Incident Library
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Cases2 cases

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  1. Symptoms

    Every rolling update of the team's microservices on Amazon EKS, behind an Application Load Balancer, was followed by intermittent 502 and 503 errors for users. A Slack message at 3 a.m. read "배포는 성공했는데, 서비스는 죽었다" (the deployment succeeded, but the service died). Every pod was Ready and Datadog was green while users saw errors.

    Trigger

    A rolling update of a Deployment served through an ALB that registers pod IPs as targets, with no pod readiness gates.

    Root cause
    Component
    Contributing factor
    Detection

    A Slack message at 3 a.m. reported that the deployment had succeeded but the service was down; Datadog dashboards were green at the time.

    Remediation
    • labelled the namespace with elbv2.k8s.aws/pod-readiness-gate-inject=enabled so the AWS Load Balancer Controller injects pod readiness gates and Kubernetes waits for the ALB target to be healthy before deleting old pods
    Lessons

    A pod being Ready in Kubernetes does not mean the load balancer will send it traffic. Without a readiness gate, a rolling update can delete the old pods while the ALB still considers the new targets unhealthy, and dashboards that track pod status stay green during the errors. Making pods wait longer on shutdown reduced the errors but did not fix the start-up gap and made every deployment much slower; the readiness gate ties the pod's Ready state to the ALB's view of the target.

  2. Symptoms

    At 09:36 on 18 March 2025, during the upgrade of the live cluster to EKS 1.30, users reported that their pods were being terminated and were not restarting automatically, in both production and non-production environments. Automated infrastructure alerts fired as well, and production services had outages.

    Trigger

    Upgrading the live EKS cluster from 1.29 to 1.30, during which the descheduler attempted to reschedule all pods on the cordoned nodes simultaneously.

    Root cause
    Component
    Contributing factor
    Detection

    Users reported at 09:36 that pods were being terminated and not restarting, and automated infrastructure alerts fired.

    Remediation
    • Removed the cordon annotation so pods could be rescheduled
    • Deleted stuck 1.30 nodes to force their recreation
    • Added a new node group of 20 nodes running 1.30 by hand
    • Cordoned and drained all old 1.29 nodes onto the new 1.30 nodes
    Action items
    • Raise an AWS Production System Down support case (Case ID: 174229643100496)
    • Get guidance from AWS support on querying control-plane logs to find the root cause of similar issues
    • Document a runbook for emergency scaling of a new node group in case of unexpected capacity shortages
    • Alert on an imbalance between the autoscaling group and the Kubernetes nodes
    • Review the descheduler configuration to understand why it throws pods off cordoned nodes, and fix it where possible
    • Alert on spikes in the pending pod count
    Lessons

    The write-up lists review actions rather than lessons. They show the team had no documented way to add an emergency node group, no alert on a mismatch between the autoscaling group and the nodes Kubernetes sees, no alert on a spike in pending pods, and no clear understanding of how the descheduler treats cordoned nodes.

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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