All articles tagged #rest

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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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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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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Disclaimer: despite the controversial title, this article is not trying to show that RPC is a superior approach to REST, or GraphQL is superior to RPC. Instead, the goal of the article is to give you an overview of the approaches, their strengths and weaknesses. The final choice anyway will be a trade-off.

Even though HTTP is an application layer (i.e. L7), protocol, when it comes to API development, HTTP de facto plays the role of a lower-level transport mechanism.

There are multiple conceptually different approaches on how to implement an API on top of HTTP:

• REST
• RPC
• GraphQL

...but the actual list of things an average API developer needs to be aware of is not limited by these three dudes. There are also JSON, gRPC, protobuf, and many other terms in the realm. Let's try to sort them out, once and for all!

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