Configure the health checks contract

You own a Kubernetes retrieval-and-generation API at GET /livez and GET /readyz. The goal is to separate process liveness from traffic readiness so orchestration restarts dead processes without creating restart storms during dependency degradation. The measurable target is livez answers within 100 ms whenever the event loop works; readyz answers within 200 ms and requires config loaded plus a successful model credential probe cached for 30 seconds; three failed readiness probes remove the pod, while liveness waits 30 seconds and fails five times before restart. The known production tension is deep checks catch dependency faults but couple fleet availability to remote services; shallow checks preserve capacity but may route traffic to pods unable to complete expensive requests.

The contract for Health checks must survive a clean checkout. Pin tool and image versions, provide sample inputs, and fail startup on malformed configuration. The service boundary is GET /livez and GET /readyz on a Kubernetes retrieval-and-generation API. Before starting dependencies, list required ports, identities, secrets, storage, and cleanup steps. Use synthetic credentials and redacted fixtures; production tokens do not make a staging lab more realistic.

Create the configuration as a reviewed artifact:

livenessProbe:
  httpGet: { path: /livez, port: 8080 }
  initialDelaySeconds: 30
  periodSeconds: 10
  timeoutSeconds: 1
  failureThreshold: 5
readinessProbe:
  httpGet: { path: /readyz, port: 8080 }
  periodSeconds: 5
  timeoutSeconds: 1
  failureThreshold: 3
  successThreshold: 2

For every field, document type, valid range, default, reload behavior, and failure behavior. Numbers are incomplete without units. A timeout of 40 could mean milliseconds or seconds; a ratio of 0.005 could be a fraction or percentage. Reject unknown keys so a typo cannot silently select a permissive default. If live reload is supported, expose a configuration revision in telemetry and keep the previous valid revision for rollback.

Run the setup workflow:

kubectl apply -f deploy/answer-api.yaml
kubectl get pod -l app=answer-api -w

The expected result is not merely exit code zero. Confirm the process identity, dependency reachability, effective configuration, and one health or introspection response. Then restart the component and prove durable state remains correct. Run the setup twice to expose non-idempotent creation, conflicting ports, or duplicate policy entries. Capture the exact versions in the evidence bundle.

The production objective remains livez answers within 100 ms whenever the event loop works; readyz answers within 200 ms and requires config loaded plus a successful model credential probe cached for 30 seconds; three failed readiness probes remove the pod, while liveness waits 30 seconds and fails five times before restart. Therefore setup must expose probe_success{probe}, probe_duration_seconds, kube_pod_container_status_restarts_total, endpoint_ready, and request error rate by pod before meaningful traffic arrives. Query each metric by its stable labels and verify an idle system is distinguishable from a broken scraper. Generate one event and check that the relevant counter changes by exactly one. Reject high-cardinality labels and ensure sensitive configuration values are redacted from startup logs.

Model a bad configuration: remove one required field, set one threshold just outside its range, and reference an unavailable dependency. Each case should fail predictably, explain the field, and avoid partial state. Next, apply the previous good revision and time recovery. A rollback that requires undocumented shell history is not a rollback plan.

The central design tension is deep checks catch dependency faults but couple fleet availability to remote services; shallow checks preserve capacity but may route traffic to pods unable to complete expensive requests. Record the chosen default, its operational owner, and a date for reevaluation. Complete setup only after a teammate can run it from the repository instructions, produce the same effective configuration, and cleanly tear it down without affecting shared environments.