kube-prometheus VS metrics-server

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

Kube-State-Metrics is only one part of the Kubernetes metrics ecosystem. Metrics-Server is another commonly discussed component that you should also include in your observability strategy. Whereas Kube-State-Metrics reports metrics about the states of objects in your cluster, Metrics-Server exposes real-time CPU and memory resource utilization metrics for your Nodes and Pods.

apiVersion: metal.sidero.dev/v1alpha1 kind: ServerClass metadata: name: masters spec: qualifiers: hardware: - system: manufacturer: QEMU memory: totalSize: "12 GB" configPatches: - op: add path: /machine/network/interfaces value: - deviceSelector: busPath: "0*" dhcp: true vip: ip: "10.1.1.50" - op: add path: /machine/network/nameservers value: - 1.1.1.1 - 1.0.0.1 - op: replace path: /machine/install value: disk: none diskSelector: size: '< 100GB' - op: replace path: /cluster/network/cni value: name: none # name: "custom" # urls: # - "https://raw.githubusercontent.com/kubebn/talos-proxmox-kaas/main/manifests/talos/cilium.yaml" - op: replace path: /cluster/proxy value: disabled: true - op: replace path: /machine/kubelet/extraArgs value: rotate-server-certificates: true - op: replace path: /cluster/inlineManifests value: - name: cilium contents: |- apiVersion: v1 kind: Namespace metadata: name: cilium labels: pod-security.kubernetes.io/enforce: "privileged" - op: replace path: /cluster/extraManifests value: - https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml - https://raw.githubusercontent.com/alex1989hu/kubelet-serving-cert-approver/main/deploy/standalone-install.yaml

Just add `--kubelet-insecure-tls`

that solves the problem!

https://github.com/kubernetes-sigs/metrics-server/issues/196

Before deploying anything into this namespace, it’s important first to verify that the metrics server is deployed and configured in your cluster. This metrics server is part of how Kubernetes implements autoscaling rules. Check whether it’s installed by running:

Metrics Server is a Kubernetes addon that collects CPU and memory resource utilization information at the Node and Pod level. It's a lightweight, single-cluster, Kubernetes-only alternative to more complex monitoring solutions like Prometheus.

(kubespray-venv) kube-master1:~# curl -LO https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml nano components.yaml

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.

and the Metrics Server.

Metrics-server is installed by default on OVH, and has to be installed manually on AWS/EKS cluster.

I have also installed metrics-server (though not sure whether that was required or not) using the following statement:kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.3.6/components.yaml