Incident Library
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Cases2 cases

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  1. Symptoms

    At 20:45 UTC on 28 May 2025, most of Twingate's API traffic started going to a single backend service, which was overwhelmed and returned elevated error rates, so API calls failed widely across core services.

    Trigger

    A rollout that updated the routing rules of Twingate's global load balancer.

    Root cause
    Component
    Contributing factor
    Detection

    The incident was detected at 20:48 UTC, three minutes after API traffic was first misrouted, and triage began.

    Remediation
    • Started a rollback of the routing change at 20:52 UTC; the automated rollback stalled on infrastructure dependencies
    • Applied a manual fix with local tooling at 21:09 UTC, which restored service by 21:14 UTC
    Action items
    • Correct the routing rule
    • Add alerts that detect backend traffic anomalies in lower environments
    • Improve resource scaling thresholds
    • Decouple infrastructure tooling from service dependencies so recovery has faster options
    • Introduce backend sharding to isolate customer workloads and reduce the impact of incidents
  2. Symptoms

    On 21 March 2025, between 21:10 and 21:41 UTC, part of the authentication and authorization requests to Twingate services failed with elevated error rates.

    Trigger

    Twingate saw anomalies in one of its GKE clusters and scaled down the deployments in that cluster to protect the rest of the system, which moved its traffic to the other clusters.

    Root cause
    Component
    Contributing factor
    Detection

    Twingate observed anomalies in one of its GKE clusters at the start of the incident.

    Remediation
    • Reintroduced capacity in the cluster that had been scaled down, so traffic was rebalanced across regions
    Action items
    • Keep working with the cloud provider to find the cause of the unexpected timeouts and of the cluster update's impact
    • Run deployments in all regions with the same number of replicas in the HPA configuration
    • Raise the maximum node counts of the cluster node autoscalers to leave more room to scale up
    • Configure the HPA to scale up faster
Check your cluster for this

These 2 cases involve Cloud load balancer and Cluster autoscaler. 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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