flama VS fastapi

There has been a considerable effort in the last few years to try and standardise the way in which these type of APIs are implemented via different frameworks. However, over the last few years, a new type of functionality has become more and more popular: machine learning (ML) models; and the existing frameworks for building APIs are not well suited for this type of functionality. In this series of posts, we will learn how to build APIs using a Framework for the development of Lightweight Applications and Machine-learning Automation, also known as Flama.

*some options that were mentioned in the thread I linked to include - Starlite (made to bypass perceived shortcomings of FastAPI) - Flama (Starlette w/ Marshmellow instead of Pydantic) - APIFlask (Flask w/ Pydantic) - Django Ninja - Quart-Schema

Flama (Starlette, Marshmallow instead of Pydantic)

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.

TiTiler is a dynamic tile server built on FastAPI and Rasterio/GDAL. Its main features include support for Cloud Optimized GeoTIFF(COG), multiple projection methods, various output formats (JPEG, JP2, PNG, WEBP, GTIFF, NumpyTile), WMTS, and virtual mosaic. It also provides Lambda and ECS deployment environments using AWS CDK.

To make it a bit more realistic, we’re going to use a FastAPI route as an example, and we’re also going to use FastAPI’s dependency injection, which can really help with readability (and testability, but more on that later).

2. FastAPI - Turbocharge Your Web APIs with Python ⚡