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

Cases2 cases

1 to 2 of 2

  1. Symptoms

    At 10:23 on 6 June 2023 a user of the Cloud Platform reported that all of their production pods had been deleted at the same moment. Several users received Pingdom alerts that their applications were down for a few minutes.

    Trigger

    The team changed the instance type of the cluster's worker nodes through Terraform.

    Root cause
    Component
    Contributing factor
    Detection

    Users reported that their production pods had been deleted at once and that Pingdom alerts showed their applications down; the incident was declared at 11:00.

    Action items
    • Ask AWS, through a support ticket, for the steps to change the node instance type without an outage
    • Plan how to make instance type changes through Terraform without downtime and add a runbook with the steps for changing the node instance type
  2. Symptoms

    At 08:52 PM IST a report came in from WER that many students could not access the JupyterHub and that it was frozen; pages were stuck and nothing was usable. A rise in activity was visible by 09:02 PM, but the usual fixes, such as opening an incognito window and restarting servers, did not help.

    Trigger

    WER students' pods used a good chunk of their memory limit (tests had shown they almost always use just under 1G of their 2G limit, against a 512M guarantee) on nodes shared with the hub's core pods.

    Root cause
    Component
    Contributing factor
    Detection

    WER reported stuck pages and students unable to reach the hub in a GitHub issue (2i2c-org/docs#27).

    Remediation
    • removed memory overcommit for the WER students
    • raised resource guarantees for all core pods, which restarted them and moved some to a new node
    Action items
    • Put user pods in a separate pool so they do not create pressure on the core pods (infrastructure#89)
    • Set limits on the support infrastructure (Prometheus, Grafana, ingress) as well (infrastructure#90)
    • Document and think about overcommit ratios for memory usage (infrastructure#91)
    • Set up better Grafana dashboards to monitor resource usage (infrastructure#92)
    • Document how people can get kubectl access to the cluster so others can look into issues too (infrastructure#87)
    Lessons

    Core pods without resource guarantees get whatever is left on a node, so when user pods on the same nodes fill the CPU and memory, the hub that serves everyone freezes. The memory overcommit ratio has to come from measured use: WER students used just under 1G against a 512M guarantee, which a 4x overcommit could not hold.

Check your cluster for this

These 2 cases involve Node pool and Container memory limit. 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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