httpx VS starlette

I've mostly ditched requests in favour of httpx these days. https://www.python-httpx.org

Now that your environment is set up, you’re going to need to install the HTTPX library for making requests, both asynchronous and synchronous which we will compare. Install this with the following command after activating your virtual environment:

Now time to code the implementation, as I wanted to both read from and write to ffmpeg concurrently, so this is going to be an asyncio application. The http client library we are using this time is httpx, which has a method to fetch download in smaller batches:

The concept of a Client in httpx is analogous to a Session in requests. However, httpx.Client is more powerful and efficient. You can read the article from the httpx documentation, to learn more about httpx.Client.

Let's look at a simple code example. This will work for requests, httpx, and aiohttp with a clean installation and no extensions.

For reference, it's a butterfly, not a moth.

Source: https://github.com/encode/httpx/issues/834

Very cool, first I'm hearing of httpx https://www.python-httpx.org/

I think most people would start with trying out requests or something for this kind of work, I'm guessing that didn't work out? You've got a star from me.

To access 10 different commands at the same time, that is tricky but definitely doable.

First thing that comes to mind, you can use aliases.

To keep it simple, lets use 3 examples instead of 10: harlequin (this project), pgcli (https://www.pgcli.com/) and httpx (https://www.python-httpx.org/)

Setup a main home for all your venvs:

    cd ~

There are already some implementations for Python HTTP clients. One of them is aiometer. But it's not suitable for my use case. Since httpx already has the internal pool, it would be better to reuse the design.

📘 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.

Or you can go straight to Starlette [0], which is much more streamlined to develop with. FastAPI is based on it itself, and while it provides many additional bells and whistles I find it messes things up significantly.

[0] https://www.starlette.io/

Now it's time to take a closer look on how FastAPI extends Starllete.