Declarative Objects and Reconciliation

Every object has apiVersion, kind, metadata and spec. The server adds status, resourceVersion, UID and managed fields. Spec is intent; status is observation. Labels select sets, annotations carry non-identifying metadata, ownerReferences drive garbage collection, and finalizers delay deletion until cleanup completes. YAML is only a transport format - the API object and its lifecycle are the real contract.

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 engineer repeatedly runs imperative create commands. Nobody can review the resulting state and rebuilding staging produces a different system. Committed manifests plus server-side apply provide reviewable intent and field ownership.

Inspect object identity

Compare metadata.generation with a controller condition's observedGeneration; a mismatch means the controller has not processed the latest intent. Use kubectl get pod NAME -o jsonpath='{.metadata.ownerReferences}' to walk ownership and kubectl get ... --show-managed-fields when field managers fight. Deletion with a timestamp but no disappearance usually means a finalizer is waiting. Read the responsible controller before removing finalizers, because forced removal can leak cloud resources or corrupt cleanup.

Scenario: GitOps repeatedly restores a replica count after an engineer scales manually. Neither side is broken: two field owners express competing intent. Choose one owner and make the change at its source.
Tip: Labels are queryable identity; annotations are metadata. Putting a release timestamp in a selector creates a new identity boundary instead of merely recording a release.

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 labels-selectors. Open /labs/kubernetes, choose labels-selectors, 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.