Cases3 cases

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1. IL-0095 An engineer deleted the prod namespace of the LEAP JupyterHub while believing the kubeconfig pointed at a new cluster, and the hub was down for about an hour

   2i2c2025

   Symptoms

   At 5:00 PM BST on 12 August 2025 a 2i2c engineer declared an incident. While deploying a new cluster called temple and running into NFS issues, the engineer had deleted the prod namespace; the deletion hung, and listing the pods showed many users from a different cluster. The engineer's first message suggested that the kubeconfig had somehow been mutated or corrupted and that some other cluster was being torn down.

   Resources deletedService outage

   Trigger

   While working around a bug in the NFS deployment process for a new hub, an engineer ran a delete of the prod namespace with the kubeconfig context pointing at the LEAP cluster.

   Root cause

   Operator error

   Component

   Persistent volumes and stateful setsJobs and CronJobsIngress controllerTerraform

   Contributing factor

   No safeguard on destructive changeNo runbook

   Detection

   The engineer who ran the deletion noticed when it hung and the pod list showed users of another cluster, and declared the incident at 5:00 PM BST, before the uptime check on leap.2i2c.cloud failed at 5:32 PM.

   Remediation

   • deleted the single remaining user pod in prod so the namespace deletion completed
   • killed the nfs-share-creator job that was blocking the deployment
   • redeployed the prod hub with the updated NFS server IP address
   • deleted the prod ingress during the deployment so that users could not start servers mid-deployment

   Action items

   • Write an incident report
   • Write a short incident response document for the case "I deleted prod, what next?"
   • Upgrade jupyterhub-home-nfs and remove the nfs-share-creator job
   • Add opt-in hardening against nested kubeconfig contexts to the deployer system
   • Change the engineer's shell configuration to show distinct kubeconfig contexts more clearly

   Lessons

   Provisioning persistent storage outside Kubernetes with Terraform meant a deleted namespace cost an hour of downtime and no data. The engineer had no visible cue for which kubeconfig context was active, which turned a workaround on a new cluster into a deletion on a production one. The engineer knew the fix but did not feel confident redeploying without first confirming with the tech lead, in another time zone, that there was no risk of losing persistent data, and reaching the tech lead took time.

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2. IL-0085 A control plane node of Magic Pages' Kubernetes cluster on Hetzner Cloud ran out of memory, the cluster's own calls then used up the Hetzner API rate limit, and all customer websites were down for about 7.5 hours

   Magic Pages2025

   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.

   Service outageNode NotReadyCloud API rate limit exhausted

   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

   Capacity exhaustion

   Component

   Cloud controller managerPersistent volumes and stateful sets

   Contributing factor

   Unknown limitRetry stormMonitoring blind spotLimited debug accessSingle point of failureNo runbook

   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.

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3. IL-0009 A duplicated generated name made Argo CD replace the InfluxDB Cloud production Application and delete the whole production environment

   InfluxData2022

   Symptoms

   Right after a pull request to the infrastructure-as-code repository was merged, every API endpoint of one InfluxDB Cloud production cluster began returning 404 errors. Writes and reads failed alike: no one could collect data, scheduled tasks failed to run and external queries did not work.

   Service outageResources deletedHigh error rate

   Trigger

   A merged pull request whose rendered YAML added a new Argo CD Application and AppProject that an automation error had named app01-cluster01, the name of the existing production Application, instead of app02-cluster01; reviewers did not notice the app01 and app02 difference.

   Root cause

   Configuration change

   Component

   Argo CDPersistent volumes and stateful setsConfiguration templating

   Contributing factor

   Missed in reviewNo runbookNo safeguard on destructive changeMonitoring blind spotAutomation amplification

   Detection

   Monitoring and alerting did not detect the issue right away, so the team's first response was to try to roll the change back rather than plan a recovery. The write-up does not say how the team found out.

   Remediation

   • stopped the revert of the merged pull request
   • restored the etcd metadata cluster
   • recreated the missing Kubernetes objects so the retained PersistentVolumes became available, then recreated the StatefulSets
   • restored the Zookeeper volumes from an hourly Velero backup
   • rebuilt the Kafka StatefulSet one pod at a time with health and readiness checks disabled
   • accepted writes into the Kafka write-ahead log and scaled up the write path with more replicas and higher memory requests and limits
   • recreated the storage pods, which downloaded data from object storage, reindexed it and replayed the write-ahead log
   • re-enabled Argo CD, queries, dashboards and scheduled tasks, restored replica counts and ran final QA checks

   Action items

   • Write runbooks for restoring state when Kubernetes objects such as PersistentVolumeClaims are deleted but the data on the underlying volumes is preserved
   • Set every volume in every environment to retain data when its PersistentVolumeClaim is deleted
   • Improve the process for handling public-facing incidents
   • Store generated YAML as one object per file, so an overwrite of an existing object shows up as such in review
   • Detect duplicate objects when generating YAML and warn before review, treating objects with the same group, namespace and name as the same even when the apiVersion string differs
   • Add the Argo CD Prune=false annotation to all stateful resources and to Namespace objects managed by Argo CD
   • Set FailOnSharedResource=true on Argo CD Applications so Argo CD fails before changing an object another Application manages or managed

   Lessons

   The biggest gap was that monitoring did not notice the deletion quickly, which pushed the team toward a reflex rollback; in a stateful system a revert can create a new, empty instance instead of bringing the old one back. Retained volumes and clients that retry writes are what kept data loss at zero. Rendering many objects into one file let an overwrite of an existing object look like an addition, so generated configuration needs one object per file and duplicate detection that compares objects the way Kubernetes does. Stateful resources need protection from pruning, and that protection must cover their Namespace too, or deleting the namespace removes everything inside it.

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