Incident Library
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Cases2 cases

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  1. Symptoms

    Nodes that already existed kept running, but every new node, spot or standard, on either v1.32 or v1.33, failed to initialise and could not join the cluster. Spot nodes that were preempted and recreated never came back, so services degraded progressively: background workers on spot instances, reporting services and order allocation stopped completely, and the APIs on existing nodes kept answering but could not scale for load spikes.

    Trigger

    Google upgraded the GKE control plane from v1.32.x to v1.33.2 at the version deprecation deadline, although auto-upgrade was disabled on the node pools.

    Root cause
    Component
    Contributing factor
    Remediation
    • Created a new GKE cluster at v1.33, the version of the forced upgrade
    • Upgraded Istio to 1.27.1 for Kubernetes 1.33 compatibility
    • Updated the cert-manager configuration for Kubernetes 1.33
    • Migrated all production workloads to the new cluster and verified pod scheduling, DNS resolution, PVC mounting and external integrations
    • Decommissioned the broken cluster
    Action items
    • Configure all clusters to use the GKE stable release channel
    • Implement a separate management cluster for Argo CD, monitoring and other control components (Q4 2025)
    • Review spot instance usage for critical workloads and add on-demand fallback pools
    • Document the SRE upgrade procedure: management cluster first, production after a monitoring period of several days
    • Document a Kubernetes, Istio and cert-manager compatibility matrix
    • Create an automated upgrade testing pipeline
    • Reserve static IPs for quick migrations
    • Pin versions of Istio, cert-manager, CSI and DNS addons
    • Stay current with stable releases and regularly check GKE alerts and announcements to avoid deprecation deadlines
    Lessons

    GKE upgrades the control plane at a version's deprecation deadline even when auto-upgrade is disabled, so staying inside the supported version window is how to avoid a forced upgrade. A control plane upgrade can leave existing nodes healthy while every new node fails, and spot nodes that cannot rejoin after preemption turn this into cascading failures. Relying heavily on spot instances for critical workers turned a node join failure into a progressive outage. Kubernetes upgrades have to be planned together with dependent components such as Istio and cert-manager, and without a standby cluster on the same provider there was no immediate failover; the only recovery left was building a new cluster.

  2. 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.

Check your cluster for this

These 2 cases involve Node pool 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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