Cordon, Drain and Node Recovery

Cordon prevents new scheduling; drain evicts ordinary workloads while respecting PDBs and controller ownership; uncordon returns capacity. DaemonSets, local storage and unmanaged Pods require explicit choices. Investigate NotReady through kubelet, runtime, disk, network and certificate evidence.

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 responder force-drains through a PDB during a database incident, removing quorum. Maintenance speed never outranks workload invariants.

Drain with workload invariants intact

Cordon first, inspect Pods and PDBs on the node, then use drain and read every refused eviction. DaemonSet Pods are skipped with the appropriate flag; mirror/static Pods and local data need separate decisions. After maintenance, verify kubelet, runtime, CNI, storage attachments and node conditions before uncordon. For NotReady nodes, gather journal, disk, certificate and network evidence if reachable.

Scenario: Drain evicts a Pod using emptyDir after an engineer adds delete-emptydir-data casually. The controller recreates compute but the Pod-scoped dataset is gone. Flags encode data-loss choices, not cleanup convenience.
Warning: Uncordoning a node because its Ready condition briefly turns true can reintroduce flapping capacity. Observe sustained health and a canary workload first.

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 taints-pdb-maintenance. Open /labs/kubernetes, choose taints-pdb-maintenance, 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.