uvicorn VS starlette

We're almost ready to start our server! We'll be using the A2AServer class from Google-A2A which under the hood starts a uvicorn server.

FastAPI and Uvicorn are two essential building blocks when developing high-performance Python APIs.

To run the server, you will also need to install Uvicorn:

So I've decided to create another series of articles, and this time talking about another piece of FastAPI development: ASGI servers, and mainly focusing on 🦄 uvicorn due to the evident synergy between both projects 😎.

Finally, whatever file launches your application (in this case, mine is main.py), it is conventional to include that if __name__ == "main": block at the bottom (shown above), to specify the entrypoint to your application. This isn't strictly necessary in many use cases—for instance, if you're writing a REST API using FastAPI and launching it via uvicorn, you don't need this block. But it also doesn't hurt to have it regardless. It's a helpful reminder of where things begin.

This article is written by Marcelo Trylesinski, a > FastAPI expert and > maintainer of Starlette and > Uvicorn. You can check out more of his work > here. All opinions expressed are his own.

Since, app has selenium minimum running requirement would be 1GB RAM on any OS, and free version of any hosting do not provide that, some that provide are limited by build times or shared cpus.

So it was only logical to go through the pain/pleasure of setting up deployment on VM.

Python Backend is deployed on Azure VM(with 4 GB), since it's built on Fastapi their AWSGI support helps keep the server running on long intervals.

This one is using CF Tunnels to Prod to reduce the latency due to server location.

You can see uvicorn docs to learn more https://www.uvicorn.org/

Uvicorn is a lightning-fast ASGI server implementation, using uvloop and httptools. It is designed to run asynchronous Python web applications, particularly those built with frameworks like FastAPI, Starlette, and others.

Uvicorn - An ASGI web server implementation for Python

FastAPI: A modern, high-performance framework designed for building APIs with Python 3.7+ that prioritizes speed and efficiency. It leverages Python type hints to improve code readability and maintainability, and it integrates seamlessly with Starlette for asynchronous capabilities and Pydantic for data validation.

In the field of Python web development, FastAPI has received much attention due to its concise and user-friendly features. However, in reality, it is just a high-level encapsulation of Starlette and Pydantic. The official Starlette website showcases its rich features, and Pydantic is renowned for its powerful data validation capabilities. By directly using these two libraries, developers can flexibly build high-performance APIs without relying on the encapsulation of FastAPI. Next, we will elaborate in detail by combining the core functions and features of both.

The framework's official website mentions a number of pros of FastAPI. In my opinion, the most useful features from a microservice perspective are: the simplicity of code (easy to use and avoid boilerplate), high operational capacity thanks to Starlette and Pydantic and compatibility with industry standards - OpenAPI and JSON Schema.

📘 Learn More: Starlette GitHub

This article is written by Marcelo Trylesinski, a > FastAPI expert and > maintainer of Starlette and > Uvicorn. You can check out more of his work > here. All opinions expressed are his own.

Starlette: The ASGI Foundation

FastAPI is essentially an API wrapper for Starlette. To fully grasp FastAPI, one must first understand Starlette.

I built several tool to tail logs with SSE using Flask/FastAPI. Easy to implement and maintain.

For FastAPI if you want some hooks when client disconnects aka nginx 499 errors, follow this simple tip

https://github.com/encode/starlette/discussions/1776#discuss...

ASGI Web Framework: Web frameworks that implement the ASGI specification (such as FastAPI), while Flask and Django are web frameworks that implement WSGI. These frameworks are designed for developers to write applications, with easy-to-use interfaces. Developers only need to fill in the business logic according to their needs. Early frameworks mostly implemented ASGI toolkits internally, while later frameworks usually combine suitable toolkits. For example, Flask uses Werkzeug (its own), and FastAPI uses Starlette (from others).

Starlette is a lightweight ASGI framework/toolkit, ideal for building high-performance async services in Python. It provides the essential components needed to build web applications, including routing, middleware, sessions, and more.