Incident Library
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Cases2 cases

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  1. Symptoms

    At around 09:33 UTC one of the three control plane nodes of the Kubernetes cluster that hosts Magic Pages went offline. Looking at the node showed that it had run out of memory.

    Trigger

    One of the three control plane nodes ran out of memory and went offline at about 09:33 UTC; the attempts to replace it with hetzner-k3s failed while the cluster's own components were exhausting the Hetzner API rate limit.

    Root cause
    Component
    Contributing factor
    Detection

    The founder found the control plane node offline at about 09:33 UTC. Monitoring alerts about unreachable customer websites started around the time the replacement attempts failed, and about half an hour of investigation identified the exhausted Hetzner API rate limit.

    Remediation
    • powered off all servers of the Kubernetes cluster to stop the API requests so the Hetzner rate limit could recover
    • waited until ratelimit-remaining had built up a buffer before starting recovery
    • restarted the control plane servers
    • started the essential Longhorn components by hand so Longhorn could heal and make storage volumes available
    • restarted the remaining websites by hand
    Action items
    • Monitor the Hetzner API call rate coming from the Kubernetes cluster nodes themselves to get early warning of high usage
    • Get a higher Hetzner API rate limit; Hetzner support was contacted
    • Investigate how to reduce the cluster's reliance on a single API, including alternatives to hetzner-k3s
    • Document and test a procedure for recovering Longhorn after a full cluster shutdown
    Lessons

    The running cluster, not only the deployment tooling, consumed the cloud provider's API quota, and in a failure it consumed it fast enough that the quota never recovered. The default limit of 3600 requests per hour was not enough for failure and recovery scenarios, and API usage from inside the cluster was not monitored. A tool that hides the provider's API errors slows diagnosis. After a full, non-graceful shutdown, Longhorn needed manual intervention to come back, which lengthened the outage.

  2. Symptoms

    Users reported that their jobs were not starting, with some sign that image pulls were involved. Many pods across the cluster were in error states related to image pulls, while applications that were already running were not affected.

    Trigger

    A change to the permissions on a storage bucket that one application needed to read. The application could no longer start, and its DaemonSet, running on roughly a thousand nodes of the cluster, went into a crash loop with imagePullPolicy: Always, so every restart pulled the image again although it was already on the node.

    Root cause
    Component
    Contributing factor
    Detection

    Users reported that their jobs were not starting; monitoring had not caught it first.

    Remediation
    • replaced the affected NAT instances so registry traffic came from new source IPs
    • reconfigured the static IPs and firewall rules so applications could reach the Cloud SQL database again
    Action items
    • Add an admission webhook that rejects floating image tags, starting with latest
    • Apply the image tag webhook to workload objects only, not to pods, after it blocked pods created by in-cluster controllers
    Lessons

    The speakers' takeaway is to be very careful with DaemonSets in large clusters: they say they had broken everything DaemonSets depend on, including DNS, Vault and the image repository, and that it is very easy to break things when managing them by the thousand. imagePullPolicy: Always made every crash-loop restart pull the image again. Sending all registry traffic through three NAT instances put the whole region behind three source IPs, which the registry rate-limited, and changing those IPs cut the connections to a critical database reached over the internet through them. An admission webhook on pods rejects the pods that controllers in the cluster create, where no user sees the error.

Check your cluster for this

These 2 cases involve Admission webhook and Cloud controller manager. 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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