metrics-server VS kind

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

For #2: There are MANY solutions but the simplest and easiest is https://github.com/kubernetes-sigs/metrics-server and then you can use kubectl top to view pods resource usage. If you want fancy graphs, long retention, alerting, analysis, etc. of resource metrics.. then you need to use something like new relic, data dog, or something open source but much more complex like prometheus/thanos/grafana.. or a managed SaaS version of those open source tools... also some k8s platforms like AWS, Spot.io, GKE, OpenShift, may have this stuff built in or turnkey

It's an autoscaler with a metrics server (so I don't need to install 2 different tools).

Sure, here's an example of how to configure the Kubernetes Metrics Server along with the Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) configurations we provided earlier. The Metrics Server is a Kubernetes add-on that provides resource utilization metrics for pods and nodes, which are used by the HPA and VPA to determine when and how to scale an application. First, you'll need to install the Metrics Server into your Kubernetes cluster. You can do this by running the following command: kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml Once the Metrics Server is installed, you can configure your HPA and VPA to use it for metrics by adding the following annotation to your deployment: apiVersion: apps/v1 kind: Deployment metadata: name: my-app annotations: autoscaling.alpha.kubernetes.io/metrics-provider: "metrics-server" With this annotation in place, the HPA and VPA will use the Metrics Server to obtain CPU and memory utilization metrics for your pods and nodes. Here's an updated example of the HPA configuration we provided earlier, with the addition of the Metrics Server configuration: apiVersion: autoscaling/v2beta2 kind: HorizontalPodAutoscaler metadata: name: my-app-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: my-app minReplicas: 1 maxReplicas: 10 metrics: - type: Resource resource: name: cpu targetAverageUtilization: 50 - type: Resource resource: name: memory targetAverageUtilization: 80 In this example, we've added a second metric to the HPA configuration that targets memory utilization at 80%. Here's an updated example of the VPA configuration we provided earlier, with the addition of the Metrics Server configuration: apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: my-app-vpa spec: targetRef: apiVersion: apps/v1 kind: Deployment name: my-app updatePolicy: updateMode: "Off" resourcePolicy: containerPolicies: - containerName: "*" minAllowed: cpu: 50m memory: 256Mi maxAllowed: cpu: 500m memory: 1Gi metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 50 - type: Resource resource: name: memory target: type: Utilization averageUtilization: 80 In this example, we've added two metrics to the VPA configuration that target CPU and memory utilization, with target average utilization of 50% and 80% respectively. I hope this helps you configure the Metrics Server, HPA, and VPA for your application in Kubernetes!

Id go for k3s then install metrics-server, then you can deploy some hpa’s

# Create EKS Cluster with version 1.23 eksctl create cluster -f eks-cluster.yaml # Output like below shows cluster has been successfully created 2022-12-30 16:26:46 [ℹ] kubectl command should work with "/home/ec2-user/.kube/config", try 'kubectl get nodes' 2022-12-30 16:26:46 [✔] EKS cluster "ca-demo" in "us-west-2" region is ready # Deploy the Metric server kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml # Output of the above command looks something like below - serviceaccount/metrics-server created clusterrole.rbac.authorization.k8s.io/system:aggregated-metrics-reader created clusterrole.rbac.authorization.k8s.io/system:metrics-server created rolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader created clusterrolebinding.rbac.authorization.k8s.io/metrics-server:system:auth-delegator created clusterrolebinding.rbac.authorization.k8s.io/system:metrics-server created service/metrics-server created deployment.apps/metrics-server created apiservice.apiregistration.k8s.io/v1beta1.metrics.k8s.io created

