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

Cases2 cases

1 to 2 of 2

  1. 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.

  2. Symptoms

    Services on Buffer's kops-managed cluster on AWS (about 60 nodes and 1500 containers) answered more slowly than they should have. The container throttling metric showed that many containers were being throttled whatever their CPU usage was, including containers that never came close to their CPU limit.

    Trigger

    CPU limits on user-facing services, enforced through CFS quota by a Linux kernel with a bug that throttled containers even when they were well under their limit.

    Root cause
    Component
    Contributing factor
    Detection

    While reviewing the container throttling metric, the team noticed many containers being throttled even though their CPU usage stayed far below their limits.

    Remediation
    • removed CPU limits from all services directly or indirectly on the users' critical path
    • moved those services to dedicated tainted nodes and gave them matching tolerations
    • set each service's CPU request to its peak usage over a few months plus a margin of more than 20%, and set memory requests the same way
    Action items
    • Move nodes to a kernel that contains the CFS throttling fix (4.19 or later), which for Buffer means a kops version whose host image is Ubuntu 20.04
    • After the kernel upgrade, check whether throttling is fully gone and update the post
    Lessons

    A CPU limit can slow a service down even when the service uses a small part of it, because of how the kernel enforces CFS quota on older kernels; the throttling metric shows it, the CPU usage graph does not. Upgrading the kernel is the safer fix. Removing limits works for latency, but only together with honest CPU and memory requests, isolation of the unbounded services, and monitoring of node usage, since unbounded containers can starve the kubelet.

Check your cluster for this

These 2 cases involve Linux kernel and Node operating system image. 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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