falcon VS Tweepy

Falcon FastAPI Sanic Starlite (disclosure: I do work here)

Sanic is very very popular with 16.6k stars, 1.5k forks, opencollective sponsors and a very active github. Falcon is more popular than japronto with 8.9k stars, 898 forks, opencollective sponsors and a very active github too. Despite Japronto been keeped as first place by TechEmPower, Falcon is a way better solution in general with performance similar to fastify an very fast node.js framework that hits 575k requests per second in this benchmark.

I prefer Falcon for kicking up an API.

Falcon

And here's a project that's mostly Python, and optionally uses Cython https://github.com/falconry/falcon

To help with the above two cases I would consider using a microframework, and the Python community provides many solutions. In my professional career I’ve had the opportunity to work with three very good alternatives to Django: Flask, Falcon, and Fast API. Flask is designed to be easy to use and extend. It follows the principles of minimalism and gives more control over the app. Choosing it, developers can use multiple types of databases, which is not easy to do in Django. We can also plug in our favorite ORM and use it without any risk of unpredictable app behavior. In contrast to Django, it’s easy to integrate NoSQL databases with Flask.

This may not be exactly what you asked for but I found contributing to open source projects really exposed me to different approaches I never would have considered and may not have fully grasped had I not had to actually dive into the code to solve an issue. Falcon is a great place to start and the guys are super friendly there.

https://falcon.readthedocs.io/en/stable/ https://fastapi.tiangolo.com/

How is this much different than what tweepy does?

There are plenty of open source modules you can use, such as tweepy, which will make it a lot easier for you.

I chose Python as a language to handle the streaming, because a) it’s the language I’m strongest in, and b) because of tweepy.

· Tweepy. An easy-to-use Python library for accessing the Twitter API.

As announced in this blog post, the Twitter API v2 supports the ability to get metadata about edited Tweets. In this short guide, I will showcase how developers and researchers can get information about edited Tweets from the Twitter API v2 in Python using Tweepy.

In this tutorial, we will configure AWS CodePipeline to build an ECR image and deploy the latest version to lambda container. The application code will stream tweets using Tweepy, a Python library for accessing the Twitter API. First we need to setup CodePipeline and the various stages to deploy application code to lambda image which will stream tweets when invoked. The intended devops architecture is as below.

That means you need a requirements.txt file in your repo somewhere, and it needs to list the dependencies for your project. A good way of knowing what the dependencies are is to check what you imported at the beginning of your python file. For my bots, I need Tweepy, config, and Python-dotenv Requirements.txt looks like this for Divas, my Twitter Bot Collection.

you can easily make a simple script with tweepy that scrapes the data and puts it into whatever file you need

In order to use the Twitter API v2, you need to apply for a Twitter developer account. Once you have an approved Twitter developer account, follow the instruction here to obtain your BEARER TOKEN that you will use to connect to the Twitter API v2 in you code in Python. We will be using the Tweepy package in Python to get images from the Twitter API v2, so you will need to have Python as well as the Tweepy package installed on your machine. Instructions on installing the Tweepy package can be found in this tutorial.