What is Kubernetes monitoring and what are the key components to observe?

Kubernetes monitoring covers multiple layers, each requiring different tooling and instrumentation. A Kubernetes cluster has at minimum these observable layers:

Control plane components: The API server, etcd, scheduler, and controller manager each expose their own Prometheus metrics. API server latency and request rates, etcd database size and disk fsync latency, and scheduler binding latency are critical signals for cluster health. The kube-state-metrics exporter converts Kubernetes object state (pod phase, deployment replicas, node conditions) into Prometheus metrics.

Node-level resources: Node Exporter (or the Windows Exporter) runs as a DaemonSet and collects host-level metrics: CPU, memory, filesystem, and network. These feed the USE method analysis for each node.

Pod and container metrics: The kubelet exposes the cAdvisor metrics endpoint, which provides CPU, memory, and network usage per container. These are scraped by Prometheus and enable per-pod resource utilization dashboards.

Application metrics: Each application exposes its own /metrics endpoint. ServiceMonitor or PodMonitor custom resources (from the Prometheus Operator) tell Prometheus which services to scrape.

Events: Kubernetes events (OOMKilled, CrashLoopBackOff, ImagePullError) are critical for understanding pod failure patterns. They can be shipped to a log aggregator using tools like eventrouter or Kubernetes event exporter.

The kube-prometheus-stack Helm chart bundles Prometheus Operator, Alertmanager, Grafana, and a set of pre-built dashboards and alert rules, making it the fastest path to a complete Kubernetes monitoring setup.

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