apiclarity VS kind

We'll install APIClarity into a Kubernetes cluster to test our API documentation. We're using a Kind cluster for demonstration purposes. Of course, if you have another Kubernetes cluster up and running elsewhere, all steps also work there.

If you go to APIClarity, the first thing you’ll see is the source code (Figure 1), followed by some documentation at the bottom.

This feature is available in the open-source tool APIClarity, as part of the OpenClarity initiative.

Hi, I would also like to add another tool I'm contributing to at work (cisco) called APIClarity [1]. It aims at reconstructing swagger specifications of REST microservices running in K8S, but can also be run locally.

This is a challenging task and we don't support OpenAPI v3 specs yet (we are working on it).

Feel free to have a look, and get ideas from it :)

We'll also be presenting it at next Kubecon 2022.

[1]: https://github.com/openclarity/apiclarity

For example, APIClarity is a tool that observes all of the API traffic within your Kubernetes environment. Based on traffic observation, APIClarity infers an OpenAPI description for those APIs. This is especially helpful if the API creator never defined or provided such a description. It also surfaces potential problems with existing APIs, such as requests made to undocumented, shadow APIs or continued use of deprecated, zombie APIs. If you’re getting started on the path toward OAS compliance, then tools like APIClarity can be a great source of insight and observability.

The fundamental first step to solving this problem is to create an API spec and use it to audit and document the APIs your apps use. Ideally, we would create an API spec simply by observing API traffic in real-world applications. In the past, there was no simple, scalable, and open-source tooling capable of doing this. Now, we have APIClarity—an open-source API traffic visibility tool for Kubernetes (K8s) clusters. It’s purpose-built to address the gap and enable API reconstruction through observation.

Have you tried https://kind.sigs.k8s.io/? If so, how does it compare to k3s for testing?

To get started, you'll need to install clusteradm and kubectl and start up three Kubernetes clusters. To simplify cluster administration, this article starts up three kind clusters with the following names and purposes:

Kind: is a tool for running local Kubernetes clusters using Docker container "nodes." It was primarily designed for testing Kubernetes itself but can also be used for local development or continuous integration.

Fortunately, just as projects like kind and Minikube enable developers to spin up a local Kubernetes environment in no time, CRC, also known as OpenShift Local and a recursive acronym for "CRC - Runs Containers", offers developers a local OpenShift environment by means of a pre-configured VM similar to how Minikube works under the hood.

I recently purchased a used Lenovo M900 Think Centre (i7 with 32GB RAM) from eBay to expand my mini-homelab, which was just a single Synology DS218+ plugged into my ISP's router (yuck!). Since I've been spending a big chunk of time at work playing around with Kubernetes, I figured that I'd put my skills to the test and run a k3s node on the new server. While I was familiar with k3s before starting this project, I'd never actually run it before, opting for tools like kind (and minikube before that) to run small test clusters for my local development work.

Features compared to https://kind.sigs.k8s.io/

Kind allows you to run a Kubernetes cluster inside Docker. This is incredibly useful for developing Helm charts, Operators, or even just testing out different k8s features in a safe way.

Check out KinD

If you're just messing around, just use kind (https://kind.sigs.k8s.io) or minikube if you want VMs (https://minikube.sigs.k8s.io). Both work on ARM-based platforms.

You can also use k3s; it's hella easy to get started with and it works great.

We'll install APIClarity into a Kubernetes cluster to test our API documentation. We're using a Kind cluster for demonstration purposes. Of course, if you have another Kubernetes cluster up and running elsewhere, all steps also work there.