It’s not enough to just test our code's happy path (in other words, the error-free path we hope our users will take). To be really confident our code can’t be abused, either accidentally or on purpose, we must actively attack it to try and find ways of breaking it. If we don’t do this, someone else will, and they probably won’t be as friendly.

While Elixir provides built-in libraries (like HTTPoison) for making HTTP requests, the Tesla library has gained popularity due to its simplicity, ease of use, and extensibility. Tesla offers a clean and composable way to define requests, handle responses, and customize client behavior. Even so, whenever you're working with external APIs, errors are inevitable. Network issues, server downtime, rate limiting, and unexpected responses can all lead to errors in your application.

Regular expressions, commonly known as regex, are a tool for text processing and pattern matching. In Python, the re module offers a robust implementation of regex, allowing developers to handle complex text manipulation efficiently. In this article, we'll get to grips with regular expressions and provide practical code examples — from the basic to more advanced — so you can understand how to use regex in Python.

For several years now, the Node.js ecosystem has been steadily shifting towards ES modules (ESM) as the preferred method for sharing and utilizing JavaScript code. While CommonJS has served the community well, ESM are rapidly gaining traction as they offer a standardized approach for creating JavaScript modules across all supported runtimes.

In part one of this series, we managed distributed state using GenServers. This provided a foundation for understanding some core concepts in distributed Phoenix applications. Now, we turn our focus to deployment and scaling strategies. As your application evolves to meet growing demands, knowing how to scale horizontally, maintain high availability, and monitor distributed components becomes crucial.

In this article, you'll learn about N+1 queries, how to detect them with AppSignal, and how to fix them to speed up your Django apps significantly. We'll start with the theoretical aspects and then move on to practical examples. The practical examples will mirror scenarios you might encounter in a production environment. Let's get started!

Unit tests are essential for increasing the test coverage of a backend application, ensuring its reliability, functionality, and robustness. Jest has become one of the most popular solutions when unit testing in Node, due to its intuitive API, zero-configuration philosophy, and flexible approach to code transpilation. In this guide, you will learn more about Jest, explore its features, and see it in action through a complete example. Become a Jest unit testing expert!

In the previous part of this series, we discovered how to create persistent connections in Rack in theory, but now we'll put what we learned into practice. The web has two formalized specifications for communication over a persistent connection: server-sent events (SSEs) and WebSockets. WebSockets are widely used and highly popular, but SSEs are far less well-known. Let's explore them first.

In the first part of this series, we set up a basic Rack app, learned how to process a request and send a response. In this post, we'll take over connections from Rack and hold persistent connections to enable pathways such as WebSockets. First, though, let's look at how an HTTP connection actually works.

When running tests, it's a great feeling to see dozens of green check marks indicating that a test suite is passing. It's especially gratifying after tackling a tricky bug or slogging through a tough feature. But those passing tests may be giving you a false sense of security. Often, bugs lurk in passing tests, undermining trust in the test suite and your application. Such tests can cause more harm than good, giving you a hearty pat on the back while hiding broken functionality.