Incident Library
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Cases2 cases

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  1. Symptoms

    A user of the LIS JupyterHub reported a "backoff after failed scale-up" error, and user servers would not start. An engineer opened incident #36, "LIS hub cannot scale", at 5:40 AM Pacific on 14 November 2022.

    Trigger

    At 10 AM UK time on 14 November 2022, the B zone of Google Cloud's europe-west2 region (London) ran out of resources to allocate. The LIS cluster's nodes were restricted to that zone.

    Root cause
    Component
    Contributing factor
    Detection

    A user reported the "backoff after failed scale-up" error; an engineer opened the incident at 5:40 AM Pacific, about three hours and forty minutes after the zone ran out of resources.

    Remediation
    • edited the node pool in the GCP console to allow node creation in the A and C zones of europe-west2, which were not exhausted
    Action items
    • Either restrict the node pool back to the B zone after the Black Friday event, or move the cluster and the NFS Filestore to the A zone (infrastructure issue #1944)
    • Investigate an enterprise plan that allows a regional NFS, at higher cost (infrastructure issue #1945)
    • Make sure PagerDuty notifications always get through to engineers (team-compass issue #574)
    Lessons

    A node pool pinned to one zone for storage performance cannot scale when the cloud provider runs out of capacity in that zone. The autoscaler's "backoff after failed scale-up" message usually points to quotas, and raising quotas does nothing here; the ZONE_RESOURCE_POOL_EXHAUSTED error in the logs is what identifies a zone without capacity. The review found that one engineer wearing many hats (incident commander, comms and debugging), with pages not reaching the other responders, made the response harder; a team response would have given more support.

  2. Symptoms

    While running the CI process on a local machine to test an application Helm chart, the SRE lead watched the pods with watch kubectl get pods. All pods reached Running, and then some went back to Pending. Two hours later the problem had grown and some pods showed the phase Unknown.

    Trigger

    A CI run installed the application's Helm charts for all four markets (Efritin.com, Tonaton.com, Ikman.lk and Bikroy.com) on the nonproduction cluster at the same time.

    Root cause
    Component
    Contributing factor
    Detection

    The SRE lead saw pods go from Running back to Pending while watching kubectl get pods during the CI run; no monitoring raised an alert, even when all three workers were not ready.

    Remediation
    • terminated the three worker nodes and let the auto scaling group replace them
    Action items
    • Fix the Datadog integration for the nonproduction AWS account and repeat the installs with telemetry
    • Increase the worker root EBS volumes from 20GB to 120GB
    • Keep common base images pulled on every node with a DaemonSet until kops supports user hooks
    • Configure different allocation limits for the different layers in Kubernetes
    • Discuss a highly available Tiller with the Helm team
    • Find a way to throttle the number of newly created pods
    Lessons

    An ordinary deploy of the application could collapse the cluster, because the default 20GB gp2 root volumes could not keep up with parallel image pulls and container starts. Without node metrics and pod phase metrics, three NotReady workers raised no alert. A single Tiller replica can block the deploy that would fix an outage.

Check your cluster for this

These 2 cases involve Cluster autoscaler and Container runtime. 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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