Incident Library
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Cases2 cases

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  1. Symptoms

    Customers of DigitalOcean Managed Kubernetes (DOKS) saw worker nodes go NotReady between 06:00 and 07:00 UTC and stay unavailable, taking down the services on them, until the nodes were replaced or rebooted; some nodes needed several replacements or reboots. The first sporadic reports on October 8 said replacing the node fixed it. The same thing happened again around 06:00 UTC on October 9, more widely and across many regions, and again on October 10. Only clusters on versions 1.27.6-do.0 and 1.28.2-do.0 were affected.

    Trigger

    A systemd timer in the DigitalOcean Debian image started apt upgrade daily at 06:00 UTC, with a random delay of up to 60 minutes.

    Root cause
    Component
    Contributing factor
    Detection

    Sporadic customer reports of failed worker nodes arrived at 07:43 UTC on October 8; internal incident response started at 07:10 UTC on October 9 after broader impact across regions.

    Remediation
    • Advised customers to replace or reboot affected worker nodes
    • Arranged SSH and console access to worker nodes while they were healthy, and live-debugged an affected node on October 10
    • Released a DaemonSet to all affected clusters that disables the apt upgrade systemd timer on every worker node
    • Released new node images without the auto-update process
    Action items
    • Disable the timer during node provisioning, then remove the DaemonSet from all clusters
    • Add a check to the internal conformance test suite for new cluster versions that the auto-upgrade process and other unwanted timers stay disabled
    • Support logging into worker nodes even when normal access paths are broken
    Lessons

    An operating system auto-update timer left enabled in a node image can change the kernel and systemd underneath Kubernetes, and a timeout that kills the upgrade half-way can leave a node without networking. Failures that start at the same time every day point to a scheduled job on the nodes, and systemd timers belong on that list next to cron jobs. When the failure takes out a node's network, an investigation that depends on logging into the node stalls; access paths that work while the node is broken shorten diagnosis.

  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 kubelet and Linux kernel. 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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