Cases7 cases

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1. IL-0004 A new default pod priority let a new Cortex cluster preempt every production ingester in a cascade, taking Grafana Cloud Hosted Prometheus down

   Grafana Labs2019

   Symptoms

   On Friday 19 July 2019 the Grafana Cloud Hosted Prometheus service, which is built on the Cortex project, was down for 26 minutes. New error-budget-based Prometheus alerts paged the on-call engineer within 4 minutes of the start.

   Pod evictionHigh error rateService outageOOMKilled

   Trigger

   An engineer created a new dedicated Cortex cluster for a large customer. Its configuration did not include the new pod priorities, so all its pods got the default medium priority, and the Kubernetes cluster did not have enough free resources to fit it.

   Root cause

   Configuration change

   Component

   Pod priority and preemption

   Contributing factor

   Partial rolloutAutomation amplificationMissed in reviewNo headroomRetry storm

   Detection

   New error-budget-based Prometheus alerts paged the on-call engineer within 4 minutes of the start of the outage.

   Remediation

   • Scaled up the underlying Kubernetes cluster to fit both the new and the production Cortex clusters
   • Diagnosed and fixed the out-of-memory errors in the authenticating reverse proxies in front of Cortex

   Action items

   • Give the production ingesters the high priority class, which should have been done before medium became the default
   • Have more people review changes to configuration that is global to the cluster
   • Require a design document for every configuration change outside a namespace
   • Automate the sizing of the frontend authenticating reverse proxy so it does not run out of memory under overload
   • Investigate the default backoff and scaling behaviour of Prometheus to prevent a stampeding herd of retries

   Lessons

   Medium should not have become the default priority until the production ingesters had the high priority, and that designation should have reached production sooner. The change was considered too minor for a design document and was only discussed in a GitHub issue; cluster-global configuration needs more scrutiny. On the positive side, once Cortex had enough capacity it recovered by itself, and Grafana Loki helped confirm that the ingesters exited gracefully.

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2. 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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