In this article I discuss container-based development, where you not only run but also develop and debug software inside a locally-running Docker container. I explain the advantages and disadvantages of this approach, and look at VS Code’s development container feature, various features of IntelliJ-based IDEs, and how to implement an IDE-independent approach. Introduction to container-based … Read more
In this book recommendation, I review the book Designing Data-Intensive Applications by Martin Kleppmann, released in 2017. I also offer my 46 page summary of the book as free download. What are data-intensive applications? This book targets software engineers and architects interested in building distributed systems. In contrast to compute-intensive applications, where CPU cycles are the primary … Read more
Collaborative code editors let developers write code together in real time, just like Google Docs. This article compares Microsoft Live Share, Code with me, and CodeTogether. I clearly demonstrate the limitations and benefits of each tool, including how it integrates into existing IDEs, end-to-end encryption and pricing. I’ll conclude with recommendations for different use cases, … Read more
In this article I discuss three types of Kubernetes development tools that speed up your daily development and debugging tasks: tools like Tilt that speed up development iteration cycles, general approaches for remote debugging with a debugger, and tools like Inspektor Gadget that help you diagnose problems in production. Introduction The increasing complexity of Kubernetes … Read more
Cluster introspection tools help you better understand what is going on in your Kubernetes cluster, by presenting the workloads visually. In this article I present a taxonomy of available tools and briefly present the tools Lens, k9s, kubenav, Octant, Headlamp, Kubernetes dashboard, Skooner and Portainer. I conclude by giving general recommendations. Introduction A very common … Read more
If you develop software that runs on Kubernetes, a local development cluster can significantly speed up your development iteration speed, because everything happens locally. I discuss the pros and cons of local clusters and then present the tools Docker for Desktop, Rancher Desktop, kind, k3d and Minikube, and how they compare. I finish with details … Read more
Starting from an overview of frequent causes for slow pipelines, I derive 5 tips to speed up GitLab CI pipelines. These include choosing the appropriate runner infrastructure, choosing the right caching approach (Docker vs. GitLab), DAG pipelines, mixing GitLab’s caches and artifacts, and optimizing individual jobs (e.g. via parallelization). Introduction As I already explained in … Read more
Using a Node.js example project I demonstrate how Docker-based caching can speed up your GitLab CI/CD pipelines even more than GitLab’s built-in caching mechanism. I explain how each approach works, and what the technical prerequisites are. I also list tools that support you with setting up a Docker-based CI pipeline. Introduction Caching of files between … Read more
This article explains why you should not tag your own Docker images with only the “latest” tag. I discuss alternative best practices, categorizing them into stable vs. unstable version tags. I also fully deconstruct a Docker image tag into its basic components to improve your understanding of Docker image names and tags. Introduction When you … Read more
In this article I explain how (and why) you install and use a Linux-based self-hosted GitLab CI/CD runner that executes jobs of your GitLab pipelines. I go into a few caveats and how you can reduce maintenance efforts for the runner to a minimum. Introduction GitLab CI/CD has a distributed architecture that consists of a … Read more