There are 1.3 billion websites out there in the great unknown and it’s hard not to think about what makes them different from one another. Why do users flock to one website and ignore the other completely? One major differentiator is, of course, content. I’m not going to dwell on what type of content is better. Another reason why users stick to one website over another is the user experience. Today we’ll be looking at a third major differentiator: Website Performance.

Have you ever grown tired of running the same kubectl commands again and again? Well the good folks over at the Kubernetes team understand you. With the addition of custom resources and the operator pattern, you can now make use of extensions, or addons as I like to call them, to the Kubernetes API that help you manage applications and components. Operators follow Kubernetes principles including the control loop.

eBPF has revolutionized the observability landscape in the Linux kernel. Throughout our previous blog post series, I covered the fundamental building blocks of the eBPF ecosystem, scratched the surface of XDP and showed how closely it cooperates with the eBPF infrastructure to introduce a fast-processing datapath in the networking stack. Nevertheless, eBPF is not exclusive to kernel-space tracing.

In Part 1 we’ve described what container monitoring is and why you need it. Because each container typically runs a single process, has its own environment, utilizes virtual networks, or has various methods of managing storage. Traditional monitoring solutions take metrics from each server and the applications they run. These servers and applications running on them are typically very static, with very long uptimes.

Even though containers have been around for ages, it wasn’t until Docker showed up that containers really became widely adopted. Docker has made it easier, faster, and cheaper to deploy containerized applications. However, organizations that adopt container orchestration tools for application deployment face new maintenance challenges.

Working with Java applications has a lot of benefits. Especially when compared to languages like C/C++. In the majority of cases, you get interoperability between operating systems and various environments. You can move your applications from server to server, from operating system to operating system, without major effort or in rare cases with minor changes.

In this tutorial, we will talk about how different Java Garbage Collectors work and what you can expect from them. This will give us the necessary background to start tuning the garbage collection algorithm of your choice. Before going into Java Garbage Collection tuning we need to understand two things. First of all, how garbage collection works in theory and how it works in the system we are going to tune.

When working with Java or any other JVM-based programming language we get certain functionalities for free. One of those functionalities is clearing the memory. If you’ve ever used languages like C/C++ you probably remember functions like malloc, calloc, realloc and free. We needed to take care of the assignment of each byte in memory and take care of releasing the assigned memory when it was no longer needed.

Big data, AI, machine learning, and numerous others are all buzzwords we seem to throw around lightly in recent years. Even though they are hugely different from one another, they all have one thing in common. Data! Huge amounts of data that need to be managed. The downside of that is that the more data you have the more of a headache it is to store, query, and make sense of.