draft VS microk8s

Draft exists to help folks get up and running on Kubernetes more easily.

Acorn will take care of creating your Kubernetes manifest files but it expects that you have a Dockerfile ready to containerize your app. Instead of writing a Dockerfile from scratch, we'll use the Draft CLI to generate one for us. Draft is able to detect languages used within your project and generate the appropriate Docker manifest to containerize your app.

The development concept at level 2 is essentially the same as at level 1. The code is deployed via a pipeline to a Kubernetes cluster. However, at this stage, developers are using special CLI tools, such as Skaffold, Draft, or Tilt that detect the file changes by the developer and then automatically trigger the pipelines.

Draft is an open-source project that streamlines Kubernetes development by taking a non-containerized application and generating the Dockerfiles, Kubernetes manifests, Helm charts, Kustomize configurations, and other artifacts associated with a containerized application. Draft can also create a GitHub Action workflow file to quickly build and deploy applications onto any Kubernetes cluster.

Azure/draft where it's an archived repository

Does anyone know what happened to Microsofts Kubernetes project Draft? On GitHub it is marked as archived: https://github.com/Azure/draft

Many of the PaaS open source implementations whether built on top of k8s or otherwise turning up dead or getting lower and lower traction. Examples would be flynn.io, for example or draft (https://github.com/Azure/draft) seems also sort of dead. What gives? What's happening? Is there no place for PaaS anymore because there are too many already? Or k8s deployment and operation is that simplified to the point that with Helm, Tekton and Brigade etc - there's a general convergence on a DIY K8s and away from PaaS layers even if they're built atop K8s?

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.