Incident Library
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Cases3 cases

1 to 3 of 3

  1. 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.

    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
    Component
    Contributing factor
    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.

  2. Symptoms

    On 25 August 2021, skyscanner.net was unavailable and the Skyscanner mobile apps could not work, for travellers and business partners everywhere at the same time. The full global outage lasted four and a half hours.

    Trigger

    A peer-reviewed change to a rarely changed root-level file of the cells templating configuration, intended as a no-op, that left out the {{ }} delimiters; it was submitted around 15:30 GMT, merged to master and rolled out automatically to every region at once.

    Root cause
    Component
    Contributing factor
    Remediation
    • restored the correct configuration for each cluster and let Argo CD reconcile the cluster back to it
    • restored services through the backup and restoration process
    • brought one region back first with the critical services and served all traffic from it from around 20:30 GMT
    • restored and verified the remaining regions and non-critical services over Thursday and Friday
    Action items
    • Avoid global configuration deploys, including rare change paths like this one
    • Treat templated configuration as code and add testing and linting
    • Plan and wargame more drastic failure scenarios in runbooks
    • Verify backup and restore processes, including access to the backups and the loss of a region
    • Refactor runbooks so they are clear and, where appropriate, idempotent for a stressed engineer in the early morning
    • Reconsider how much configuration is templated across regions, weighing configuration drift against automated global rollout
    Lessons

    Global configuration changes had been prevented in many cases, but not in this rarely used path. Configuration that gains templating and logic has become code and needs tests and at least linting. The failure scenarios and runbooks were not pessimistic enough, a backup only counts once it has been restored, and runbooks need care so they work for a stressed engineer at night. Automation that templates configuration across regions trades drift for the risk of rolling one mistake out everywhere. An experienced incident commander who kept the response calm and blameless made a large difference.

  3. Symptoms

    Terraform, applied from the shared cluster configuration repository after a merge, deleted Spotify's production GKE cluster in Asia. While the team tried to recreate it, Terraform deleted a second production cluster, so two of the three production clusters were gone.

    Trigger

    A feature team's pull request that added three users to an existing RBAC role binding file was merged before the cluster operators' pull request that imported the Asia cluster into Terraform state. The review build of the import pull request had already written the cluster into the shared state file, and the merged configuration had no definition of the Asia cluster, so Terraform removed it.

    Root cause
    Component
    Contributing factor
    Detection

    Internal service discovery could not reach the deleted cluster and the cluster operator team was paged.

    Remediation
    • removed the deleted clusters from the service discovery configuration and restarted service discovery so clients fell back to the instances outside Kubernetes
    • one team that ran half on Kubernetes and half on VMs created more VMs outside Kubernetes to handle the load
    • recreated the clusters, using the hourly backups
    • updated the places where the old clusters' master IPs were hardcoded
    Action items
    • Post the Terraform dry-run output as a comment on each pull request
    • Require a status check that the branch is up to date with master, so the dry run reflects what will actually happen
    • Require an approving review on pull requests
    • Fail reviews automatically when certain keywords such as destroy appear; cluster deletions are done manually outside this repository
    • Add linters and validators for the cluster configuration
    • Schedule and announce disaster recovery tests that cover a cluster going away and smaller failures
    Lessons

    Plan for failure from the start and migrate large infrastructure gradually, so there is redundancy and a way back. A backup whose restore has never been tested is not a backup. Practising disaster recovery paid off: restoring the first deleted cluster took three and a quarter hours, and the team can now restore a larger cluster in one hour. What matters is not who made the mistake but what was learned from it.

Check your cluster for this

These 3 cases involve Argo CD and Configuration templating. 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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