Cases4 cases

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1. IL-0063 VPN outage after recreating a Redis StatefulSet restarted the whole Redis cluster without readiness checks

   AdGuard VPN2025

   Symptoms

   The day began with an alert that several instances of the Redis cluster were running out of disk space. That part was fixed quickly. The outage came an hour later: right after the Redis StatefulSet was deleted and recreated at 8:10 UTC, Kubernetes restarted every Redis pod within about five minutes, and problems started to appear.

   Service outageOOMKilledProbe failuresHigh latency

   Trigger

   To make Git match the volumes that had been resized by hand, the Redis StatefulSet was deleted with orphan (non-cascading) deletion and recreated with the new storage template; Kubernetes reconciled the new spec by restarting the entire Redis cluster.

   Root cause

   Configuration change

   Component

   Persistent volumes and stateful setsIn-memory cacheContainer memory limit

   Contributing factor

   No health probesUnverified assumptionSingle point of failureRetry stormNo headroom

   Detection

   An alert about Redis instances running out of disk space started the work; the outage itself showed up as backend errors about Redis connections right after the StatefulSet was recreated.

   Remediation

   • made the Redis masters forget obsolete node identities, reset the affected replicas and rejoined them, then waited for replication to catch up
   • identified the component that was actually running out of memory, scaled the backend service and raised memory limits for that component
   • temporarily relaxed probes so backend instances took traffic immediately instead of flapping between ready and not ready
   • reverted the temporary workarounds at 10:00 UTC

   Action items

   • Add startup probes so a Redis pod is not marked ready until its dataset is loaded
   • Build an own control plane for Redis that handles restarted pods stuck to an old master address
   • Reduce dependence on the single Redis cluster: move some important data to other stores, such as a separate Redis cluster per server, and use Kafka for some backend components

   Lessons

   The disk resize was not the problem; the cleanup afterwards was. An immutable field pushes operators toward deleting and recreating the controller, and that can set off a full reconcile. Startup and readiness probes are what slow a rolling restart down to a safe pace; without them a distributed store restarts in the most fragile way. Recovery plans must expect a cluster to heal slowly, and dependent services need to degrade gracefully while it does.

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2. IL-0044 Prometheus OOMKilled in a loop while replaying its write-ahead log after a kube-prometheus-stack upgrade

   Saifeddine Rajhi2024

   Symptoms

   After an upgrade of the kube-prometheus-stack Helm chart, several pods stayed not ready. Among them was prometheus-prometheus-operator-prometheus-0, showing 3/4 Running and a recent termination.

   OOMKilledProbe failuresMonitoring outage

   Trigger

   During the kube-prometheus-stack Helm chart upgrade, Prometheus had to replay its write-ahead log on startup, and the replay needed more memory than its limit allowed.

   Root cause

   Capacity exhaustion

   Component

   PrometheusContainer memory limit

   Contributing factor

   Unknown limitNo headroom

   Detection

   The team noticed pods not ready after the Helm chart upgrade and found the OOMKilled termination with kubectl describe pod.

   Remediation

   • ran promtool tsdb analyze inside the Prometheus pod to find high-cardinality metrics such as haproxy_server_http_responses_total

   Action items

   • Verify that the memory overhead during WAL replay stays within acceptable limits, for example 10 to 15%, and do not run Prometheus close to its memory limit
   • Optimise storage, scraping and remote write configuration and upgrade to newer Prometheus releases
   • Recover automatically from OOM crash loops, for example by deleting the WAL on OOM events
   • Enforce scrape limits so Prometheus stops scraping targets when memory use passes a threshold

   Lessons

   Size Prometheus for its restart, not only for steady state: WAL replay can need far more memory than normal running, and a Prometheus that already runs near its limit can be OOM-killed during the replay. Much of Prometheus memory use comes from cardinality, which promtool tsdb analyze can measure even when Prometheus itself is down.

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3. IL-0191 A label selector accidentally added to the Loki distributor Service matched only the new pods of a rollout, which took all write traffic, were OOM-killed and stalled the rollout

   Grafana Labs2022

   Symptoms

   During a rollout of the Grafana Loki distributor Deployment in one Grafana Cloud Logs environment, the write path went down for about 25 minutes. The newly started distributor pods were OOM-killed over and over, never passed their health and readiness checks, and the rollout stopped making progress.

   Service outageOOMKilledLoad imbalanceProbe failures

   Trigger

   Step 1 of moving the Loki ring off a separately deployed key-value store onto embedded memberlist added a new label, gossip_ring_member: 'true', to the distributor pods and Service, together with a small config change, and the change required a rollout of the distributors.

   Root cause

   Configuration change

   Component

   Configuration templatingService discoveryContainer memory limit

   Contributing factor

   Unreviewed defaultsIgnored early signalUnrepresentative staging

   Detection

   The first clue was kubectl get pods and the kube_deployment_status_replicas_updated metric showing that only 16 of 140 distributor pods had been updated.

   Remediation

   • scaled the distributor Deployment out horizontally so the load spread over more pods with the new label and the rollout could continue
   • reverted the migration step to end the outage
   • set service_ignored_labels:: [$._config.gossip_member_label] and passed it to k.util.serviceFor so the label is no longer copied into the Service selector

   Action items

   • Update the function that generates a Service from a Deployment or StatefulSet to handle edge cases, including this one

   Lessons

   A Service whose selector is built by copying every label of a workload changes which pods it matches as soon as a new label is added. During a rollout it then matches only the new pods, which receive all the traffic before there are enough of them. A few errors during a rollout in other environments hid the problem, because those environments gave distributors more memory and could absorb the shift. Manifest diffs kept for every pull request made it possible to see that the Service selector had changed.

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4. IL-0503 mybinder.org launches failed after a GKE node ran out of memory and its nginx-ingress proxy pod crash-looped

   Binder Project2017

   Symptoms

   On 30 November 2017 at 16:59 PST, mybinder.org operators noticed that Grafana looked wrong and that launching a new server failed with an error that a temporary user could not be created. Pods that were already running kept running, but no new ones were being created.

   Service outageContainer crash loopProbe failuresOOMKilled

   Trigger

   A build container, which ran without a memory limit, most likely used up all the memory on the node that ran the proxy pod.

   Root cause

   Capacity exhaustion

   Component

   Ingress controllerContainer memory limitJupyterHub

   Contributing factor

   No resource requests or limitsHarmful manual actionNo alert on limit

   Detection

   An operator happened to look at Grafana at 16:59 PST, saw that it looked wrong, and confirmed the error by launching a new server.

   Remediation

   • Cordoned the node that had run out of memory
   • Uncordoned the node once the proxy was working

   Action items

   • Limit the memory available to build pods
   • Change the JupyterHub configuration so user pods are not cleaned up when the jupyterhub pod restarts
   • Nominate one person to drive the response, possibly in a shared terminal, so only one person changes the cluster
   • Alert when something is wrong with the cluster, for example by counting 4xx responses on the jupyterhub pod

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