Incident Library
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Cases2 cases

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  1. Symptoms

    At 1:47am GMT on 28 October 2025, Harness's automated synthetic checks that simulate SDK traffic for the Feature Flags service began failing, and some requests to http://config.ff.harness.io/client/auth received 502 responses.

    Trigger

    During a routine deployment, Kubernetes rescheduled some pods of a Feature Flags dependency onto new nodes that did not have the image cached locally.

    Root cause
    Component
    Contributing factor
    Detection

    Automated synthetic checks that simulate SDK traffic for the Feature Flags service began failing at 1:47am GMT.

    Remediation
    • Restored the missing image to Google Artifact Registry
    • Redeployed the impacted pods
    Action items
    • Review and strengthen the image retention policies in Google Artifact Registry to prevent unintended deletions
    • Validate automatically that all production images are kept for a defined minimum period
    • Add monitoring and alerting for image pull errors
  2. Symptoms

    Alerts fired at 3:13 PM PST on 11 December 2024, and from 3:16 PM every OpenAI service was significantly degraded or completely unavailable, including ChatGPT, the API and Sora. The status page reported API calls returning errors and difficulties logging in to platform.openai.com and ChatGPT. Customer impact peaked at 3:40 PM.

    Trigger

    At 2:23 PM PST a change introducing a new telemetry service, which collects detailed Kubernetes control plane metrics, was merged and the deployment pipeline started. Between 2:51 PM and 3:20 PM the change was applied to all clusters.

    Root cause
    Component
    Contributing factor
    Detection

    OpenAI's detection tools worked: alerts fired at 3:13 PM PST, a few minutes before customer impact began at 3:16 PM, and the root cause was identified at 3:16 PM.

    Remediation
    • Scaled clusters down to reduce the total Kubernetes API load
    • Blocked network access to the Kubernetes admin APIs so no new expensive requests arrived
    • Scaled up the Kubernetes API servers
    • Removed the telemetry service once enough control was back
    • Moved traffic from impacted clusters to healthy ones
    • Intervened manually in clusters where simultaneous resource downloads saturated resource limits
    Action items
    • Put every infrastructure configuration change through a phased rollout with continuous monitoring of both the service workloads and the clusters, including the Kubernetes control plane
    • Run fault injection tests in which the data plane runs without the control plane for longer, and tests that roll out bad changes on purpose to check detection and rollback
    • Build break-glass mechanisms so engineers can reach the Kubernetes API servers in any circumstances
    • Decouple the Kubernetes data plane from the control plane so that service discovery for critical workloads does not depend on the control plane
    • Speed up recovery with better caching and dynamic rate limiters for resources needed at cluster startup, and regular drills that rapidly replace an entire cluster
    Lessons

    Several systems and processes failed at once and interacted in unexpected ways: tests did not catch the change's effect on the Kubernetes control plane, DNS caching put a delay between the change and the failures, and being locked out of the control plane made remediation very slow. Because service discovery used Kubernetes DNS, the control plane was carrying load for product traffic even though the data plane was meant to be independent of it.

Check your cluster for this

These 2 cases involve Client-side DNS resolver and cache 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.

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