Version Skew and Safe Upgrades

Read release notes and deprecations, back up etcd, verify health and capacity, upgrade control-plane components before nodes, then kubelets according to supported skew. Drain nodes with disruption awareness and validate workloads between stages. Pin and review add-on compatibility, especially CNI, CSI and ingress.

Analogy: Kubernetes is a thermostat, not a remote control. You declare the temperature; independent controllers measure reality and keep acting. Debugging means finding which sensor, rule or actuator prevents convergence.

Read the object as evidence

kubectl get RESOURCE NAME -n NAMESPACE -o yaml
kubectl describe RESOURCE NAME -n NAMESPACE
kubectl get events -n NAMESPACE --sort-by=.metadata.creationTimestamp
kubectl explain RESOURCE.spec

Do not memorize these as a ritual. The first command exposes desired and observed state, the second connects conditions and events, the third supplies a timeline, and the fourth checks the server's schema. Compare what the controller was asked to do with what it reports doing. Then test the narrowest hypothesis.

Scenario: An upgrade removes an API used only by a dormant CronJob. The main deployment looks healthy until the next schedule fails. Inventory stored and applied APIs before maintenance.

Upgrade one compatibility boundary at a time

Inventory deprecated APIs in manifests and stored objects, read release and component notes, back up etcd, and verify spare capacity. Upgrade control-plane components first, then nodes within supported skew, validating DNS, CNI, CSI, ingress, metrics and admission between stages. Drain one failure domain carefully and keep rollback limits clear; an etcd data migration may not be reversed by downgrading binaries.

Scenario: Core workloads survive the upgrade, but a monthly CronJob uses a removed API and cannot create its next Job. Exercise dormant and scheduled paths in pre-upgrade validation.
Tip: Pin add-on versions against the target Kubernetes release and test webhooks early; they intercept API writes during the maintenance itself.

Production reasoning

Ask four questions: Who owns this object? What dependency must become ready next? Which controller reports the blocking condition? What evidence would disprove my current theory? This prevents symptom-driven changes. Record the context, namespace, object generation, image digest and recent rollout before mutation; a recreated Pod may erase the evidence you needed.

Warning: Running is not the same as ready, healthy, durable or correct. Kubernetes status is layered. Confirm the application-level outcome as well as the object state.
Goal: Put this model into practice in the Kubernetes lab taints-pdb-maintenance. Open /labs/kubernetes, choose taints-pdb-maintenance, predict the failure path before changing anything, then use the simulator's check command to validate the finished state.

Deliberate practice

Before the lab, write the expected object relationship and the first three commands you will run. Afterward, explain why the fix converged and name one tempting change that would only mask the symptom. Repeat using an explicit namespace and a structured output format. This prediction-observation-explanation loop is what turns command familiarity into production judgment.

45-minute investigation

  1. Map (5 min): draw the owner-to-child chain and mark every namespace, selector, identity and dependency involved.
  2. Predict (5 min): write one expected status condition, one likely event and one log or metric signal before opening the lab.
  3. Observe (10 min): collect YAML, describe output and ordered events. Do not mutate state. Record which observation disproves your first theory.
  4. Repair (15 min): make the smallest declarative correction, watch the responsible controller converge, and verify the user-facing path rather than stopping at Running.
  5. Stress (10 min): change one relevant constraint - replica count, label, readiness, resource value or placement rule - predict the outcome, observe it, then restore the known-good declaration.
Tip: Keep a short incident note with symptom, evidence, hypothesis, change, result. Across four sections this produces a reusable runbook instead of a pile of remembered commands.