ubuntu@korifi:~$ kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.6.2/components.yaml serviceaccount/metrics-server created clusterrole.rbac.authorization.k8s.io/system:aggregated-metrics-reader created clusterrole.rbac.authorization.k8s.io/system:metrics-server created rolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader created clusterrolebinding.rbac.authorization.k8s.io/metrics-server:system:auth-delegator created clusterrolebinding.rbac.authorization.k8s.io/system:metrics-server created service/metrics-server created deployment.apps/metrics-server created apiservice.apiregistration.k8s.io/v1beta1.metrics.k8s.io created ubuntu@korifi:~$ kubectl get po,svc -A NAMESPACE NAME READY STATUS RESTARTS AGE cert-manager pod/cert-manager-74d949c895-w6gzm 1/1 Running 0 13m cert-manager pod/cert-manager-cainjector-d9bc5979d-jhr9m 1/1 Running 0 13m cert-manager pod/cert-manager-webhook-84b7ddd796-xw878 1/1 Running 0 13m kpack pod/kpack-controller-84cbbcdff6-nnhdn 1/1 Running 0 9m40s kpack pod/kpack-webhook-56c6b59c4-9zvlb 1/1 Running 0 9m40s kube-system pod/coredns-565d847f94-kst2l 1/1 Running 0 31m kube-system pod/coredns-565d847f94-rv8pn 1/1 Running 0 31m kube-system pod/etcd-kind-control-plane 1/1 Running 0 32m kube-system pod/kindnet-275pd 1/1 Running 0 31m kube-system pod/kube-apiserver-kind-control-plane 1/1 Running 0 32m kube-system pod/kube-controller-manager-kind-control-plane 1/1 Running 0 32m kube-system pod/kube-proxy-qw9fj 1/1 Running 0 31m kube-system pod/kube-scheduler-kind-control-plane 1/1 Running 0 32m kube-system pod/metrics-server-8ff8f88c6-69t9z 0/1 Running 0 4m21s local-path-storage pod/local-path-provisioner-684f458cdd-f6zqf 1/1 Running 0 31m metallb-system pod/controller-84d6d4db45-bph5x 1/1 Running 0 29m metallb-system pod/speaker-pcl4p 1/1 Running 0 29m projectcontour pod/contour-7b9b9cdfd6-h5jzg 1/1 Running 0 6m43s projectcontour pod/contour-7b9b9cdfd6-nhbq2 1/1 Running 0 6m43s projectcontour pod/contour-certgen-v1.23.2-hxh7k 0/1 Completed 0 6m43s projectcontour pod/envoy-v4xk9 2/2 Running 0 6m43s servicebinding-system pod/servicebinding-controller-manager-85f7498cf-xd7jc 2/2 Running 0 115s NAMESPACE NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE cert-manager service/cert-manager ClusterIP 10.96.153.49 9402/TCP 13m cert-manager service/cert-manager-webhook ClusterIP 10.96.102.82 443/TCP 13m default service/kubernetes ClusterIP 10.96.0.1 443/TCP 32m kpack service/kpack-webhook ClusterIP 10.96.227.201 443/TCP 9m40s kube-system service/kube-dns ClusterIP 10.96.0.10 53/UDP,53/TCP,9153/TCP 32m kube-system service/metrics-server ClusterIP 10.96.204.62 443/TCP 4m21s metallb-system service/webhook-service ClusterIP 10.96.186.139 443/TCP 29m projectcontour service/contour ClusterIP 10.96.138.58 8001/TCP 6m43s projectcontour service/envoy LoadBalancer 10.96.126.44 172.18.255.200 80:30632/TCP,443:30730/TCP 6m43s servicebinding-system service/servicebinding-controller-manager-metrics-service ClusterIP 10.96.147.189 8443/TCP 115s servicebinding-system service/servicebinding-webhook-service ClusterIP 10.96.14.224 443/TCP 115s

To get started, you'll need to install clusteradm and kubectl and start up three Kubernetes clusters. To simplify cluster administration, this article starts up three kind clusters with the following names and purposes:

Kind: is a tool for running local Kubernetes clusters using Docker container "nodes." It was primarily designed for testing Kubernetes itself but can also be used for local development or continuous integration.

Fortunately, just as projects like kind and Minikube enable developers to spin up a local Kubernetes environment in no time, CRC, also known as OpenShift Local and a recursive acronym for "CRC - Runs Containers", offers developers a local OpenShift environment by means of a pre-configured VM similar to how Minikube works under the hood.

I recently purchased a used Lenovo M900 Think Centre (i7 with 32GB RAM) from eBay to expand my mini-homelab, which was just a single Synology DS218+ plugged into my ISP's router (yuck!). Since I've been spending a big chunk of time at work playing around with Kubernetes, I figured that I'd put my skills to the test and run a k3s node on the new server. While I was familiar with k3s before starting this project, I'd never actually run it before, opting for tools like kind (and minikube before that) to run small test clusters for my local development work.

Features compared to https://kind.sigs.k8s.io/

Kind allows you to run a Kubernetes cluster inside Docker. This is incredibly useful for developing Helm charts, Operators, or even just testing out different k8s features in a safe way.

Check out KinD

If you're just messing around, just use kind (https://kind.sigs.k8s.io) or minikube if you want VMs (https://minikube.sigs.k8s.io). Both work on ARM-based platforms.

You can also use k3s; it's hella easy to get started with and it works great.

We'll install APIClarity into a Kubernetes cluster to test our API documentation. We're using a Kind cluster for demonstration purposes. Of course, if you have another Kubernetes cluster up and running elsewhere, all steps also work there.

yes I know there is lens, but it does not allow me to see logs of multiple pods at same time and what is even more important it is not friendly for ephemeral clusters - in my case with help of kind I am recreating whole cluster each time from scratch