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

Cases2 cases

1 to 2 of 2

  1. Symptoms

    For about six months, applications on Omio's Google Kubernetes Engine clusters stalled at random: they got stuck, stopped answering health check requests or lost network connections, and nothing the team looked at explained why.

    Trigger

    Multi-threaded workloads running with CPU limits on nodes whose kernel did not yet have the fix for a known CFS quota bug that causes unnecessary throttling.

    Root cause
    Component
    Contributing factor
    Detection

    Months of random stalls and failed health checks led the team to read the cgroup cpu.stat throttling counters inside the pods, which showed far more throttling than the limits should allow.

    Remediation
    • removed CPU limits until the kernel with the CFS bug fix was deployed across all clusters
    Action items
    • Deploy the kernel with the CFS quota bug fix across all clusters
    • Monitor utilisation, saturation and error metrics, API latency and error rates to confirm the change behaves as expected
    Lessons

    A CPU limit is a time quota per 100 ms period, not a share of cores, so a multi-threaded container can be stopped for most of each period long before it uses the cores it was given, and a kernel bug can make that worse. Throttling is invisible in standard Kubernetes metrics and has to be read from the cgroup counters. Removing limits trades away protection against a container that takes a whole node, which is only acceptable when runtimes are tuned.

  2. Symptoms

    At about 15:00 UTC on 19 July 2018, monitoring alerted on rising 5xx responses from several APIs, most notably pilot and tiles. Logins failed first, because the pilot service fetches the user's profile, and from there all services became unresponsive. Every affected API ran on the production Kubernetes cluster.

    Root cause
    Component
    Contributing factor
    Detection

    Monitoring alerted on an increase in 5xx responses from several APIs, most notably pilot and tiles.

    Remediation
    • restarted the kubelets on the stuck worker nodes, which did not last
    • restarted all worker nodes in a staggered order starting at about 18:00 UTC, which took about 1.5 hours
    Action items
    • Restart nodes on a rolling schedule to keep nodes and kubelets healthy until the fixes land upstream and reach the cloud provider, Microsoft Azure
    Lessons

    When nodes keep falling back to NotReady after kubelet restarts and pods fail on network and DNS errors, a known upstream bug may be the cause; matching the symptoms to the upstream issue tracker, with help from the community, found the cause and the fix candidates.

Check your cluster for this

These 2 cases involve kubelet and Container CPU 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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