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

Cases3 cases

1 to 3 of 3

  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

    At 08:52 PM IST a report came in from WER that many students could not access the JupyterHub and that it was frozen; pages were stuck and nothing was usable. A rise in activity was visible by 09:02 PM, but the usual fixes, such as opening an incognito window and restarting servers, did not help.

    Trigger

    WER students' pods used a good chunk of their memory limit (tests had shown they almost always use just under 1G of their 2G limit, against a 512M guarantee) on nodes shared with the hub's core pods.

    Root cause
    Component
    Contributing factor
    Detection

    WER reported stuck pages and students unable to reach the hub in a GitHub issue (2i2c-org/docs#27).

    Remediation
    • removed memory overcommit for the WER students
    • raised resource guarantees for all core pods, which restarted them and moved some to a new node
    Action items
    • Put user pods in a separate pool so they do not create pressure on the core pods (infrastructure#89)
    • Set limits on the support infrastructure (Prometheus, Grafana, ingress) as well (infrastructure#90)
    • Document and think about overcommit ratios for memory usage (infrastructure#91)
    • Set up better Grafana dashboards to monitor resource usage (infrastructure#92)
    • Document how people can get kubectl access to the cluster so others can look into issues too (infrastructure#87)
    Lessons

    Core pods without resource guarantees get whatever is left on a node, so when user pods on the same nodes fill the CPU and memory, the hub that serves everyone freezes. The memory overcommit ratio has to come from measured use: WER students used just under 1G against a 512M guarantee, which a 4x overcommit could not hold.

  3. Symptoms

    Right after an updated RedisFailover resource was rolled out, the large Redis database lost all its data and the service went down; the data had to be restored from a backup taken just before the change.

    Trigger

    A RedisFailover change that raised the number of Redis replicas from one to two and, in the same rollout, lowered the containers' memory requests and limits from 22 GB to 19 GB.

    Root cause
    Component
    Contributing factor
    Remediation
    • restored the Redis data from the backup prepared before the rollout
    Action items
    • Report the readiness probe flaw to the Redis Operator project and test the developers' fix
    • Reconsider the use of the Spotahome Redis Operator
    • Clean up the Redis databases and fix the application code that created the excess keys
    • Consider Redis built-in clustering and sharding
    Lessons

    Changing the replica count and the pod resources in one rollout turned a seemingly harmless change into a rolling restart of the only pod with data while the new replica was still empty. A readiness probe that only checks that Redis answers a ping does not tell whether a replica has finished syncing. Operators for critical stateful components need to be evaluated for maturity before production use, and the dump.rdb file should be backed up before any change to the Redis cluster.

Check your cluster for this

These 3 cases involve Container memory limit and Persistent volumes and stateful sets. 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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