mangum VS fastapi

Hey! I was in the same place as you are, and the best solution I found was to use Mangum (https://mangum.io/). I believe it also works with Django. Mangum is an adapter that transforms lambda events into the corresponding structure to be received by your Framework API endpoints. We are currently using it with FastAPI and it's great. We code our backend without thinking about whether it will run on Lambda, and Magum takes care of the rest.

In the real world I'm never using static cloud resources. It's all serverless, containers, or horizontal auto-scaling. I let the infrastructure handle asynchronous scaling when needed. For FastAPI specifically I've used Mangum: https://mangum.io/ to provide the asynchronous invocation below the ASGI layer. Then all my FastAPI code can just be synchronous.

I discovered https://github.com/jordaneremieff/mangum which basically transforms a fastapi app to be compatible with aws lambda.

Should I create only 1 apigtw resource with 1 lambda and use mangum + fastapi for my rest api ?

Mangum: For the integration of the Aws Lambda and the Api Gateway with the FastApi

If you go the lambda route, you can use DRF (or any ASGI app) using https://mangum.io/

Yes, it's possible to wrap any asgi app to run in a lambda. Check out Mangum https://github.com/jordaneremieff/mangum

But we do not have a handler function, do we? We have a Mangum object wrapping the FastAPI application. Luckily, the Mangum object acts as a handler function, so we can just add the following in example/src/app/__init__.py:

I've used fastapi in a lambda. The package Mangum simplifies the conversion of lambda handler to a more tradition request.

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.