kind VS microk8s

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.

yes I know there is lens, but it does not allow me to see logs of multiple pods at same time and what is even more important it is not friendly for ephemeral clusters - in my case with help of kind I am recreating whole cluster each time from scratch

FROM postgres:11CMD sleep 30 && postgres I'm reusing this example from the same issue with microk8s where I could solve it by changing the kubelet and kubeapi-server configuration files (see https://github.com/ubuntu/microk8s/issues/770 in case you're interested). I assume this is not possible with GKE clusters as they don't expose these files, probably for good reasons.

$ kubectl get podsNAME READY STATUS RESTARTS AGEbackend-deployment-66cff7d4c6-gwbzf 0/1 ImagePullBackOff 0 18s Before that it was ErrImagePull. So, my question is, how to tell it to use local docker images? Somewhere on the internet I read that I need to build images using microk8s.docker but it seems to be removed.

There's a whole community of hobbyists building Raspberry Pi clusters, porting things to work on various Arm processors, exploring and contributing to minimalist distros like k0s and microk8s, etc.

same pvc worked fine on "GKE" (Google Kubernetes Engine) but failing in my local cluster using microk8s

Local Kubernetes Clusters: It is now possible to run Kubernetes on local machines with tools such as Kubernetes in Docker (kind), minikube or MicroK8s. This allows developers to run their first experiments completely isolated from others and with low risk and low cost.

Local Kubernetes clusters are clusters that are running on the individual computer of the developer. There are many tools that provide such an environment, such as Minikube, microk8s, k3s, or kind. While they are not all the same, their use as a development environment is quite comparable.

Since the developer is the only one who has to access this cluster for development, local clusters can be a feasible solution for this purpose. Over time, several solutions have emerged that are particularly made for running Kubernetes in local environments. The most important ones are Kubernetes in Docker (kind), MicroK8s, minikube and k3s. For a comparison of these local Kubernetes options, you can look at this post.

Another very important distinction of this level is that developers have direct access to Kubernetes for the first time. While it is not strictly necessary, the standard case for the Kubernetes access with these tools is to use a local Kubernetes cluster, i.e. a Kubernetes cluster started with tools such as minikube, kind or MicroK8s on the local computer of the developer.