Selectors and Affinity

nodeSelector is exact and simple. Node affinity adds expressions and preferred rules. Pod affinity co-locates related Pods; anti-affinity separates failure domains but can be expensive and unsatisfiable. Topology spread constraints often express balanced resilience more directly.

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: Hard anti-affinity requires every replica in a unique zone but the cluster has two zones and three replicas. The third remains Pending by design. Decide which rule may soften under constrained capacity.

Express preference separately from requirement

Required node affinity and nodeSelector filter candidates; preferred affinity changes scores. Pod anti-affinity can prevent replicas sharing a topology domain, while topology spread can balance skew with clearer behavior when domains are missing. Always model the number of replicas, available domains and behavior when a domain is down. Test labels with kubectl get nodes --show-labels.

Scenario: Three replicas require unique zones in a two-zone development cluster, so one never schedules. Production resilience became development deadlock. A soft rule or whenUnsatisfiable: ScheduleAnyway may fit the nonproduction objective.
Warning: Preferred rules are not guarantees. If compliance requires a node class, use required placement plus admission and verify the underlying node labels are protected from kubelet self-modification.

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.