Nicolas Fränkel

In the first post of this series, we described the concept behind a Kubernetes controller. In short, it’s just a plain control loop that reconciles the desired state of the cluster with its current state. In the second post, we implemented a sidecar controller in Java. This third and last post will be focused on where to deploy this Java controller and how to improve it to be on par with a Go one. Running outside the cluster or inside? As mentioned in the first post, there’s no re

The last two blog posts were focused on how to write a custom Kubernetes controller in Java. As usual, I’m writing a demo along with the posts: that allows me to face real issues, and be able to detail them. In this context, I had to handle a couple of them when implementing the demo code. This post is dedicated to one of them: the compilation of not-compatible Java code with Graal VM’s native image AOT compilation. The context In order for a Java Kubernetes controller to be on pa

In the previous post, we laid out the foundations to create our own custom Kubernetes controller. We detailed what a controller was, and that its only requirement is to be able to communicate with HTTP/JSON. In this post, we are going to finally start developing it. The technology stack can be Python, NodeJS or Ruby. Because this blog is named 'A Java Geek', it’s normal to choose Java. As a use-case, we will implement the sidecar pattern: every time a pod gets scheduled, a sidecar pod w

It’s hard nowadays to ignore Kubernetes. It has become the ubiquitous platform of choice to deploy containerized applications. In a few years, Kubernetes has entrenched itself deeply in the DevOps landscape under the tutelage of the Cloud Native Computing Foundation. One could speculate about the reasons. IMHO, one very compelling argument is that it allows users to be independent of the API of a single cloud provider. If you’ve been living under the monopoly of Microsoft on the deskt

If you’ve been working in Java since 'quite some time', you probably are experienced in using Map objects. One very frequent use-case is to check if a Map contains a value: Get the valueIf the value is null, put an initial value.If isn’t, transform the get value and put the new value into the map under the same key Map<String, Integer> map = new HashMap<>(); // ... Integer value = map.get(key); if (value == null) { map.put(key, 1); } else { map.put(key, ++valu

There are a lot of different ways to configure containers running on Kubernetes: Environment variablesConfig mapsVolumes shared across multiple podsArguments passed to scheduled podsetc. Those alternatives fit a specific context, with specific requirements. For example, none of them allow you to clone a Git repository before the container starts. It would be possible to design that feature into the image itself. Yet, that would introduce coupling, and defeat the Single Responsibility principl

This is the 3rd post in the Stream Processing focus series. Last week, we had a look at how stream processing could help us compute mathematical approximations using Hazelcast Jet. We described the Pipeline API, and mentioned that it drew from a source and drained to a sink. This week, I’d like to detail those concepts, what out-of-the-box sinks and sources are available in Jet, and how to create your own should the need be.

This is the 2nd post in the Stream Processing focus series. Last week, we had a look at what is stream processing. In this post, I’d like to address one possible use-case for stream processing: the computation of mathematical approximations.

This is the 1st post in the Stream Processing focus series. If software is eating the world, data is the new oil. Those who are able to best manage the huge mass of data will emerge out on top. The state-of-the-art way to refine this oil is stream processing. In this post, I’d like to describe what is stream processing, and why it’s necessary in this day and age.

Minification (also minimisation or minimization) is the process of removing all unnecessary characters from the source codes of interpreted programming languages or markup languages without changing their functionality. These unnecessary characters usually include white space characters, new line characters, comments, and sometimes block delimiters, which are used to add readability to the code but are not required for it to execute. Minification reduces the size of the source code, making its tr