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

    Services on the production Amazon EKS cluster behind the idus marketplace threw intermittent errors whose cause was hard to pin down. Calls to external services such as RDS and Redis failed now and then, and whenever DNS traffic spiked, domain lookups answered slowly or failed outright.

    Trigger

    All CoreDNS pods were scheduled onto the same node, and that node's memory usage spiked until it ran out of memory.

    Root cause
    Component
    Contributing factor
    Detection

    The write-up does not say how the full outage was noticed. The earlier DNS trouble showed up as intermittent service errors, and investigating failed calls to RDS and Redis led to DNS resolution failures.

    Remediation
    • deployed NodeLocal DNSCache as a DaemonSet in front of CoreDNS
    • added a PodDisruptionBudget (maxUnavailable 1) and topologySpreadConstraints across zones for CoreDNS
    • lowered ndots from 5 to 2 with dnsConfig in the Deployments
    Lessons

    The CoreDNS setup that EKS creates by default (two pods) can be enough for a small or development cluster but becomes a bottleneck in a large production cluster, and DNS is a single point of failure that needs high availability and room to scale. Tuning the CoreDNS cache and buffer size only relieved the symptoms; the real load came from the extra queries that ndots:5 generates for external names. A CoreDNS pod without a memory limit can share a node's fate with everything else on it.

Check your cluster for this

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