murder VS microk8s

> I have many thousands of machines running in multiple datacenters and even getting a ~4mb binary distributed onto them without saturating the network (100mbit) and slowing everything else down, is a bit of a challenge.

You may find murder[1] of some use.

[1] https://github.com/lg/murder

Is there a repository for Fork Freshness? I could see the twitter account ignoring requests in the future and the same fate could fall to this project. I would recommend releasing the project under AGPL-3.0-or-later to partially solve this issue so the project can continue in the event of abandonment. I could see people contributing code to search for projects in other known forges such as GitLab, Sourceforge, Savannah, Gitea, pagure, and sourcehut as sometimes projects are forked outside of the original forge.

I have noticed this issue that Fork Freshness tries to solve. My example is Twitter's project murder https://github.com/lg/murder When a project becomes unmaintained whether officially or unofficially, the future home is often lost unless the original points to the new home at the top of the README file. You can dig within GitHub in the Insights > Network section to get a visual glimpse of what has changed since. https://github.com/lg/murder/network The original repository put up a notice that the project is unmaintained and archived the project which effectively ends the project in practice. In this case, ervinb's fork seems to be the most active commits before being abandoned. https://github.com/ervinb/murder Other forks also had independent commits that never were pulled into other projects. Looking at the network method fails to differentiate 30 grammar fixes from 30 new features without digging into each promising looking fork. Even then, you may miss a single commit that included more work then the entirety of the other commits. Disclosure: I have not worked on murder.

This is a serious problem and I hope we solve it.

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.