Rolling Updates Without Wishful Thinking

A rollout is safe only if readiness reflects ability to serve, capacity covers surge, and termination drains traffic. kubectl rollout status, history, pause, resume and undo expose the state machine. progressDeadlineSeconds detects a stalled rollout; minReadySeconds requires sustained readiness before progress counts.

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: The new version returns 200 on a shallow health endpoint before caches warm. Kubernetes routes traffic, latency spikes, and the rollout still appears healthy. Probes must represent user-serving readiness.

Calculate the rollout envelope

For ten replicas with maxSurge: 25% and maxUnavailable: 20%, the rollout may create up to thirteen total Pods and allow two unavailable after percentage rounding rules. Verify real capacity, quota and disruption constraints before release. Watch kubectl rollout status deploy/web, ReplicaSet counts and endpoint readiness together. A rollout completing means the controller met its conditions, not that latency or errors stayed acceptable.

Scenario: New Pods become ready after one successful probe, then fail under traffic. Add startup protection, representative readiness and minReadySeconds; use application error and latency gates outside Kubernetes.
Scenario: Old Pods receive TERM while a load balancer still sends connections. Coordinate endpoint removal, preStop only when necessary, application drain behavior and grace period.

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 rolling-update-hpa-scaling. Open /labs/kubernetes, choose rolling-update-hpa-scaling, 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.