Incident Library
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Cases4 cases

1 to 4 of 4

  1. 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.

  2. Symptoms

    From 22:25 to 22:50 UTC every DigitalOcean service in the SFO3 region had network connectivity problems, as traffic between the datacenter and the backbone dropped. Traffic through the router was back by 22:30 and SFO3's control plane systems had reconnected by 22:50, and most services recovered then.

    Trigger

    Most of the line cards on one SFO3 core router rebooted at the same time at 22:21 UTC because of hardware errors on the device.

    Root cause
    Component
    Contributing factor
    –
    Remediation
    • The crashed line cards came back online and traffic on the core router was restored
    • Manually recycled all affected nodes across the SFO3 DOKS fleet
    Action items
    • Work with the router vendor's support team to find the root cause of the line card crash
    • Upgrade software on the core routers in SFO3
    • Raise thresholds for automated remediation so affected nodes are restored without manual action
    Lessons

    A short network fault can leave Kubernetes nodes broken long after the network itself is healthy, when the guest networking on the machines does not recover by itself. Checking node health and the API servers after the network returns, and having automated remediation that is allowed to replace many nodes at once, shortens that tail.

  3. Symptoms

    On 2022-10-31 a planned upgrade of the vpc-cni EKS add-on, started from the deployment pipeline at 13:45 UTC, timed out at 14:10. Only about 10 of the 45 aws-node pods had upgraded, 5 were stuck in Terminating and the other 30 had not been attempted.

    Trigger

    At 07:30 UTC on 2022-10-31 the daily node rollover replaced the existing nodes with nodes built from AMI v20221027, the latest AWS EKS node AMI, released on 2022-10-29 and later recalled because of a kernel issue.

    Root cause
    Component
    Contributing factor
    Detection

    The team noticed nodes flapping between NodeNotReady and NodeReady ("KubeletNotReady PLEG is not healthy") at 14:30 UTC while watching the stalled vpc-cni upgrade, about seven hours after the rollover. Time to detection was 7 hours. Alerting did not catch the NodeNotReady events or the socket growth.

    Remediation
    • cordoned, drained and deleted 15 stuck nodes one at a time to complete the vpc-cni upgrade
    • changed the Terraform infrastructure-modules so a specific AMI could be set
    • rolled the EC2 Auto Scaling group launch configuration back to the previous known good AMI through the pipeline
    Action items
    • Reach out to teams with suggestions about readiness and liveness probes
    • Create a dashboard for readiness and liveness probe coverage
    • Write a wiki article on readiness and liveness probes and their usefulness
    • Modify infrastructure-modules to allow specifying a particular AMI
    • Schedule a discussion on whether to take the latest AMI immediately and how to test new AMIs before they are deployed
    • Implement alerting on abnormal NodeNotReady events and on increased socket use
    • Create a learning path for Kubernetes within the team
    • Collect logs from nodes centrally
    • Create a Prometheus exporter that retrieves the latest available AMI, with a dashboard and alerts
    Lessons

    When a new node AMI has just been rolled out, upgrading other cluster components on the same day makes it hard to tell which change is causing trouble; here the vpc-cni upgrade on the same day confused matters. Taking the latest AMI automatically with no way to pin one meant a rollback first needed a Terraform change. The team also concluded that it needs better communication paths with application teams during incidents.

  4. Symptoms

    For about six months, applications on Omio's Google Kubernetes Engine clusters stalled at random: they got stuck, stopped answering health check requests or lost network connections, and nothing the team looked at explained why.

    Trigger

    Multi-threaded workloads running with CPU limits on nodes whose kernel did not yet have the fix for a known CFS quota bug that causes unnecessary throttling.

    Root cause
    Component
    Contributing factor
    Detection

    Months of random stalls and failed health checks led the team to read the cgroup cpu.stat throttling counters inside the pods, which showed far more throttling than the limits should allow.

    Remediation
    • removed CPU limits until the kernel with the CFS bug fix was deployed across all clusters
    Action items
    • Deploy the kernel with the CFS quota bug fix across all clusters
    • Monitor utilisation, saturation and error metrics, API latency and error rates to confirm the change behaves as expected
    Lessons

    A CPU limit is a time quota per 100 ms period, not a share of cores, so a multi-threaded container can be stopped for most of each period long before it uses the cores it was given, and a kernel bug can make that worse. Throttling is invisible in standard Kubernetes metrics and has to be read from the cgroup counters. Removing limits trades away protection against a container that takes a whole node, which is only acceptable when runtimes are tuned.

Check your cluster for this

These 4 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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