quart VS fastapi

Sorry. It's not Quartz, it's just Quart https://pgjones.gitlab.io/quart/

If you're looking for an experience that's very similar to Flask you can use Quart which is inspired by Flask. Quart even has a guide about how to migrate from a Flask application to using Quart! Flask's own documentation for async views actually recommends using Quart in some cases due to the performance hit from using a new event loop per request.

Flask getting async support is a huge step for the asyncio web ecosystem in Python. I wonder how Flask's ASGI support will evolve in parallel with Quart[1] which bills itself as the ASGI version of Flask and is maintained by a member of the Pallets team. One of the maintainers of Werkzeug even recommended using Quart[2]

[1] https://pgjones.gitlab.io/quart/

[2] https://github.com/pallets/werkzeug/issues/1322#issuecomment...

What we get with the async part of this release - If you would like to run an async library or your own async code from a flask route you can do that now. This is super useful, where let's say we have some async code that fetches data from many sources concurrently, or call multiple a few ML prediction endpoints at the same time (as long as they don't time out) using httpx and respond with some sort of outcome, or finally try that cool new async-only database library. A current (v2) limitation is that the you can't make concurrent requests using just the current asyncio implementation (an alternative with Flask API and ASGI: Quart). Typically in production gunicorn or uwsgi + threads/processes/gevent-eventlet is used and this makes Flask behave asynchronously. More here and here if interested.

For people looking to do async stuff with Flask, there is also Quart! It’s essentially an async port of Flask, with all your favorite, familiar methods!

I agree with the transition issues, Quart has this awkward code to try and get around this.

Appreciate the feedback! I forgot to pimp my favorite web framework, Quart, but I can attest to Flask and even Django being great. I always looked down on Django as too big but then I used it for my current company and I realized I was totally wrong. Easy to pick up, lots of functionality if you want it, but it doesn't force it on you.

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.

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: