otomi-core VS kube-prometheus

An example of a self-hosted Kubernetes-based PaaS is Otomi. Install Otomi on your Kubernetes cluster, compose your platform (by activating the required capabilities) and build, deploy and expose apps in just a couple of minutes. Heroku, but Kubernetes native and running on your own cluster.

This video shows some of the new features of Otomi version 0.19.0 that will be released in Week 11 2023. Follow us on GitHub and be the first to try it out: https://github.com/redkubes/otomi-core

Otomi

Kubernetes is 'only' a multi-node cluster kernel. Some call it the Linux of the cloud.

And because K8s is only a kernel, there are now over 2000+ (open source) projects, all adding some extra functionality to it. Be it for observability, security, or networking. But all of these projects don't really collaborate and end-users don't ask for maturity of individual projects, they want sets/stacks of projects that integrate well.

Now every company has created some Stack with applications and configurations for Kubernetes, all trying to reinvent the wheel and spending an often shocking $ in doing so.

So here is my take:

- Let's create a new category in the Cloud Native Computing Foundation (CNCF) landscape and call it Integrated Stacks for K8s

- To be accepted, a stack needs to provide an open integration framework for other projects to add/integrate their apps

- Just like aLinux distro, each stack is ideal for some specific use case(s)

- A stack can be installed in one run, contains integrated apps that work out-of-the-box, has a (web) UI that acts as a desktop environment to provide easy and secure access to all features. Call it a new user experience for Kubernetes

Wouldn't it be great to have a list of all Kubernetes stacks available that everyone can use (and contribute to)? Just like (in the Linux analogy) you can choose between Linux Mint, Fedora, or Ubuntu.

We already created the first: https://github.com/redkubes/otomi-core

The last one is mostly an observability stack with Prometheus, Metric server, and Prometheus adapter to have excellent insights into what is happening on the cluster. You can reuse the same stack for autoscaling by repurposing all the data collected for monitoring.

kustomize Kube Prometheus

On the other hand, the Kube-prometheus project provides documentation and scripts to operate end-to-end Kubernetes cluster monitoring using the Prometheus Operator, making easier the process of monitoring the Kubernetes cluster.

For our load testing we’ve deployed Temporal on Kubernetes, and we’re using MySQL for the persistence backend. The MySQL instance has 4 CPU cores and 32GB RAM, and each Temporal service (Frontend, History, Matching, and Worker) has 2 pods, with requests for 1 CPU core and 1GB RAM as a starting point. We’re not setting CPU limits for our pods—see our upcoming Temporal on Kubernetes post for more details on why. For monitoring we’ll use Prometheus and Grafana, installed via the kube-prometheus stack, giving us some useful Kubernetes metrics.

The 2 most common approaches I have seen are kube-prometheus-stack and kube-prometheus..

- I'm missing a lot of the Grafana dashboards that are provisioned during the deployment, not sure why as it has worked before, and wanted to add them after install... I believe it's different ConfigMaps like the one in kube-prometheus but I was wondering if there's a way to force provisioning them all again at once (multiple k8s, node_exporter, vm, etc)?

Check out the kube Prometheus project -- https://github.com/prometheus-operator/kube-prometheus It's a bit heavy, but the included recording rules and dashboards give you a great start at understanding your cluster.

The actual link to the prometheus/grafana bundle: https://github.com/prometheus-operator/kube-prometheus

Here’s a list of what’s installed: https://github.com/prometheus-operator/kube-prometheus/tree/main/manifests