Incident Library
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Cases2 cases

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  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

    On Friday 19 July 2019 the Grafana Cloud Hosted Prometheus service, which is built on the Cortex project, was down for 26 minutes. New error-budget-based Prometheus alerts paged the on-call engineer within 4 minutes of the start.

    Trigger

    An engineer created a new dedicated Cortex cluster for a large customer. Its configuration did not include the new pod priorities, so all its pods got the default medium priority, and the Kubernetes cluster did not have enough free resources to fit it.

    Root cause
    Component
    Contributing factor
    Detection

    New error-budget-based Prometheus alerts paged the on-call engineer within 4 minutes of the start of the outage.

    Remediation
    • Scaled up the underlying Kubernetes cluster to fit both the new and the production Cortex clusters
    • Diagnosed and fixed the out-of-memory errors in the authenticating reverse proxies in front of Cortex
    Action items
    • Give the production ingesters the high priority class, which should have been done before medium became the default
    • Have more people review changes to configuration that is global to the cluster
    • Require a design document for every configuration change outside a namespace
    • Automate the sizing of the frontend authenticating reverse proxy so it does not run out of memory under overload
    • Investigate the default backoff and scaling behaviour of Prometheus to prevent a stampeding herd of retries
    Lessons

    Medium should not have become the default priority until the production ingesters had the high priority, and that designation should have reached production sooner. The change was considered too minor for a design document and was only discussed in a GitHub issue; cluster-global configuration needs more scrutiny. On the positive side, once Cortex had enough capacity it recovered by itself, and Grafana Loki helped confirm that the ingesters exited gracefully.

Check your cluster for this

These 2 cases involve In-memory cache and Container memory limit. 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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