kube-prometheus VS kubernetes-the-hard-way

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

As I delved into the world of Kubernetes, I opted for what seemed like the scenic route—until reality hit. With determination and a sprinkle of naivety, I plunged into crafting a Kubernetes cluster from scratch. Little did I know, I was in for a wild ride of commands, configurations, and complexities. But fear not! With the guiding light of a course based on the notorious "Kubernetes The Hard Way" GitHub repository, albeit customized for AWS instead of Google Cloud, I embarked on my journey to build my digital fortress, one step at a time.

Another resource that I found pretty helpful was "Kubernetes the Hard Way" by Kelsey Hightower despite its complexity.

https://github.com/kelseyhightower/kubernetes-the-hard-way

I recently published Kubernetes the Harder Way, a guide loosely based on Kelsey Hightower's Kubernetes the Hard Way, but lenghtier, more explanatory, broader in scope, and - most importantly - harder, by targeting a local machine instead of Google Cloud Platform.

How about this? It will setup a kubernetes cluster on GCP with 3 worker nodes?

https://github.com/kelseyhightower/kubernetes-the-hard-way

Some costs here but click on the google cloud calc as it seems to have gone up since he wrote this:

If you wanna go all in: https://github.com/kelseyhightower/kubernetes-the-hard-way

Oh, and both: "Kubernetes up and Running" and "Production Kubernetes" are great books.

Using Kubernetes The Hard Way is a great way to learn it deeply and quickly.

If you wanna learn it deeply and quickly, Kelsey Hightower's Kubernetes the hard way is fantastic!