- What metrics are exposed by the metrics server?
- Why do the CPU values reported by Metrics Server differ from Prometheus/docker stats/top etc.?
- Why do the memory values reported by Metrics Server differ from Prometheus/docker stats/top etc.?
- How does the metrics server calculate metrics?
- How often is metrics server released?
- Can I run two instances of metrics-server?
- How to run metrics-server securely?
- How to run metric-server on different architecture?
- What Kubernetes versions are supported?
- How is resource utilization calculated?
- How to autoscale Metrics Server?
- Can I get other metrics beside CPU/Memory using Metrics Server?
- How large can clusters be?
- How often metrics are scraped?
Metrics server collects resource usage metrics needed for autoscaling: CPU & Memory.
Metric values use standard kubernetes units (m, Ki), same as those used to
define pod requests and limits (Read more Meaning of CPU, Meaning of memory)
Metrics server itself is not responsible for calculating metric values, this is done by Kubelet.
Metrics Server reports CPU value optimized for horizontal scaling of pods. CPU is reported as the 15 seconds average usage.
Metrics Server reports memory value optimized for vertical autoscaling of pods. Memory is reported as the working set at the instant the metric was collected. In an ideal world, the "working set" is the amount of memory in-use that cannot be freed under memory pressure. However, calculation of the working set varies by host OS, and generally makes heavy use of heuristics to produce an estimate. It includes all anonymous (non-file-backed) memory since Kubernetes does not support swap. The metric typically also includes some cached (file-backed) memory, because the host OS cannot always reclaim such pages.
Metrics Server itself doesn't calculate any metrics, it aggragetes values exposed by Kubelets so they are available in API that can by used for autoscaling. For any problem with metric values please contact SIG-Node.
There is no hard release schedule. A release is done after an important feature is implemented or upon request.
Yes, but it will not provide any benefits. Both instances will scrape all nodes to collect metrics, but only one instance will be actively serving metrics API.
Suggested configuration:
- Cluster with RBAC enabled
- Kubelet read-only port port disabled
- Validate kubelet certificate by mounting CA file and providing
--kubelet-certificate-authorityflag to metrics server - Avoid passing insecure flags to metrics server (
--deprecated-kubelet-completely-insecure,--kubelet-insecure-tls) - Consider using your own certificates (
--tls-cert-file,--tls-private-key-file)
Starting from v0.3.7 docker image k8s.gcr.io/metrics-server/metrics-server should support multiple architectures via Manifests List.
List of supported architectures: amd64, arm, arm64, ppc64le, s390x.
Metrics server is tested against the last 3 Kubernetes versions.
Metrics server doesn't provide resource utilization metrics (e.g. percent of CPU used). Kubectl top and HPA calculate those values by themselves based on pod resource requests or node capacity.
Metrics server scales linearly vertically according to the number of nodes and pods in a cluster. This can be automated using addon-resizer.
No, metrics server was designed to provide metrics used for autoscaling.
Metrics Server was tested to run within clusters up to 5000 nodes with an average pod density of 30 pods per node.
Default 60 seconds, can be changed using metrics-resolution flag. We are not recommending setting values below 15s, as this is the resolution of metrics calculated within Kubelet.