Kubernetes Observability
Kubernetes observability instruments clusters and workloads to surface metrics, logs, and traces that reveal what is happening inside a dynamic container environment. It covers monitoring infrastructure health, application performance, and debugging failed deployments.
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Recommended first:kubernetes-fundamentals
Intro
Kubernetes Observability
A Kubernetes cluster is a distributed system running your distributed systems — so when something misbehaves, "look at the server" stops being an instruction anyone can follow. Kubernetes observability is about knowing where the platform's signals live — health probes, events, logs, metrics — and how they compose into an investigation path. The good news: Kubernetes is unusually self-describing, and most answers are one or two kubectl commands away if you know which layer to ask.
The mental model: observability in Kubernetes happens at two levels that must not be confused. The platform level — is the Pod scheduled, is the container restarting, is the node under pressure — and the application level — is your code doing the right thing. Kubernetes gives you the first natively and gives your applications the plumbing (probes, log capture, metrics APIs) to deliver the second. General observability theory is covered in the Observability Fundamentals course; this one is about where Kubernetes itself keeps the truth.
Health: probes tell the kubelet what "working" means
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Sources
- https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/
Supports
- Liveness/readiness/startup probe semantics and failure consequences
- Probe mechanisms (httpGet, tcpSocket, grpc, exec) and tuning parameters
- Guidance against liveness probes with external dependencies; startup probes for slow-starting apps
- https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/
Supports
- Probe definitions within the Pod lifecycle; readiness gating Service endpoints
- https://kubernetes.io/docs/concepts/cluster-administration/logging/
Supports
- stdout/stderr convention, runtime capture, kubectl logs including --previous
- Node-level rotation; log loss on node failure
- Cluster-level logging architectures — node agent DaemonSet, sidecars, direct shipping
- Kubernetes providing no native cluster-level log storage
- https://kubernetes.io/docs/tasks/debug/debug-cluster/resource-usage-monitoring/
Supports
- Resource metrics pipeline — kubelet/cAdvisor, metrics-server, Metrics API, kubectl top, HPA
- Full monitoring pipeline distinction; metrics-server not a full monitoring solution
- https://kubernetes.io/docs/concepts/cluster-administration/system-metrics/
Supports
- Prometheus-format /metrics endpoints on Kubernetes components
- https://kubernetes.io/docs/concepts/cluster-administration/kube-state-metrics/
Supports
- Object-state metrics exported from the API (replicas, phases, conditions)
- https://kubernetes.io/docs/tasks/debug/debug-application/debug-running-pod/
Supports
- Debugging workflow — describe, logs, exec, ephemeral debug containers (kubectl debug), node debugging
- CrashLoopBackOff investigation via previous logs and events
- https://kubernetes.io/docs/tasks/debug/
Supports
- Application-level vs cluster-level troubleshooting split
- https://kubernetes.io/docs/reference/kubernetes-api/cluster-resources/event-v1/
Supports
- Events as API objects with limited retention (default ~1h TTL)
- https://kubernetes.io/docs/reference/using-api/health-checks/
Supports
- API server livez/readyz/healthz endpoints and verbose checks