Systems | Development | Analytics | API | Testing

When developing software applications, data storage is a key concern. The reality is that your first concern should be the data model you choose, which in turn affects how you store data. Generally speaking, this means deciding between SQL and NoSQL databases. In this article, you will learn how to use MongoDB, a popular NoSQL database, in a Flask application. First, you will learn why MongoDB is a good choice, and then we will implement a practical hands-on project using MongoDB in Flask.

In our previous article, we explored the basics of monitoring Ecto with AppSignal, covering everything from initial setup to tracking key metrics such as query execution time and resource consumption. We even set up custom instrumentation for database connection pools to gain deeper insights into our application's performance. However, setting up monitoring is just the first step toward maintaining a healthy, high-performing Elixir application.

Choosing the right framework can significantly impact the success of a dev project. If you've chosen to use Python for your project's backend, you might need to decide between Flask and Django, the most popular web frameworks in the Python ecosystem. This article will help you determine which of the two best suits your specific needs by examining the key factors to consider. Before diving into the technicalities, let's briefly introduce both Flask and Django.

In this post, we'll run through testing a Node-MongoDB app, step by step. You can test MongoDB using mongodb-memory-server, an in-memory version of MongoDB that runs independently of a persistent database. A freshly spun-up mongod process starts at roughly 7 MB of memory, providing a lightweight, self-contained environment for running tests. Let's get going!

Our previous article in this series established that Solid Queue is an excellent choice if you need a system for processing background jobs. It minimizes external dependencies — no need for Redis! — by storing all jobs in your database. Despite that, it is incredibly performant. But just being performant is not enough for a production-ready background job system. Rails developers have come to expect a lot over the years. We don't just want to enqueue jobs to run in the background.

Relational databases rely on SQL, or Structured Query Language, as the standard way to control and interact with their stored data. In the Node.js ecosystem, Sequelize is a prominent Object-Relational Mapping(ORM) library that bridges the gap between Node.js apps and SQL databases, enabling you to interact with your data using familiar JavaScript idioms. This article will guide you through using Sequelize to connect to databases, run queries, and handle results within your Node.js projects.

Deploying Phoenix applications in production environments poses unique challenges due to Elixir and the Erlang Virtual Machine (VM). The ecosystem offers multiple strategies — ranging from releases to mix-based approaches (as detailed in the Phoenix Deployment Guide) — and various platforms employ different methods. While some rely on buildpacks (for example, Heroku and Gigalixir), others use containerization (like Fly).

Performance and stress testing are two popular testing methodologies. They focus on determining how an application behaves under regular and unexpectedly high traffic, respectively. These tests are essential for assessing a system's robustness and scalability, particularly in applications with fluctuating traffic like Node.js backends. In this article, you'll learn about stress and performance testing, why they're important, the best tools available, and how to use them in Node. Let's dive in!

By optimizing Python code, you improve performance, reduce resource consumption, and enhance scalability. While Python is known for its simplicity and readability, these characteristics can sometimes come at the cost of efficiency. In this article, we'll explore four ways to optimize your Python project and improve performance. First, we'll look at how best to use data structures.

The latest Node.js release has arrived, and it's not just another incremental update. It delivers meaningful improvements that directly address real-world pain points and opens up new possibilities for JavaScript on the server. In this article, we'll cut through the changelog noise and focus on what's genuinely worth getting excited about in this release. Let's get started!