microk8s VS skaffold

If you want hands-on practice you should have a running Kubernetes cluster (I used MicroK8s for this tutorial) and Helm (see how to install on Installing Helm tutorial). It is important that you understand the basics of these tools to fully understand.

And install microk8s:

MicroK8s is a lightweight, batteries included Kubernetes distribution by Canonical designed for running edge workloads which also happens to be developer-friendly and a great choice for building your own homelab. The following lab covers how to install and run MicroK8s on your own edge node running Ubuntu 22.04 LTS, deploy the NGINX web service and exposing your NGINX website to the Internet with SSL/TLS enabled using AWS resources included within the Free Tier.

One quick and easy win I can recommend, is microk8s.

I had the same problem (and there's a github issue about this: https://github.com/canonical/microk8s/issues/2186). I swapped to k3s and the usage was half of what microk8s used.

microk8s currently has a showstopping issue that makes it guaranteed to have an irrecoverable failure in HA mode. see https://github.com/canonical/microk8s/issues/3227

k0s is better but also has a lot of bugs. it's the closest to vanilla kubernetes among all the distributions.

> like the simplest GPU support

linux users should be ready to install the nvidia device plugin. if they can't do that, they're never going to succeed in running a gpu accelerated application on their cluster anyway.

> like bootstrapping

in my experience, writing all the bootstrap scripts is painful. but now that there's chatgpt, so much of the drudgery as gone away.

MicroK8S to spin up a Kubernetes cluster

They now power my microk8s/x86 cluster (in addition to my 8-node Raspberry Pi4 ARM64 microk8s cluster), microceph cluster and my LXD cluster, and all are configured with WOL, so I can bring up the cluster from any machine in the homelab, on demand.

A nice middle ground is using a tool like Google's Skaffold, which provides "Bazel-like" capabilities for composing Docker images and tagging them based on a number of strategies, including file manifests. In my case, I also use build args to explicitly set versions of external dependencies.

While I am in a Typescript environment with this setup at the moment, my personal experience that Skaffold with Docker has a lighter implementation and maintenance overhead than Bazel. (You also get the added benefit of easy deployment and automatic rebuilds.)

I quite liked using Bazel in a small Golang monorepo, but I ran into pain when trying to do things like include third-party pre-compiled binaries in the Docker builds, because of the unusual build rules convention. The advantage of Skaffold is it provides a thin build/tag/deploy/verify layer over Docker and other container types. Might be worth a look!

Kudos to the Google team building it! https://skaffold.dev

To get a similar experience of preevy up, first we’ll need to split the build and deploy using process or alternatively employ tools that orchestrate build-tag-push-update-sync flow like Skaffold/Tilt.

We will be using docker and microk8s from Canonical. For running our software during development, we will be using skaffold which is a great tool developed by Google.

To add more context, if you are developing containers in a local dev environment, the minimum you should have is the Google Cloud SDK and Skaffold. The SDK will allow you to programmatically interact with Googleapis e.g. auth, services, resources. Skaffold will allow you to build and deploy to the cloud similar to working with a local dev environment.

I have used both Skaffold and Devspace. I prefer the latter.

I wonder if it has some overlap with https://skaffold.dev/.

K3d and Skaffold for local development

skaffold.dev - build in k8s - no more asking for the database password. All the plumbing to the backend is just done so it's easier for them to test and demo any branch