nerdctl VS kind

What about the docker-cli? colima also ships with a docker-compatible cli to interact with containerd called nerdctl. We can execute the same docker cli commands like:

Using nerdctl: https://github.com/containerd/nerdctl

I'd really disagree that compose files are somehow one-shot, or blindly modified. To the contrary, really, we have them checked in with the source code. Upon deployment to the cluster, the (running) services will be intelligently updated or replaced (in a rolling manner, causing zero downtime). LXC might be more elegant, but I have no idea what simple, file-based format I could use to let engineers describe the environment their app should run in without compose.

I need something that even junior devs can start up with a single command, that can be placed in the VCS along with the code, and that will not require deep Linux knowledge to get running. Open for suggestions here, really.

Now since Kubernetes works off of containerd I'll be taking a different approach on handling container builds by using nerdctl and the buildkit that comes bundled with it. I'll do this on the amd64 control plane node since it's beefier than my Raspberry Pi workers for handling builds and build related services. Go ahead and download and unpack the latest nerdctl release as of writing (make sure to check the release page in case there's a new one):

That's why nerdctl, their cli binary, is so well named.

Well, it is indeed a good option. However, containerd is a good alternative that is growing even among developers. Please see: https://github.com/containerd/nerdctl

Nerdctl/containerd has IPFS support :)

https://github.com/containerd/nerdctl/blob/main/docs/ipfs.md

nerdctl supports IPFS for both image pulling and pushing, including encrypted images and eStargz lazy pulling. For building, the current method is a locally hosted translator so that the traditional pulls can be converted to work over IPFS. They even have docs on running it on k8s node, though if my reading is correct this isn't exactly a cloud native approach (running systemd services on each node...).

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