Kubernetes Metrics Server
Metrics Server is a scalable, efficient source of container resource metrics for Kubernetes built-in autoscaling pipelines.
Metrics Server collects resource metrics from Kubelets and exposes them in Kubernetes apiserver through Metrics API
for use by Horizontal Pod Autoscaler and Vertical Pod Autoscaler. Metrics API can also be accessed by
making it easier to debug autoscaling pipelines.
Metrics Server is not meant for non-autoscaling purposes. For example, don’t use it to forward metrics to monitoring solutions, or as a source of monitoring solution metrics. In such cases please collect metrics from Kubelet
/metrics/resource endpoint directly.
Metrics Server offers:
- A single deployment that works on most clusters (see Requirements)
- Fast autoscaling, collecting metrics every 15 seconds.
- Resource efficiency, using 1 mili core of CPU and 2 MB of memory for each node in a cluster.
- Scalable support up to 5,000 node clusters.
You can use Metrics Server for:
- CPU/Memory based horizontal autoscaling (learn more about Horizontal Autoscaling)
- Automatically adjusting/suggesting resources needed by containers (learn more about Vertical Autoscaling)
Don’t use Metrics Server when you need:
- Non-Kubernetes clusters
- An accurate source of resource usage metrics
- Horizontal autoscaling based on other resources than CPU/Memory
For unsupported use cases, check out full monitoring solutions like Prometheus.
Metrics Server has specific requirements for cluster and network configuration. These requirements aren’t the default for all cluster distributions. Please ensure that your cluster distribution supports these requirements before using Metrics Server:
- Metrics Server must be reachable from kube-apiserver by container IP address (or node IP if hostNetwork is enabled).
- The kube-apiserver must enable an aggregation layer.
- Nodes must have Webhook authentication and authorization enabled.
- Kubelet certificate needs to be signed by cluster Certificate Authority (or disable certificate validation by passing
--kubelet-insecure-tlsto Metrics Server)
- Container runtime must implement a container metrics RPCs (or have cAdvisor support)
Latest Metrics Server release can be installed by running:
kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml
Installation instructions for previous releases can be found in Metrics Server releases.
|Metrics Server||Metrics API group/version||Supported Kubernetes version|
*For <1.16 requires passing
--authorization-always-allow-paths=/livez,/readyz command line flag
Latest Metrics Server release can be installed in high availability mode by running:
kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/high-availability.yaml
Note that this configuration requires having a cluster with at least 2 nodes on which Metrics Server can be scheduled.
Also, to maximize the efficiency of this highly available configuration, it is recommended to add the
--enable-aggregator-routing=true CLI flag to the kube-apiserver so that requests sent to Metrics Server are load balanced between the 2 instances.
Metrics Server requires the
CAP_NET_BIND_SERVICE capability in order to bind to a privileged ports as non-root.
If you are running Metrics Server in an environment that uses PSPs or other mechanisms to restrict pod capabilities, ensure that Metrics Server is allowed
to use this capability.
This applies even if you use the
--secure-port flag to change the port that Metrics Server binds to to a non-privileged port.
Starting from v0.5.0 Metrics Server comes with default resource requests that should guarantee good performance for most cluster configurations up to 100 nodes:
- 100m core of CPU
- 200MiB of memory
Metrics Server resource usage depends on multiple independent dimensions, creating a Scalability Envelope. Default Metrics Server configuration should work in clusters that don’t exceed any of the thresholds listed below:
|Quantity||Namespace threshold||Cluster threshold|
|#Pods per node||70||70|
|#Deployments with HPAs||100||100|
Resources can be adjusted proportionally based on number of nodes in the cluster. For clusters of more than 100 nodes, allocate additionally:
- 1m core per node
- 2MiB memory per node
You can use the same approach to lower resource requests, but there is a boundary where this may impact other scalability dimensions like maximum number of pods per node.
Depending on your cluster setup, you may also need to change flags passed to the Metrics Server container. Most useful flags:
--kubelet-preferred-address-types- The priority of node address types used when determining an address for connecting to a particular node (default [Hostname,InternalDNS,InternalIP,ExternalDNS,ExternalIP])
--kubelet-insecure-tls- Do not verify the CA of serving certificates presented by Kubelets. For testing purposes only.
--requestheader-client-ca-file- Specify a root certificate bundle for verifying client certificates on incoming requests.
You can get a full list of Metrics Server configuration flags by running:
docker run --rm k8s.gcr.io/metrics-server/metrics-server:v0.5.0 --help
This Helm chart can deploy the metric-server service in your cluster.
Metrics Server is a component in the core metrics pipeline described in Kubernetes monitoring architecture.
For more information, see:
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This project is maintained by SIG Instrumentation
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