Incident Library
  • Root cause
  • Component
  • Symptom
  • Contributing factor
  • Case

Cases2 cases

1 to 2 of 2

  1. Symptoms

    At 13:42 the RootVolUtilisation-Critical alert fired in the high-priority alerts channel. At 14:03 a user reported image pull errors with the message "no space left on device".

    Trigger

    Upgrading the EKS Terraform module to version 18, which changed the node launch template, and draining workloads onto the new nodes it created.

    Root cause
    Component
    Contributing factor
    Detection

    The RootVolUtilisation-Critical alert fired in the high-priority alerts channel at 13:42, and a user reported "no space left on device" image pull errors at 14:03.

    Remediation
    • Uncordoned the old default node group and drained the new nodes to move workloads back
    • Identified the launch template change that cut the root volume to 20 GB and applied a fix on the manager and live clusters
    • Drained the old node group onto the fixed nodes
    Action items
    • Update the runbook to compare launch template changes during an EKS module upgrade
    • Create a test setup that pulls images of different sizes, like live
    • Update the RootVolUtilisation alert runbook to check the disk space configuration
    • Scale CoreDNS dynamically based on the number of nodes
    • Investigate whether IPv6 can solve the IP prefix starvation problem
    • Add drift testing to identify when a terraform plan shows a change to the launch template
    • Set up logging for the CNI and ipamd logs and alerts on errors related to IP prefix starvation
    Lessons

    The write-up lists review actions rather than lessons. They aim at two gaps: the launch template changes brought by a module upgrade were not compared or drift-tested before rollout, and there were no CNI or ipamd logs or alerts to show the IP prefix starvation the team suspected during the drain.

  2. Symptoms

    The NU.nl development team got reports that several services on one of its Kubernetes clusters behaved erratically: occasional error pages, slow responses and timeouts. The affected services were mostly internal tools and dashboards.

    Trigger

    A new ElastAlert rule, deployed shortly before from the command line, that fired if the Cloudfront-* Elasticsearch indexes stopped receiving documents; those indexes take a few million requests per hour, and the rule made ElastAlert's memory grow without bound.

    Root cause
    Component
    Contributing factor
    Detection

    Users reported error pages, slow responses and timeouts from several services; no alert fired.

    Remediation
    • terminated the NotReady nodes one by one so the auto-scaling group replaced them (the new nodes also went NotReady, but Prometheus and Grafana landed on a stable node)
    • found the ElastAlert pods' memory growth on the Grafana pod memory dashboard
    • fixed the ElastAlert problem that caused the memory growth
    Action items
    • Define alerts on resource usage such as CPU, memory and disk space
    • Keep researching an alerting strategy that combines Elasticsearch, Prometheus Alertmanager and New Relic
    • Consider exporting metrics out of the cluster and running Grafana outside it, on EC2 or a separate Kubernetes cluster
    • Either visualise the important metrics in the ELK stack or make Prometheus and Grafana more available, and improve the metrics strategy
    • Set resource limits for ElastAlert through the Helm values
    • Configure namespaces with default memory requests and limits
    • Isolate customer-facing workloads from build, logging and monitoring workloads (a dedicated node group, or later a separate tools cluster)
    • Deploy everything through automation such as Jenkins pipelines, and consider a GitOps approach
    • Deploy through a pipeline that includes a smoke test step
    • Make Kubernetes and cloud infrastructure work part of the team's sprints, picked up by all team members while pairing with more experienced members, and hold workshops that go deep into specific topics
    Lessons

    Fixing the root cause covers only a small part of what an incident can teach; the chain of smaller failures around it is where most of the learning is. A pod without a memory limit can take whole nodes down, so limits need defaults at the namespace level. Monitoring that runs inside the cluster it watches goes blind with it. Changes installed by hand from the command line leave the rest of the team without a record of what changed.

Check your cluster for this

These 2 cases involve CoreDNS and kubelet. 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.

By root cause1