Incident Library
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Cases4 cases

1 to 4 of 4

  1. Symptoms

    In January 2022 an etcd memory spike overloaded the Kubernetes control plane of one of Render's clusters in Frankfurt. At first only operations that need the control plane failed: new deploys and cron job runs.

    Trigger

    A memory spike in etcd overloaded the cluster's control plane.

    Root cause
    Component
    Contributing factor
    Action items
    • Run CoreDNS on data plane nodes instead of control plane nodes
    • Run etcd on dedicated nodes
    Lessons

    CoreDNS can keep answering from its in-memory cache while the API server is down, but it cannot initialise without it. With CoreDNS on the control plane nodes, a reboot or restart during a control plane outage turned a failure of deploys and cron jobs into a failure of requests to services that were already running. The core problem was that the data plane was not isolated enough from the control plane: even without the reboot, the etcd memory spike alone could have forced CoreDNS to restart.

  2. Symptoms

    Dex, the OIDC proxy that handled Kubernetes authentication, stored its data in the Kubernetes API (storage type kubernetes, inCluster: true) and had built up a huge number of AuthRequest objects. Listing all AuthRequests timed out, and without the list they could not be deleted.

    Trigger

    The dex namespace was deleted so that Kubernetes would clean up the accumulated AuthRequest objects itself.

    Root cause
    Component
    Contributing factor
    Detection

    The speaker says he had got hints of the problem earlier. While the masters were down, plenty of alerts arrived in Slack, but monitoring could not be reached because authentication and port-forwarding no longer worked.

    Remediation
    • removed the kubernetes finalizer from the dex namespace by calling the API directly (kill-kube-ns)
    • deployed Dex to a new namespace with sane defaults
    Lessons

    Dex with Kubernetes as its storage keeps every AuthRequest as an API object, and with a long TTL they pile up until the API cannot list them. Deleting a namespace that holds that many objects makes Kubernetes list them in a loop and can overload the masters. Monitoring that is reached through cluster authentication is lost when authentication is what broke.

  3. Symptoms

    The daily NRE Labs statistics showed lesson activity collapsing, and kubectl could not reach the cluster: `The connection to the server 1.2.3.4:6443 was refused - did you specify the right host or port?`

    Trigger

    Unknown. The liveness probe that kubeadm writes into the etcd static pod manifest (an `etcdctl get foo` against 127.0.0.1:2379, failureThreshold 8, timeoutSeconds 15) started failing. The author first suspected GCP maintenance the night before, but could not reproduce the probe failure by hand and does not know why it began.

    Root cause
    Component
    Contributing factor
    Detection

    The author saw lesson activity drop in the daily NRE Labs statistics during his morning routine.

    Remediation
    • removed the liveness probe section from the kubeadm-generated etcd static pod manifest and applied it between etcd restarts
    Action items
    • Add monitoring, especially for the single-node master
    • Run a basic "down for maintenance" page in Google Cloud Functions that points to Twitter for updates, to redirect to during outages
    Lessons

    Because all of the infrastructure is managed as code, the cluster could be rebuilt quickly, but destroying it before finding the cause also destroyed the evidence. Monitoring matters, especially with a single-node master. Read the restart counts in kubectl output, not just the Running status. Remember the fundamentals instead of expecting the issue to be more complicated than it is, and do not let the urgency of an outage push you into hasty decisions. Put up a maintenance page instead of leaving the site failing halfway.

  4. Symptoms

    At 14:10 an engineer deployed a change to a service that Monzo's current account needs to process payments, and every request to that service started to fail. Current account customers saw payment failures; the prepaid card was not affected at first because it does not use that service. The change was rolled back at 14:12, but the errors continued.

    Trigger

    At 14:10 an engineer deployed a change to a service needed to process current account payments. Two weeks earlier the etcd cluster had been upgraded and grown from three to nine nodes, and one day earlier another team had scaled a new service down to zero replicas while leaving its Kubernetes service in place.

    Root cause
    Component
    Contributing factor
    Detection

    Payment failures began right after the 14:10 deploy. The team rolled back at 14:12 and declared an outage internally at 14:16. At 15:13, when no service could serve requests, practically all automated alerts fired.

    Remediation
    • Rolled back the payments service change (the errors continued)
    • Activated internal backup processes so most customers could still use their card
    • Restarted all linkerd instances (this made the outage worse)
    • Restarted the three Kubernetes API server processes so replacement linkerd instances could start
    • Began deploying a newer linkerd version with the fix for empty services
    • Deleted the Kubernetes service that had no endpoints, which let linkerd load service discovery again
    Action items
    • Fix the bug in Kubernetes that can trigger timeouts after a cluster reconfiguration
    • Roll out a new version of linkerd that fixes the parsing error
    • Create better health checks, dashboards and alerts for the affected components so they give clearer signals about what is wrong and prevent human error
    • Improve procedures to communicate outages internally and externally as clearly and quickly as possible
    Lessons

    A large failure in a distributed system can be very hard to understand, and well-meant human action can compound it, as restarting every linkerd instance did here by exposing a second incompatibility. Because Monzo deploys often and its network changes constantly, the deployment and request-routing systems have to be trustworthy. Monzo committed to publishing a similar post-mortem for any incident of this severity.

Check your cluster for this

These 4 cases involve etcd and Kubernetes API server. 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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