metrics-server
Scalable and efficient source of container resource metrics for Kubernetes built-in autoscaling pipelines. (by kubernetes-sigs)
k8s-prometheus-adapter
An implementation of the custom.metrics.k8s.io API using Prometheus (by DirectXMan12)
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metrics-server | k8s-prometheus-adapter | |
---|---|---|
40 | 13 | |
5,419 | 1,821 | |
2.3% | 1.6% | |
8.6 | 6.2 | |
5 days ago | 11 days ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
metrics-server
Posts with mentions or reviews of metrics-server.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-03.
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Upgrading Hundreds of Kubernetes Clusters
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.
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Deploy Secure Spring Boot Microservices on Amazon EKS Using Terraform and Kubernetes
and the Metrics Server.
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☸️ Managed Kubernetes : Our dev is on AWS, our prod is on OVH
Metrics-server is installed by default on OVH, and has to be installed manually on AWS/EKS cluster.
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Kubernetes HPA on AKS is failing with error 'missing request for cpu'
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
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Factors affecting performance of job running in a pod
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
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Autoscaling Ingress controllers in Kubernetes
It's an autoscaler with a metrics server (so I don't need to install 2 different tools).
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Checking the Capability of ChatGPT for DevOps Automation Solution Design
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!
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plz help
Id go for k3s then install metrics-server, then you can deploy some hpa’s
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Autoscaling Nodes in Kubernetes
# 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
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Korifi : API Cloud Foundry V3 expérimentale dans Kubernetes …
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
k8s-prometheus-adapter
Posts with mentions or reviews of k8s-prometheus-adapter.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-03.
-
Upgrading Hundreds of Kubernetes Clusters
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.
-
Helm: Is there a way to access templates of a sibling subchart
I'm deploying kube-prometheus-stack along with prometheus-adapter in my monitoring stack for custom metrics.
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Deploy prometheus-adapter with kube-prometheus-stack monitoring stack?
I would like to see if anyone deployed prometheus-adapter and kube-prometheus-stack together for monitoring?
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Horizontal Pod Autoscale
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.
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Steps to write own adaptor
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
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Monitoring Your Spacelift Account via Prometheus
A prometheus-adapter installation.
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Advanced Features of Kubernetes' Horizontal Pod Autoscaler
Prometheus adapter to get custom/external metrics from Prometheus instance into Kubernetes API.
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Pod spread by percentage
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
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Practical Introduction to Kubernetes Autoscaling Tools with Linode Kubernetes Engine
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).
- How to scale containers that are unrelated to physical traits like CPU or Memory?
What are some alternatives?
When comparing metrics-server and k8s-prometheus-adapter you can also consider the following projects:
prometheus - The Prometheus monitoring system and time series database.
keda - KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
kube-state-metrics - Add-on agent to generate and expose cluster-level metrics.
k9s - 🐶 Kubernetes CLI To Manage Your Clusters In Style!
kube-prometheus - Use Prometheus to monitor Kubernetes and applications running on Kubernetes
cluster-proportional-autoscaler - Kubernetes Cluster Proportional Autoscaler Container
istio - Connect, secure, control, and observe services.
spring-auto-scaling-k8
minikube - Run Kubernetes locally
helm-charts - Prometheus community Helm charts
metrics-server vs prometheus
k8s-prometheus-adapter vs keda
metrics-server vs kube-state-metrics
k8s-prometheus-adapter vs k9s
metrics-server vs kube-prometheus
k8s-prometheus-adapter vs cluster-proportional-autoscaler
metrics-server vs istio
k8s-prometheus-adapter vs spring-auto-scaling-k8
metrics-server vs k9s
k8s-prometheus-adapter vs prometheus
metrics-server vs minikube
k8s-prometheus-adapter vs helm-charts