Incident Library
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Cases2 cases

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  1. Symptoms

    On a Sunday, a team using one of Skyscanner's Kubernetes clusters manually paged the out-of-hours Kubernetes support because their service showed elevated latency, worst at the p90 and maximum levels.

    Trigger

    Upgrading the HAProxy ingress controller to v0.6 and going back from Service virtual IPs to individual pod IPs as backends, on the understanding that the sort-backends flag introduced in v0.5, false by default, meant backends were randomly shuffled.

    Root cause
    Component
    Contributing factor
    Detection

    A cluster user manually paged the out-of-hours Kubernetes support about elevated latency; Prometheus queries then showed the uneven request distribution across pods.

    Remediation
    • switched the affected service from pod IPs back to the Service virtual IP through a config change in the internal deployment tool Slingshot
    • patched the controller to keep each backend's servers in an insertion-ordered slice and changed the template to iterate over that slice
    • tested the patch in sandbox clusters and rolled it out to the production clusters
    Action items
    • Run the internal controller patch in production and return to pod IPs as the default backends
    • Raise the problem upstream with a GitHub issue and pull request
    • Test changes to cluster components against the specific behaviour and conditions that matter
    Lessons

    The team already knew that sorted backends cause hot-podding and still did not test the new flag well enough to see that it did not work; changes to cluster components need tests aimed at the exact behaviour that matters, under conditions like production. A keyword can behave differently in different parts of a language: range over a map is unordered in Go code but sorted in text/template. A small cluster component can do outsized harm to every user of the cluster and, in the end, to customers.

  2. Symptoms

    On the evening of a long-overdue upgrade of a loveholidays production GKE cluster from 1.10.X to 1.12.5-gke.10, planned to take 2 to 2.5 hours, the master upgrade and the first two 7-node NodePools went smoothly. After that the NodePool upgrades slowed to a crawl. Each node is cordoned, drained and upgraded in turn. Even a 14-node cluster at about 5 minutes per node took over an hour, and with about 100 more nodes to go, some of them taking over 15 minutes to drain, the sequential drains were too slow to finish at a reasonable hour.

    Trigger

    A manual upgrade of the GKE masters and NodePools from 1.10.X to 1.12.5-gke.10, two versions at once, on 19 March 2019; GKE's release notes of 14 March had already said that version was no longer available for new clusters or master upgrades because masters showed elevated error rates, but it could still be selected.

    Root cause
    Component
    Contributing factor
    Detection

    The slow drains, zombie nodes and stuck NodePool were seen by the engineer running the upgrade; the broken Ingress was noticed after the upgrade and explained by Google support; the HPA problem was noticed the next day.

    Remediation
    • drained the remaining NodePool nodes ahead of the upgrade with kubectl drain --ignore-daemonsets --delete-local-data
    • deleted the zombie instances from the managed instance group with gcloud compute instance-groups managed delete-instances
    • cancelled the stuck UPGRADE_NODES operation with gcloud beta container operations cancel
    • used kubectl port-forward in place of the broken internal Ingresses
    • scaled down the older of two active ReplicaSets, or triggered a dummy deployment, to bring the HPA back
    Action items
    • Alert when a HorizontalPodAutoscaler is at 70% and at 100% of its capacity
    • Alert when a deployment has had two active ReplicaSets for 30 minutes and for 60 minutes
    • Re-evaluate termination grace periods that were set to arbitrary large values
    • Read the GKE release notes before every cluster upgrade
    • Use auto-upgrade in non-production environments, and manual cluster upgrades with automatic node upgrades in production
    • Keep environments as identical as possible, including resource names
    Lessons

    Read the release notes before upgrading: the target version had been withdrawn five days earlier. Drain time depends on pod configuration, so emptyDir volumes, long grace periods and missing disruption budgets turn a NodePool upgrade into hours. Pre-production only catches problems like the ingress bug if it matches production down to resource names. Auto-upgrades in non-production surface problems early, and a mix of manual cluster upgrades and automatic node upgrades in production keeps clusters current without much effort.

Check your cluster for this

These 2 cases involve Ingress controller 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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