Diegolden

Topic 5: Metrics And Health

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Topic 5: Metrics And Health

Official baseline: (The Kubernetes Authors, 2026a, 2026b). My working version: Metrics turn cluster behavior into signals that humans and controllers can act on.

Mental Model

Metrics turn cluster behavior into signals that humans and controllers can act on.

Notes

  • Metrics Server helps autoscaling but is not a full observability system.
  • Control plane and node metrics catch platform issues before app alerts do.
  • Alert on symptoms and saturation, not every noisy counter.

Homelab Angle

Track CPU, memory, disk pressure, Pod restarts, pending Pods, and certificate expiry first.

Verify It

  • Read the object status before changing the manifest.
  • Check events for the controller or node that is actually complaining.
  • Confirm the official source linked below still matches the cluster version you run.

Common Failure Modes

  • Treating the YAML object as the system, instead of one input to a reconciliation loop.
  • Debugging from outside the cluster when the failure only exists inside cluster networking or node state.
  • Forgetting that Kubernetes version, addon version, and runtime behavior are linked.

Sources

  • Metrics for Kubernetes System Components - source path: content/en/docs/concepts/cluster-administration/system-metrics.md, commit 8cc9e19b8eec8d5cf49eacd66f86a81648edb1a0.
  • Workload Autoscaling - source path: content/en/docs/concepts/workloads/autoscaling.md, commit 8cc9e19b8eec8d5cf49eacd66f86a81648edb1a0.
  • Kubernetes documentation is licensed under CC BY 4.0; these notes are original commentary and link back to the official source.
The Kubernetes Authors. (2026a). Metrics for Kubernetes System Components. https://kubernetes.io/docs/concepts/cluster-administration/system-metrics/
The Kubernetes Authors. (2026b). Workload Autoscaling. https://kubernetes.io/docs/concepts/workloads/autoscaling/

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