mangum VS databases

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.

In the code ,we are using SQLAlchemy as our ORM(Object Relational Mapper) and Databases as our query builder.

MySQL is a powerful and popular database, and it's well-suited for use with Python. In the following section, we are going through how to set up a connection to a MySQL database in an asynchronous manner using the create_asynchrouns_engine function from SQLAlchemy. We'll also use the databases package and the aiomysql extra dependency. We'll also adapt the SQLAlchemy declarative approach to defining our users' table.

To take an example: I switched a service over from doing synchronous (plain def everywhere) to async (async def and await everywhere, with async DB, and saw… basically no performance improvement.

No, as I said, async db connection is the key. You can do that with SQLAlchemy using the databases library, but since you are not using SQLAlchemy it makes more sense to use the underlying db driver, aiomysql, directly.

Using the encode/databases library and forgoing SQLAlchemy's ORM

ormar is a mini async ORM for python. It uses sqlalchemy for building queries, databases for asynchronous execution of queries, and pydantic for data validation. You can create an ormar model and generate pydantic models from it.

works but there seems to be a difference between the regular slqalchemy syntax from what https://github.com/encode/databases/ is using in the docs?