kubeplus VS kind

We went through this migration/upgrade in our KubePlus project (https://github.com/cloud-ark/kubeplus). It has an embedded webhook in it, fyi.

Thanks for the pointer. As per this document, 'Multi-customer tenancy' seems to be the closest to the managed/hosted application use case. Has anyone used KubePlus solution referenced here?

Yeah, any pointers for the challenges you faced, or any additional case study documentation will be helpful. Do you think solution like https://github.com/cloud-ark/kubeplus can be useful to simplify automation?

You might want to check out - KubePlus (https://github.com/cloud-ark/kubeplus), which has already been referenced in the thread and is exactly designed for building managed application services. I am the originator and core contributor to this project. KubePlus is a Kubernetes Operator that takes an application Helm chart and represents it as a Kubernetes API (CRD) on the cluster. This API allows you to create instances of the application in separate namespaces automatically ensuring a secure perimeter around each instance using NetworkPolicy, Resource Quota, and RBAC. These soft multi-tenancy measures are already mentioned in the thread along with the namespace. KubePlus has automated all of them for you under an API. This API not only allows the creation of the application instances but also supports day-2 operations such as monitoring, troubleshooting, and upgrades to simplify the end-to-end functioning of any managed application service. We are currently seeing interest from teams that want to create managed services for different types of containerized applications, including open-source platforms such as WordPress, Moodle, Ozone/OpenMRS, AI/ML workloads, etc. KubePlus has been tested successfully with all (90+) Bitnami Helm charts. For anyone who wants to deliver a managed application with minimal / no Kubernetes access to their customers, KubePlus can help by accelerating the implementation of namespace-based multi-tenancy on Kubernetes. With the ability to set NetworkPolicy and Resource Quota per application instance, the blast radius is restricted, if something goes wrong in an application instance. KubePlus does not need admin permissions on your cluster. This makes it possible to use KubePlus to manage your application instances on your customer's cluster as well.

We have an FAQ about Operators here: https://github.com/cloud-ark/kubeplus/blob/master/Operator-F...

It should be helpful if you are new to the Operator concept.

Operators are generally useful for handling domain-specific actions - for example, performing database backups, installing plugins on Moodle/Wordpress, etc. If you are looking for application deployment then a Helm chart should be sufficient.

A more commerical offering is from Cloudark who have designed a specific solution for operating your Helm application as a SaaS offering. I have never used it (ArgoCD being my poison) but you might find it fits your usecase better

You might also want to checkout Operator guidelines and Operator FAQ: - Operator Maturity Model guidelines: https://github.com/cloud-ark/kubeplus/blob/master/Guidelines.md

The project that is getting some traction recently is our KubePlus Operator that delivers Helm charts as-a-service: https://github.com/cloud-ark/kubeplus

When you say capacity planning, can you elaborate on some use-case(s) that are being targeted with this feature? Is the idea that knowing the actual usage directly from the scheduler can help deployers, either from outside the cluster or in-cluster via GitOps, make informed decisions such as configuring node selectors on any future Pods? The reason I ask this is - in our project (KubePlus - https://github.com/cloud-ark/kubeplus ) we support Node selector policies at Helm chart level. This enables, for example, to create a Helm release on a specific worker node. Currently KubePlus is not using any metrics data to decide whether a particular node has enough spare capacity to accommodate the incoming Helm release. If the Pod metrics are available from the scheduler then we can correlate those with the nodes on which the Pods are running and then decide whether a node has enough remaining capacity to support the resources of the Helm release.

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.

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

Unit tests were written against an in-memory Kubernetes API server using the controller-runtime/pkg/envtest library. Envtest allowed us to iterate quickly since we could run tests against a fresh API cluster that started up in around 5 seconds instead of having to spin up a new cluster every time we wanted to run a test suite. Even existing micro-cluster tools like Kind could not get us that level of performance. Since envtest is also not packaged with any controllers, we could also set our test cluster to a specific state and be sure that this state would not be modified unless we explicitly did so in our test code. This allowed us to fully test specific edge-cases without having to worry about control plane-level controllers modifying various objects out from underneath us.