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

Cases2 cases

1 to 2 of 2

  1. Symptoms

    From 16:18 UTC on 28 May 2026 the Lucidworks AI hosted LLMs in the us-southcarolina region were unavailable: queries to llama-3-8b-instruct, llama-3v2-3b-instruct and phi-4-multimodal-instruct returned 500 or 429 errors. Search, embedding models, the Lucidworks Platform UI and passthrough LLM calls kept working.

    Trigger

    A routine Kubernetes patch upgrade on a cluster in us-southcarolina cycled its node pools one after another in quick succession.

    Root cause
    Component
    Contributing factor
    Remediation
    • Deleted the degraded replacement head node that could not pull container images
    • Waited for the LLM image pulls to finish on the replacement nodes
    • Launched new nodes with the required LLM images and redirected Lucidworks AI traffic to them
    Action items
    • Upgrade clusters that host LLM workloads in sequence, with each node pool fully healthy before the next one is upgraded
    • Investigate image pre-loading, such as pre-baked disks or image streaming, for large ML model images
    • Open a support ticket with the cloud provider about the abnormal Docker pull times
    • Coordinate Kubernetes maintenance windows with LLM service owners so they do not clash with ongoing deployments
    • Build tooling to force a Ray cluster failover to the blue or green state instead of waiting for Ray to settle it
  2. Symptoms

    At 08:18 UTC on 13 July 2020, production Kubernetes pods began to be marked unavailable. The loss of pods spread through the clusters and reduced capacity until GitHub.com services went down.

    Trigger

    A container in the production pods exceeded its memory limit and was terminated; a routine DNS maintenance operation completed earlier had left the clusters unable to reach the image registry.

    Root cause
    Component
    Contributing factor
    Remediation
    • restarted the process with the cached DNS records so the clusters could reach the registry
    • fetched the container images, redeployed and recovered the services
    Action items
    • Improve monitoring so pod restarts cannot fail again in the same pattern
    • Minimise the dependency on the image registry
    • Expand validation during DNS changes
    • Re-evaluate all existing Kubernetes deployment policies
    • Improve progressive deployments so the impact of a deployment can be evaluated incrementally
    Lessons

    A container that production traffic does not need can still take pods out of service, because a pod is available only when all of its containers are healthy. ImagePullPolicy Always turns every container restart into a registry dependency, so an unrelated DNS change that breaks registry access turns a one-minute self-healing event into a long outage, and a mass redeploy spreads it.

Check your cluster for this

These 2 cases involve Model inference server 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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