments next week and make sure to address the issues / ideas mentioned. If you don’t mind feel free to drop a comment here as well with your ideas / needs. https://github.com/nginx/unit/issues/945

I will work on a compression between Nginx and Unit to close this gap in our documentation.

You could try running h5ai with NGINX UNIT.

nginx, but nginx-unit looks very interesting. I might switch a few docker containers to use it before trying to use it natively.

unix:/filesocketheadache (problems) verses unix:@abstractsocketmagic (problem solved)

Try Nginx Unit to get away from the php-fpm complexity. It runs on http by default so it's super easy to spin up and access by itself (no need for an extra http server) or behind another http gateway as a simple proxy.

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.