Helm, Kustomize and Release Assets

Helm templates and packages applications with values and release history. Kustomize overlays declarative patches without templating. Prefer Kustomize for controlled environment differences and Helm for distributable applications with a values API. Render before applying, diff changes, pin chart versions, and treat generated YAML as production code.

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 unpinned chart dependency changes between deployments. The same Git commit produces different resources. Lock dependencies and store reviewed values per environment.

Render, diff and own generated YAML

Use helm template or helm upgrade --install --dry-run to inspect rendered resources, and kubectl diff -k overlays/prod before apply. Keep secrets out of values committed to source. In Helm, understand that a release owns rendered objects and hooks can run imperative work. In Kustomize, keep bases reusable and overlays small enough that reviewers can see the effective change.

Scenario: A production overlay replaces an entire container list to change one environment value, silently dropping probes and resources from the base. Rendered-diff review catches patch semantics that source review misses.
Tip: Pin chart and image versions, commit dependency locks, and test rendered manifests against the target API and admission policies in CI. Reproducibility includes generator inputs, not just the final Git SHA.

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 configmap-secret-mount. Open /labs/kubernetes, choose configmap-secret-mount, 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.