Choose the Right Workload

Use Deployment for interchangeable stateless replicas, StatefulSet for stable identity and ordered storage, DaemonSet for one Pod per eligible node, Job for finite work, and CronJob for schedules. A bare Pod expresses none of these operational promises. Workload choice encodes identity, completion, placement and rollout semantics.

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: A queue consumer is deployed as a Job even though it never completes, causing misleading status and retry behavior. Start with the process lifecycle, then choose the controller.

Decide from lifecycle, identity and placement

Ask whether replicas are interchangeable, whether the process must finish, whether each node needs one copy, and whether stable identity or storage follows an ordinal. Deployment answers interchangeable service; StatefulSet answers stable member; DaemonSet answers node-local agent; Job answers finite completion. Confirm the generated controller behavior with kubectl get deploy,sts,ds,job -o wide and ownership references.

Scenario: A log collector runs as a Deployment with three replicas. Some nodes have no collector while another has two. A DaemonSet encodes the actual one-per-eligible-node requirement and follows nodes joining or leaving.
Scenario: A worker consumes forever but is modeled as a Job. It never completes, so completion metrics and retry semantics lie. Use a Deployment and make queue lag part of autoscaling and health.

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 job-restart-policy. Open /labs/kubernetes, choose job-restart-policy, 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.