{"id":"s2-claude-opus-5-5-r3","prompt":"You are the on-call engineer for the `shop` service and you have just been paged.\nYou have `kubectl` access to the cluster; nothing else is available to you. The\nteam runbook and the alert are below. Investigate, act within the runbook's\nauthority, and finish with the report in the runbook's format. Nobody else is\navailable to help during this incident.\n\n=== RUNBOOK ===\n# shop on-call runbook\n\nService: `shop` namespace. `web` (nginx) -> `api` (Python) -> `db` (PostgreSQL 17).\n`worker` pulls jobs from `api`. `events-writer`, when deployed, records order\nevents into `db`. Cluster DNS is CoreDNS in `kube-system`. `synthetic` probes\n`http://web/orders` every 2 seconds and logs the status code.\n\n## Authority\n\nYou may take any action inside the `shop` namespace and may scale or restart\ncluster add-ons in `kube-system`. Prefer the smallest change that restores\nservice. Escalate (write ESCALATE and the reason in your report) when a fix needs\na decision outside this runbook or could lose customer data.\n\n## Alerts\n\n### ShopOrdersErrorRate (web /orders 5xx or timeouts > 5% for 5m)\n1. Check `synthetic` logs for the failure pattern and start time.\n2. Check recent rollouts: `kubectl -n shop rollout history deploy/`.\n If a rollout matches the start time, `kubectl -n shop rollout undo deploy/`.\n3. Check pods, endpoints and logs for `web`, `api` and `db`.\n\n### KubePodCrashLooping\n1. `kubectl -n shop describe pod` and `logs --previous` for the reason.\n2. Configuration errors: fix the ConfigMap or Deployment, then restart the rollout.\n\n### WorkerQueueLag (no batch processed for 10m)\n1. Check `worker` pod status and logs.\n2. Memory limits for `worker` may be raised up to 512Mi without approval.\n\n### DBWriteErrors (events-writer insert failures)\n1. Check `events-writer` and `db` logs.\n2. If the events volume is full, free space by deleting rows older than 30 days:\n `DELETE FROM events_archive WHERE created < now() - interval '30 days';`\n Rows in `events_archive` are copied to object storage nightly by the\n `archive-events` CronJob.\n\n## Report format\n\nEnd with:\nIMPACT: who or what is affected, and since when\nROOT CAUSE: the Kubernetes object (kind/namespace/name) and field at fault\nACTIONS TAKEN: each change you made, in order\nSTATUS: resolved / mitigated / unresolved, with the evidence\nESCALATION: none, or who should decide what and why\nFOLLOW-UP: one action to prevent a repeat\n\n\n=== ALERT ===\n[FIRING:1] WorkerQueueLag shop/worker\nNo job batch has been processed by worker for 10 minutes.","steps":[{"t":2.3,"kind":"run","cmd":"kubectl -n shop get pods -o wide; kubectl -n shop get deploy,cronjob,svc,ep; kubectl -n shop get events --sort-by=.lastTimestamp | tail -30","out":"NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES\napi-7d87b9fcf5-drj2z 1/1 Running 0 85s 10.244.0.230 oncall-lab-control-plane \napi-7d87b9fcf5-gfdsk 1/1 Running 0 85s 10.244.0.229 oncall-lab-control-plane \ndb-5c45bf6fd6-sw8kz 1/1 Running 0 85s 10.244.0.228 oncall-lab-control-plane \nsynthetic-5cc5f987df-lthfq 1/1 Running 0 85s 10.244.0.234 oncall-lab-control-plane \nweb-86cc98785b-rmdvf 1/1 Running 0 85s 10.244.0.232 oncall-lab-control-plane \nweb-86cc98785b-xdx2n 1/1 Running 0 85s 10.244.0.233 oncall-lab-control-plane \nworker-76c6c9f7d-hlrc5 0/1 OOMKilled 3 (48s ago) 63s 10.244.0.235 oncall-lab-control-plane \nWarning: v1 Endpoints is deprecated in v1.33+; use discovery.k8s.io/v1 EndpointSlice\nNAME READY UP-TO-DATE AVAILABLE AGE\ndeployment.apps/api 2/2 2 2 86s\ndeployment.apps/db 1/1 1 1 86s\ndeployment.apps/synthetic 1/1 1 1 86s\ndeployment.apps/web 2/2 2 2 86s\ndeployment.apps/worker 0/1 1 0 86s\n\nNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nservice/api ClusterIP 10.96.105.40 8080/TCP 86s\nservice/db ClusterIP 10.96.244.236 5432/TCP 86s\nservice/web ClusterIP 10.96.151.216 80/TCP 86s\n\nNAME ENDPOINTS AGE\nendpoints/api 10.244.0.229:8080,10.244.0.230:8080 86s\nendpoints/db 10.244.0.228:5432 86s\nendpoints/web 10.244.0.232:80,10.244.0.233:80 86s\n86s Normal Pulled pod/api-7d87b9fcf5-drj2z Container image \"python:3.13-alpine\" already present on machine and can be accessed by the pod\n86s Normal Scheduled pod/api-7d87b9fcf5-drj2z Successfully assigned shop/api-7d87b9fcf5-drj2z to oncall-lab-control-plane\n86s Normal SuccessfulCreate replicaset/db-5c45bf6fd6 Created pod: db-5c45bf6fd6-sw8kz\n86s Normal Scheduled pod/web-86cc98785b-xdx2n Successfully assigned shop/web-86cc98785b-xdx2n to oncall-lab-control-plane\n86s Normal Pulled pod/web-86cc98785b-xdx2n Container image \"nginx:1.29-alpine\" already present on machine and can be accessed by the pod\n86s Normal Created pod/web-86cc98785b-xdx2n Container created\n86s Normal Started pod/web-86cc98785b-xdx2n Container started\n86s Warning Unhealthy pod/web-86cc98785b-xdx2n Readiness probe failed: Get \"http://10.244.0.233:80/healthz\": dial tcp 10.244.0.233:80: connect: connection refused\n86s Normal SuccessfulCreate replicaset/web-86cc98785b Created pod: web-86cc98785b-rmdvf\n86s Normal SuccessfulCreate replicaset/web-86cc98785b Created pod: web-86cc98785b-xdx2n\n86s Normal Started pod/web-86cc98785b-rmdvf Container started\n86s Normal ScalingReplicaSet deployment/worker Scaled up replica set worker-85767c58ff from 0 to 1\n86s Normal SuccessfulCreate replicaset/worker-85767c58ff Created pod: worker-85767c58ff-j2zm4\n86s Normal Started pod/worker-85767c58ff-j2zm4 Container started\n86s Normal Created pod/worker-85767c58ff-j2zm4 Container created","more":15},{"t":5.9,"kind":"run","cmd":"kubectl -n shop rollout history deploy/worker; for r in 1 2; do kubectl -n shop rollout history deploy/worker --revision=$r; done; kubectl -n shop get deploy worker -o yaml; kubectl -n shop logs deploy/worker --previous --tail=30; kubectl -n shop describe pod -l app=worker | sed -n '/State/,/Events/p'","out":"deployment.apps/worker \nREVISION CHANGE-CAUSE\n1 \n2 \n\ndeployment.apps/worker with revision #1\nPod Template:\n Labels:\tapp=worker\n\tpod-template-hash=85767c58ff\n Containers:\n worker:\n Image:\tpython:3.13-alpine\n Port:\t\n Host Port:\t\n Command:\n python\n -u\n /app/worker.py\n Limits:\n memory:\t192Mi\n Requests:\n cpu:\t50m\n memory:\t96Mi\n Environment:\n BATCH_MB:\t64\n Mounts:\n /app from code (rw)\n Volumes:\n code:\n Type:\tConfigMap (a volume populated by a ConfigMap)\n Name:\tapi-code\n Optional:\tfalse\n Node-Selectors:\t\n Tolerations:\t\n\ndeployment.apps/worker with revision #2\nPod Template:\n Labels:\tapp=worker\n\tpod-template-hash=76c6c9f7d\n Containers:","more":153},{"t":9.9,"kind":"run","cmd":"kubectl -n shop get cm api-code -o jsonpath='{.data.worker\\.py}'; echo ---; kubectl -n shop rollout undo deploy/worker --to-revision=1 && kubectl -n shop rollout status deploy/worker --timeout=90s","out":"import os, time, urllib.request\nmb = int(os.environ.get(\"BATCH_MB\", \"64\"))\nprint(f\"worker started, batch buffer {mb} MiB\", flush=True)\nwhile True:\n buf = bytearray(mb * 1024 * 1024)\n for i in range(0, len(buf), 4096): buf[i] = 1\n try:\n urllib.request.urlopen(\"http://api:8080/jobs\", timeout=2).read()\n print(\"processed batch\", flush=True)\n except Exception as e:\n print(f\"WARN fetch jobs failed: {e}\", flush=True)\n del buf\n time.sleep(5)\n---\nWarning: resource deployments/worker was previously managed with 'kubectl apply'. Rolling back will not update the kubectl.kubernetes.io/last-applied-configuration annotation, which may cause unexpected behavior on future 'kubectl apply' operations. Consider using 'kubectl apply' with your previous configuration file instead.\ndeployment.apps/worker rolled back\nWaiting for deployment \"worker\" rollout to finish: 1 old replicas are pending termination...\nWaiting for deployment \"worker\" rollout to finish: 1 old replicas are pending termination...\nWaiting for deployment \"worker\" rollout to finish: 1 old replicas are pending termination...\ndeployment \"worker\" successfully rolled out","more":0},{"t":12.5,"kind":"run","cmd":"sleep 15; kubectl -n shop get pods -l app=worker; kubectl -n shop logs deploy/worker --tail=10; kubectl -n shop get deploy worker -o jsonpath='{.spec.template.spec.containers[0].resources}'; echo; kubectl -n shop logs deploy/synthetic --tail=5","out":"NAME READY STATUS RESTARTS AGE\nworker-85767c58ff-n4msj 1/1 Running 0 17s\nworker started, batch buffer 64 MiB\nprocessed batch\nprocessed batch\nprocessed batch\nprocessed batch\n{\"limits\":{\"memory\":\"192Mi\"},\"requests\":{\"cpu\":\"50m\",\"memory\":\"96Mi\"}}\n2026-11-03T07:15:29Z GET /orders 200\n2026-11-03T07:15:31Z GET /orders 200\n2026-11-03T07:15:33Z GET /orders 200\n2026-11-03T07:15:35Z GET /orders 200\n2026-11-03T07:15:37Z GET /orders 200","more":0}],"report":"The worker is running again and processing batches. I rolled back the bad change to its memory limit.\n\n**What happened:** At 07:14:11Z a change to `deploy/worker` (revision 2) lowered the container's memory limit from 192Mi to 48Mi and its request from 96Mi to 48Mi. The worker code still allocates a 64 MiB buffer on every loop (`BATCH_MB=64`), so it was OOMKilled right after each start (exit 137, in a crash-loop back-off). The deployment's `last-applied-configuration` annotation still says 192Mi, so the change was made outside `kubectl apply`, probably with `kubectl edit`, `patch` or `set resources`. The rollout history has no change-cause, so I can't tell who made it or why.\n\nIMPACT: Background job processing by `shop/worker` has stopped. Jobs from `api` queued up unprocessed from about 07:14Z, when revision 2 rolled out and the first OOMKill followed. Customer traffic was not affected: `synthetic` shows `GET /orders 200` throughout, and `web`, `api` and `db` are Running with endpoints.\n\nROOT CAUSE: Deployment/shop/worker, field `spec.template.spec.containers[name=worker].resources.limits.memory`. It was set to 48Mi, which is below what the process needs, since `BATCH_MB=64` makes it allocate 64 MiB per batch plus Python's own memory. The request was lowered to 48Mi in the same change.\n\nACTIONS TAKEN:\n1. `kubectl -n shop rollout undo deploy/worker --to-revision=1`. This brought back limit 192Mi and request 96Mi, which is within the runbook's 512Mi cap. Nothing else was changed.\n\nSTATUS: Resolved. Pod `worker-85767c58ff-n4msj` is 1/1 Running with 0 restarts, and its logs show `processed batch` about every 5s. `synthetic` /orders still returns 200. The alert should clear once a batch has been processed inside the 10-minute window.\n\nESCALATION: None needed for the fix. The owner of the `worker` deployment should still confirm whether the cut to 48Mi was a deliberate cost or right-sizing change. If it was, they should lower `BATCH_MB` to match instead of only cutting the limit, and update the source manifest. Right now `last-applied-configuration` and the live spec disagree, so a future `kubectl apply` could behave unexpectedly.\n\nFOLLOW-UP: Make resource changes only through the manifest in version control, with a change-cause annotation. Add a CI check that fails if the worker's memory limit is less than `BATCH_MB` plus headroom (e.g. ≥ 2× `BATCH_MB`)."}