k8s-prometheus-adapter VS k9s

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).

Pierre: The first tool I recommend is K9s. It's not just a time-saver but a productivity booster. With its intuitive interface, you can speed up all the usual kubectl commands, access logs, edit resources and configurations, and more. It's like having a personal assistant for your cluster management tasks.

The last thing you really need is a common set of tools that you want fingertip access to. I really commonly use LazyGit and K9s in my day job so those are the tools I will show off in this article.

K9s is your best friend (get it? 🐶) when exploring your cluster via the terminal. It shares commonality with Vim for its interaction style using shortcuts and starting commands with: but don’t let that discourage you. K9s keeps a vigilant eye on Kubernetes activities, providing real-time information and intuitive commands for resource interaction.

k9s: brew install k9s

I would like to put in a vote for k9s, which is also on the list at Terminal Trove. [0] It's the most convenient tool I've ever found for Kubernetes management. Based on that experience I'll definitely be checking out Harlequin.

[0] https://k9scli.io/

$ wget https://github.com/derailed/k9s/releases/download/v0.29.1/k9s_Darwin_amd64.tar.gz $ tar -xzf k9s_Darwin_amd64.tar.gz $ sudo mv k9s /usr/local/bin/

All in all, run things, do some kubectl apply -f something.yml every day, install k9s, and try to configure a big one cluster at some point.

(K9s is one of my favorite tools for navigating Kubernetes clusters through the CLI).

K9s is an open-source, terminal-based UI for interacting with your Kubernetes clusters, making navigating, observing, and managing your apps easier. If you use Kubectl but wish it was easier and faster to use, K9s might be just what you're looking for!

k9s