Incident Library
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  • Root cause
  • Component
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  • Contributing factor
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Cases2 cases

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  1. 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.

  2. Symptoms

    On 30 November 2017 at 16:59 PST, mybinder.org operators noticed that Grafana looked wrong and that launching a new server failed with an error that a temporary user could not be created. Pods that were already running kept running, but no new ones were being created.

    Trigger

    A build container, which ran without a memory limit, most likely used up all the memory on the node that ran the proxy pod.

    Root cause
    Component
    Contributing factor
    Detection

    An operator happened to look at Grafana at 16:59 PST, saw that it looked wrong, and confirmed the error by launching a new server.

    Remediation
    • Cordoned the node that had run out of memory
    • Uncordoned the node once the proxy was working
    Action items
    • Limit the memory available to build pods
    • Change the JupyterHub configuration so user pods are not cleaned up when the jupyterhub pod restarts
    • Nominate one person to drive the response, possibly in a shared terminal, so only one person changes the cluster
    • Alert when something is wrong with the cluster, for example by counting 4xx responses on the jupyterhub pod
Check your cluster for this

These 2 cases involve Container memory limit and Ingress controller. 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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