Python and REST APIs
Python has been used to build REST APIs for a very long time. While there are many options and solutions, DRF and Flask seem to be the most popular ones, at least until recently. If you are feeling adventurous, you can Google less popular or older frameworks such as bottle.py and CherryPy.
DRF is a plugin system for the Django web framework and enables a Django system to create highly customized REST API responses and generate endpoints based on the defined models. DRF is a very mature and battle-tested system. It is regularly updated, and its documentation is very detailed.
Flask, Python’s lightweight microframework, is a real gem among the web-building Python tools and can create REST APIs in a lot of different ways. You can use pure Flask and just output the appropriate format (i.e., JSON instead of HTML) or use some of the extensions developed to make the creation of REST APIs as straightforward as possible. Both of these solutions are fundamentally synchronous, although there seems to be active development in the direction of enabling async support.
There are also some very robust and mature tools, such as Tornado, which is an asynchronous networking library (and a server) that is able to scale to tens of thousands of open connections. Finally, in the last couple of years, several new Python-based solutions have been created.
One of these solutions, and arguably the fastest, is Starlette. Dubbed as a lightweight ASGI framework/toolkit, it is ideal for building high-performance async services.
Sebastian Ramirez built FastAPI on top of Starlette and Pydantic, while also adding numerous features and goodies by using the latest Python features, such as type hinting and async support. According to some recent developer surveys1, FastAPI is quickly becoming one of the most popular and most loved web frameworks.
In later chapters of this book, you’ll go over the most important features of FastAPI, but at this point, we’ll stress the significance of having a truly async Python framework as the glue for the most diverse components of a system. In fact, besides doing the usual web framework stuff, such as communicating with a database, spitting out data to a frontend, and managing authentication and authorization, this Python pipeline enables you to quickly integrate and easily carry out frequently required tasks such as background jobs, header and body manipulation, response and request validation, and more through the dependency injection system.
The book will try to cover the absolute minimum necessary for you to be able to build a simple FastAPI system, but along the way it will consider various web server solutions and deployment options (such as Deta, Heroku, and DigitalOcean) for your FastAPI Python-based backend, while trying to opt for free solutions.
So, to cut a long story short, you should consider choosing FastAPI because you ideally want the ability and speed to handle requests asynchronously as if you were using a Node.js server while having access to the Python ecosystem. Additionally, you want the simplicity and development speed of a framework that automatically generates documentation for you.
After reviewing the backend components, it is time to finalize your stack and work on the frontend. The next section gives you a brief introduction to React and discusses what distinguishes it from other (also valid) solutions.
