k8s-prometheus-adapter 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.

I'm deploying kube-prometheus-stack along with prometheus-adapter in my monitoring stack for custom metrics.

I would like to see if anyone deployed prometheus-adapter and kube-prometheus-stack together for monitoring?

For us it is saturation of CPU and thread pool. It's implemented by exposing metrics of the thread pool to prometheus and turning that into a custom metric. (see) Looking at scaling based on job queue length next.

If you are using Prometheus or kube-prometheus-stack, you will need https://github.com/kubernetes-sigs/prometheus-adapter We are using it to scale our Pods based on number of messages in RabbitMQ queue. There also a walkthrough on https://github.com/kubernetes-sigs/prometheus-adapter/blob/master/docs/walkthrough.md

A prometheus-adapter installation.

Prometheus adapter to get custom/external metrics from Prometheus instance into Kubernetes API.

I never tested this but you have customized metrics API if the value % is available should work from my point of view Check this here https://github.com/kubernetes-sigs/prometheus-adapter

CPU and memory might not be the right metrics for your application to make scaling decisions. In such cases, you can use HPA (or VPA) with custom metrics as an alternative. To use custom metrics for autoscaling, you can use a custom metrics adapter instead of the Kubernetes Metrics Server. Popular custom metrics adapters are the Prometheus adapter and Kubernetes Event-Driven Autoscaler (KEDA).

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!