All articles on Kubernetes

"How to start programming in Go and for Kubernetes?" - the question I often get from fellow DevOps people. This is a tricky one. And I don't have a universal answer to it. However, I do have some thoughts to share.

But first, let me tell you my own story.

In my case, it was rather an evolutionary step - I'd been developing software for almost 10 years by the time I started coding for Kubernetes. I'd also been (sporadically) using Go for some of my server-side projects since probably 2015. And around 2019, I started my transition to, first, SRE and, then, Platform Engineering. So, when I decided to get my hands dirty with Kubernetes controllers, it was just a matter of joining the right team and picking up the Kubernetes domain. Luckily, I had a good candidate on my radar, and that required just an internal transition from one team to another.

However, based on my observations, for many contemporary DevOps engineers, the direction of the desired transformation is often inverse. From Ops to Dev (preferably, for Ops).

Since your background and experience may vary, instead of giving a concrete piece of advice here, I'll try to explain how I'd approach the problem given different levels of proficiency with the technologies.

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Last week at KubeCon, there was a talk about Kubernetes ephemeral containers. The room was super full - some people were even standing by the doors trying to sneak in. "This must be something really great!" - thought I and decided to finally give Kubernetes ephemeral containers a try.

So, below are my findings - traditionally sprinkled with a bit of containerization theory and practice 🤓

TL;DR: Ephemeral containers are indeed great and much needed. The fastest way to get started is the kubectl debug command. However, this command might be tricky to use if you're not container-savvy.

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A short one today. Just wanted you to meet my new favorite Go library to work with Kubernetes - k8s.io/cli-runtime. It's probably the most underappreciated package in the whole k8s.io/* family based on its value to the number of stars ratio.

Here is what the README file says about it:

Set of helpers for creating kubectl commands, as well as kubectl plugins.

This library is a shared dependency for clients to work with Kubernetes
API infrastructure which allows to maintain kubectl compatible behavior.

If the above description didn't sound too impressive, let me try to decipher it for you - with the cli-runtime library, you can write CLI tools that behave like and are as potent as the mighty kubectl!

Here is what you actually can achieve with just a few lines of code using the cli-runtime library:

• Register the well-know flags like --kubeconfig|--context|--namespace|--server|--token|... and pass their values to one or more client-go instances.
• Look up cluster objects by their resources, kinds, and names with the full-blown support of familiar shortcuts like deploy for deployments or po for pods.
• Read and kustomize YAML/JSON Kubernetes manifests into the corresponding Go structs.
• Pretty-print Kubernetes objects as YAML, JSON (with JSONPath support), and even human-readable tables!

Interested? Then have a look at the usage examples below 😉

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There are many ways to extend Kubernetes with custom functionality, starting from writing kubectl plugins and ending with implementing scheduler extensions. The exhaustive list of extension points can be found in the official docs, but if there were a ranking based on the hype around the approach, I bet developing custom controllers or operators, if you will, would win.

The idea behind Kubernetes controllers is simple yet powerful - you describe the desired state of the system, persist it to Kubernetes, and then wait until controllers do their job and bring the actual state of the cluster close enough to the desired one (or report a failure).

However, while controllers get a lot of the press attention, in my opinion, writing custom controllers most of the time should be seen as just one (potentially optional) part of the broader task of extending the Kubernetes API. But to notice that, a decent familiarity with a typical workflow is required.

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The official Kubernetes Go client comes loaded with high-level abstractions - Clientset, Informers, Cache, Scheme, Discovery, oh my! When I tried to use it without learning the moving parts first, I ran into an overwhelming amount of new concepts. It was an unpleasant experience, but more importantly, it worsened my ability to make informed decisions in the code.

So, I decided to unravel client-go for myself by taking a thorough look at its components.

But where to start? Before dissecting client-go itself, it's probably a good idea to understand its two main dependencies - k8s.io/api and k8s.io/apimachinery modules. It'll simplify the main task, but that's not the only benefit. These two modules were factored out for a reason - they can be used not only by clients but also on the server-side or by any other piece of software dealing with Kubernetes objects.

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