Incident Library
  • Root cause
  • Component
  • Symptom
  • Contributing factor
  • Case

Cases2 cases

1 to 2 of 2

  1. 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.

    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
    Component
    Contributing factor
    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.

  2. Symptoms

    Right after a pull request to the infrastructure-as-code repository was merged, every API endpoint of one InfluxDB Cloud production cluster began returning 404 errors. Writes and reads failed alike: no one could collect data, scheduled tasks failed to run and external queries did not work.

    Trigger

    A merged pull request whose rendered YAML added a new Argo CD Application and AppProject that an automation error had named app01-cluster01, the name of the existing production Application, instead of app02-cluster01; reviewers did not notice the app01 and app02 difference.

    Root cause
    Component
    Contributing factor
    Detection

    Monitoring and alerting did not detect the issue right away, so the team's first response was to try to roll the change back rather than plan a recovery. The write-up does not say how the team found out.

    Remediation
    • stopped the revert of the merged pull request
    • restored the etcd metadata cluster
    • recreated the missing Kubernetes objects so the retained PersistentVolumes became available, then recreated the StatefulSets
    • restored the Zookeeper volumes from an hourly Velero backup
    • rebuilt the Kafka StatefulSet one pod at a time with health and readiness checks disabled
    • accepted writes into the Kafka write-ahead log and scaled up the write path with more replicas and higher memory requests and limits
    • recreated the storage pods, which downloaded data from object storage, reindexed it and replayed the write-ahead log
    • re-enabled Argo CD, queries, dashboards and scheduled tasks, restored replica counts and ran final QA checks
    Action items
    • Write runbooks for restoring state when Kubernetes objects such as PersistentVolumeClaims are deleted but the data on the underlying volumes is preserved
    • Set every volume in every environment to retain data when its PersistentVolumeClaim is deleted
    • Improve the process for handling public-facing incidents
    • Store generated YAML as one object per file, so an overwrite of an existing object shows up as such in review
    • Detect duplicate objects when generating YAML and warn before review, treating objects with the same group, namespace and name as the same even when the apiVersion string differs
    • Add the Argo CD Prune=false annotation to all stateful resources and to Namespace objects managed by Argo CD
    • Set FailOnSharedResource=true on Argo CD Applications so Argo CD fails before changing an object another Application manages or managed
    Lessons

    The biggest gap was that monitoring did not notice the deletion quickly, which pushed the team toward a reflex rollback; in a stateful system a revert can create a new, empty instance instead of bringing the old one back. Retained volumes and clients that retry writes are what kept data loss at zero. Rendering many objects into one file let an overwrite of an existing object look like an addition, so generated configuration needs one object per file and duplicate detection that compares objects the way Kubernetes does. Stateful resources need protection from pruning, and that protection must cover their Namespace too, or deleting the namespace removes everything inside it.

Check your cluster for this

These 2 cases involve Persistent volumes and stateful sets and Argo CD. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

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