The JavaScript language has evolved significantly in the last few years, but because browsers have been slow in adopting these changes a lot of people have not kept up with the language. React encourages developers to use modern JavaScript, so this chapter gives you an overview of the newest features of the language.

Welcome to the React Mega-Tutorial! In this series I will share my experience in developing real-world, non-trivial front end applications using the React library and a handful of related packages.

Unlike most other books and tutorials, the React Mega-Tutorial will take you on a development journey. Instead of teaching you React concepts with isolated examples, it will show you how to develop a complete front end application. You will begin by creating a brand new React project, and then start adding features and functionality to it as you progress through the chapters. When you reach the end, you will have a complete project of which you will understand every single line of code. More importantly, you will understand the concepts and techniques involved in creating it, in way that will be directly applicable to your own projects.

In this article I'm going to show you a few common patterns for client authentication based on tokens, and how can they be implemented in a Python API back end. This method of authentication works well for rich clients, like JavaScript-based front end applications running in the browser, or perhaps a command-line (CLI) application.

I have written about Authentication several times on this blog, so this article is a bit different. Since I have already provided a few authentication projects in previous articles and in my open source projects, in this article I'm going to go over all the considerations you have to take into account when deciding how to best implement authentication for your own API project.

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When you compare Python and JavaScript async code, you may think they are very much alike, since both use the async/await pattern. In reality, there is a significant difference between the two, which in my opinion, makes the JavaScript async model superior to Python's.

Do you want to know what the difference is, and how to make async Python work like async JavaScript? Watch or read on for the details!

The WebSocket protocol was standardized 10 years ago (in 2011, can you believe it?) so I'm sure for many of it you does not need an introduction. But in case you are not familiar with it, WebSocket is an extension to the HTTP protocol that provides a permanent, bi-directional communication channel between a client and the server, where both sides can send and receive data in real time, free of the constraints of the request/response cycle of HTTP.

Flask, being a minimalist web framework, does not have WebSocket support built-in. The old Flask-Sockets extension, which has not been maintained in the last few years, provided support for this protocol. My own Flask-SocketIO extension has also been a favorite, but this is Socket.IO, which is a more complex protocol that combines WebSocket and HTTP.

The good news is that if you are using Flask 2 you now have a new extension (also created by myself) called Flask-Sock, which provides modern WebSocket support for your application. In this article I'm going to show you how to work with this extension.

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In this article I'm going to show you how to implement an order page for your Flask application that you can use to sell products or services online. The solution I'm going to present to you uses Stripe Checkout, a secure and easy to use credit card charging service that integrates nicely with Flask.

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Most database problems go unnoticed during development, because we tend to code our applications using small datasets. It is when the application has been on production for some time that database performance issues start to appear, causing parts of the application to become slower and slower as the database continues to grow.

How do you debug and identify this type of problems? In this article I'm going to show you how to fix the most common database performance problems, which are those that are caused by improper indexing. Examples for Postgres, MySQL and SQLite are included!

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This is the third part in my series about unit testing Python applications. In this article I'm going to show you some of the techniques I use when I work with Python web applications. For the examples in this article I will be using the Flask framework (I know, what a surprise!), but the concepts I'll show you are universal and can be applied to other frameworks as well.

While this is the first article in the series that covers web applications, I will be applying many of the ideas and solutions I shared in the first and second parts, so be sure to check those out first, even if you are only interested in web applications.

I'm excited to share that I've completed a revision of the Flask Mega-Tutorial to keep it in line with new releases of Flask, Python and third-party dependencies.

To celebrate this update, you can purchase the paid version of this course with a $10 USD discount (this offer is valid through the rest of July 2021). If you are interested in this offer, use the FLASK2 promotional code at checkout, or click here to go directly to the order page with the coupon added. Thank you!

Rendering a table with data in a Flask template is a relatively simple task when the table is short, but can be incredibly hard for larger tables that require features such as sorting, pagination and searching. In this article I'm going to show you how to integrate the dataTables.js library in your templates, which will allow you to create fully featured tables with ease!