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

Cases2 cases

1 to 2 of 2

  1. Symptoms

    A production EKS cluster on Kubernetes 1.30 went into read-only mode: the API server rejected every request that would change an object, such as creating pods or scaling deployments, and deleting objects to free space did not work either.

    Trigger

    Kyverno 1.12 kept creating ephemeralreports objects that piled up in etcd until the database passed EKS's size limit.

    Root cause
    Component
    Contributing factor
    Detection

    The cluster rejected writes; the audit logs carried the "etcdserver: mvcc: database space exceeded" error.

    Remediation
    • AWS raised the etcd storage limit from 8/10GB to 12GB to unlock writes
    • an AWS engineer deleted the leaked Kyverno ephemeralreports objects directly from etcd
    Action items
    • Monitor the etcd database size with apiserver_storage_size_bytes and never let it reach 8GB
    • Upgrade Kyverno 1.12 to 1.12.4, which fixes the ephemeralreports pile-up, or disable reporting for admission events and tune --aggregationWorkers
    Lessons

    On EKS, once etcd hits its size limit the cluster becomes read-only and only AWS can clean it up, so cleanup scripts must run before the limit is reached. Watch the etcd database size and object counts per resource, especially for controllers that create report objects.

  2. Symptoms

    Between 5:42 AM and 7:16 AM PT on 8 November 2023 a large share of requests to OpenAI failed with 502 or 503 errors. Every model and API endpoint saw significant failures.

    Trigger

    A morning with dramatically more completions than any previous day pushed the routing layer nodes, which already had a chronic memory problem, past their memory limits.

    Root cause
    Component
    Contributing factor
    Remediation
    • limited incoming traffic
    • redeployed the routing service en masse
    • ramped traffic back up slowly
    Action items
    • Pre-allocate the responseBuffer and reuse it instead of allocating a new one in a loop (3X improvement in memory and CPU usage)
    • Adjust the configured memory limits to an appropriate level so the service has significant headroom
    • Add rate limit controls to shed traffic more gracefully
    • Increase the service's capacity
    • Change alerting to catch the underlying memory behaviour before it escalates into service issues
    • Configure autoscaling for the service, which was not possible before because added capacity could hurt an upstream service
Check your cluster for this

These 2 cases involve etcd and Kubernetes API server. 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.

By root cause1