Cases4 cases

1 to 4 of 4

1. IL-0192 A Crossplane composition merge-policy change for a TLS policy update switched Kubernetes Services from LoadBalancer to ClusterIP and deleted the load balancers behind about 25% of Grafana Cloud services

   Grafana Labs2025

   Symptoms

   At 08:41 UTC on 18 February 2025, several Grafana Labs application teams noticed that some of their endpoints were no longer available. The investigation quickly found that the load balancers for those endpoints were gone.

   Service outageResources deleted

   Trigger

   A change to the merge policy of a Crossplane composition, made so that the default TLS policy of the load balancers could be overridden, was applied to development, staging and production at the same time.

   Root cause

   Configuration change

   Component

   Configuration templatingCloud load balancer

   Contributing factor

   Unverified assumptionNo staged rolloutExcessive blast radiusNo safeguard on destructive change

   Detection

   Application teams noticed at 08:41 UTC that some of their endpoints were no longer available.

   Remediation

   • rolled back the Crossplane composition change
   • recreated the affected Kubernetes Services so that Kubernetes recreated their load balancers

   Action items

   • Add a CI check that blocks a deployment to more than one wave, so any exception to the dev, staging, canary, production order is caught
   • Give higher priority to a more sophisticated wave design that was already under discussion
   • Enable deletion protection on the load balancers, one layer below Crossplane
   • Enable deletion protection on other critical cloud provider resources
   • Develop the Crossplane Managed Resources exporter for change validation and visibility, now in beta testing within Platform engineering; simulated against the change that caused this outage, it shows a clear diff
   • Complete and test support for reconfiguring the TLS policy so application teams can deploy it

   Lessons

   A change that looked small and low-risk had a side effect that only a full end-to-end test through Crossplane and the cloud provider's load balancer controller would have shown; seeing the intended override work was not that test. Staged rollout and blast-radius limits that exist only as process can be skipped, so Grafana Labs moved to enforce waves in CI and to add deletion protection on the load balancers themselves, beneath the Crossplane layer.

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2. IL-0152 An incorrect configuration applied during maintenance made an internal infrastructure cluster automatically delete the control planes of 200 DOKS clusters in FRA1

   DigitalOcean2024

   Symptoms

   At 13:02 UTC on 2024-01-23 DigitalOcean posted that, as of 12:18 UTC, its engineers were investigating an issue with Kubernetes clusters in the FRA1 region and that users might see errors when communicating with their clusters. The control planes of the affected DOKS (DigitalOcean Kubernetes) clusters had become inaccessible.

   Resources deletedControl plane unresponsive

   Trigger

   An incorrect configuration was applied to an internal infrastructure cluster in FRA1 at 12:21 UTC during a maintenance operation.

   Root cause

   Configuration change

   Component

   etcdKubernetes API server

   Contributing factor

   Automation amplificationNo safeguard on destructive changeExcessive blast radius

   Detection

   Engineers were investigating errors with Kubernetes clusters in FRA1 as of 12:18 UTC and identified the incorrect configuration as the cause at 12:36.

   Remediation

   • restored each affected cluster's etcd from its latest twice-daily backup
   • restored the stateless control plane components after etcd
   • patched the recovery mechanism after unexpected problems during recovery
   • found and fixed an additional small bug during recovery
   • contacted affected customers directly with further instructions, since some might need to reconfigure Kubernetes resources

   Action items

   • Prevent automatic deletion of infrastructure cluster resources under any circumstances in the production environment

   Lessons

   When managed control planes run as resources in another cluster, one configuration mistake in that cluster can delete hundreds of control planes at once through ordinary automation. The etcd backups limited the damage, but their twice-daily schedule set the worst case at 12 hours of lost control plane state, and the recovery tooling itself needed patching during the incident.

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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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4. IL-0061 A Terraform pull request merged out of order deleted a production GKE cluster, and recreating it deleted a second one

   Spotify2019

   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.

   Resources deleted

   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

   Configuration change

   Component

   TerraformCI systemGKE control plane

   Contributing factor

   No safeguard on destructive changeMissed in reviewUnverified assumption

   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.

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