falcon VS fastapi

Falcon FastAPI Sanic Starlite (disclosure: I do work here)

Sanic is very very popular with 16.6k stars, 1.5k forks, opencollective sponsors and a very active github. Falcon is more popular than japronto with 8.9k stars, 898 forks, opencollective sponsors and a very active github too. Despite Japronto been keeped as first place by TechEmPower, Falcon is a way better solution in general with performance similar to fastify an very fast node.js framework that hits 575k requests per second in this benchmark.

I prefer Falcon for kicking up an API.

Falcon

And here's a project that's mostly Python, and optionally uses Cython https://github.com/falconry/falcon

To help with the above two cases I would consider using a microframework, and the Python community provides many solutions. In my professional career I’ve had the opportunity to work with three very good alternatives to Django: Flask, Falcon, and Fast API. Flask is designed to be easy to use and extend. It follows the principles of minimalism and gives more control over the app. Choosing it, developers can use multiple types of databases, which is not easy to do in Django. We can also plug in our favorite ORM and use it without any risk of unpredictable app behavior. In contrast to Django, it’s easy to integrate NoSQL databases with Flask.

This may not be exactly what you asked for but I found contributing to open source projects really exposed me to different approaches I never would have considered and may not have fully grasped had I not had to actually dive into the code to solve an issue. Falcon is a great place to start and the guys are super friendly there.

https://falcon.readthedocs.io/en/stable/ https://fastapi.tiangolo.com/

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:

FastAPI is a modern, fast, web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, fast to run, and secure. In this blog post, we’ll explore the key features of FastAPI and walk through the process of creating a simple API using this powerful framework.

FastAPI is a modern, fast web framework for building APIs with Python 3.7+ that automatically generates OpenAPI and JSON Schema documentation. While FastAPI simplifies API development, manually creating and updating API documentation can still be a time-consuming task. In this blog post, we’ll explore how to leverage FastAPI’s automatic documentation generation capabilities, specifically focusing on Swagger and ReDoc, and how to streamline the process of documenting your APIs.