Requests, Limits and QoS

Schedulers place Pods using requests; runtimes enforce limits. CPU is compressible and throttled, memory is incompressible and may trigger OOM kill. Guaranteed, Burstable and BestEffort QoS influence eviction priority. Requests are capacity reservations and should come from observed distributions, not guesses.

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 container has a 128Mi memory limit but routinely peaks at 160Mi. Restart loops are enforcement working correctly. Inspect last state and OOMKilled before blaming the application lifecycle.

Distinguish reservation from enforcement

CPU and memory requests drive scheduling. CPU limits normally enforce throttling; memory limits can lead to cgroup OOM kills because memory is not compressible. QoS follows the relationship of requests and limits across every container, including sidecars. Size requests from percentiles and service objectives, then reserve rollout and failure headroom rather than setting request equal to a quiet average.

Scenario: An app requests 100m, uses 900m steadily, and has no CPU limit. It runs until contention, then receives far less CPU because scheduling never reserved its real need. Requests influence both placement and relative contention.
Warning: A very high limit is not capacity. If no node can accommodate the request or memory physically runs out, declared limits cannot create resources.

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 resource-requests-oom. Open /labs/kubernetes, choose resource-requests-oom, 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.