How To Call Kubernetes API using Simple HTTP Client

There are plenty of reasons to call the Kubernetes API using a CLI (like curl) or GUI (like postman) HTTP client. For instance, you may need finer-grained control over Kubernetes Objects than kubectl provides or just want to explore the API before trying to access it from code.

This article is not a mere list of handy commands but a thoughtful walk-through revealing some interesting problems you may stumble upon while calling the Kubernetes API from the command line. It covers the following topics:

  • How to get the Kubernetes API server address
  • How to authenticate the API server to clients
  • How to authenticate clients to the API server using certificates
  • How to authenticate clients to the API server using tokens
  • Bonus: How to call the Kubernetes API from inside a Pod
  • How to perform the basic CRUD operations on Kubernetes Objects with curl
  • How to access the Kubernetes API directly using the kubectl's raw mode
  • Bonus: How to see what API requests a kubectl command like apply sends.

Happy reading!

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Kubernetes API Basics - Resources, Kinds, and Objects

This is the first post in the series of articles on how to work with the Kubernetes API from code. The Kubernetes API is a bit more advanced than just a bunch of HTTP endpoints thrown together. Therefore, it's vital to understand the Kubernetes API structure and be fluent in the terminology before trying to access it from code. Otherwise, the attempt will be quite painful - the official Go client comes with so many bells and whistles that trying to wrap your head around the client and the API concepts simultaneously might overwhelm you quickly.

The Kubernetes API is massive - it has hundreds of endpoints. Luckily, it's pretty consistent, so one needs to understand just a limited number of ideas and then extrapolate this knowledge to the rest of the API. In this post, I'll try to touch upon the concepts I found the most fundamental. I'll favor simplicity and digestability over academic correctness and completeness of the material. And as usual, I just share my understanding of things and my way of thinking about the topic - so, it's not an API manual but a record of personal learning experience.

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OpenFaaS - Run Containerized Functions On Your Own Terms

For a long time, serverless has been just a synonym of AWS Lambda for me. Lambda offered a convenient way to attach an arbitrary piece of code to a platform event like a cloud instance changing its state, a DynamoDB record update, or a new SNS message. But also, from time to time, I would come up with a piece of logic that is not big enough to deserve its own service and at the same time doesn't fit well with the scope of any of the existing services. So, I'd often also put it into a function to invoke it later with a CLI command or an HTTP call.

I moved away from AWS a couple of years ago, and since then, I've been missing this ease of deploying serverless functions. So I was pleasantly surprised when I learned about the OpenFaaS project. It makes it simple to deploy functions to a Kubernetes cluster, or even to a VM, with nothing but containerd.

Intrigued? Then read on!

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Containers vs. Pods - Taking a Deeper Look

Containers could have become a lightweight VM replacement. However, the most widely used form of containers, standardized by Docker/OCI, encourages you to have just one process service per container. Such an approach has a bunch of pros - increased isolation, simplified horizontal scaling, higher reusability, etc. However, there is a big con - in the wild, virtual (or physical) machines rarely run just one service.

While Docker tries to offer some workarounds to create multi-service containers, Kubernetes makes a bolder step and chooses a group of cohesive containers, called a Pod, as the smallest deployable unit.

When I stumbled upon Kubernetes a few years ago, my prior VM and bare-metal experience allowed me to get the idea of Pods pretty quickly. Or so thought I... ๐Ÿ™ˆ

Starting working with Kubernetes, one of the first things you learn is that every pod gets a unique IP and hostname and that within a pod, containers can talk to each other via localhost. So, it's kinda obvious - a pod is like a tiny little server.

After a while, though, you realize that every container in a pod gets an isolated filesystem and that from inside one container, you don't see processes running in other containers of the same pod. Ok, fine! Maybe a pod is not a tiny little server but just a group of containers with a shared network stack.

But then you learn that containers in one pod can communicate via shared memory! So, probably the network namespace is not the only shared thing...

This last finding was the final straw for me. So, I decided to have a deep dive and see with my own eyes:

  • How Pods are implemented under the hood
  • What is the actual difference between a Pod and a Container
  • How one can create Pods using Docker.

And on the way, I hope it'll help me to solidify my Linux, Docker, and Kubernetes skills.

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Learn-by-Doing Platforms for Dev, DevOps, and SRE Folks

There are many resources for people who want to learn Linux, Containers, or Kubernetes. However, most of these resources don't come with an interactive, hands-on learning experience. You can read tens of fine blog articles and watch hundreds of engaging YouTube videos, maybe even take some courses with theoretical quizzes at the end, but it's doubtful you'll master any of the above technologies without actively practicing them.

Theoretical-only knowledge of, say, Kubernetes doesn't really count. Hands-on exercises should be a must-have learning element. Some resources, including this blog, strive to provide reproducible instructions so that students can try out the new skills. However, for that, a running system is needed. Setting up such a system can make the learning curve substantially steeper or even make the task fully unbearable for inexperienced students.

So, where can a student practice the new skills?

One option is to experiment on a real staging (or production ๐Ÿ™ˆ) environment. But it can be quite harmful. Luckily, there is an alternative. Some learning platforms offer interactive playgrounds mimicking real-world setups. On these platforms, students can SSH into disposable Linux servers, or even access multi-server stages right from their browsers!

Experimenting with the new skills in such sandboxes makes the learning hands-on. At the same time, these platforms free students from the need for provisioning playgrounds. It brings students closer to real-world environments while keeping the learning process safe - playgrounds can always be destroyed and recreated without damaging any real production systems.

I got so fascinated by the idea of interactive playgrounds recently that I spent a week researching platforms that provide in-browser learn-by-doing experience. Below are my findings, alphabetically ordered:

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