Incident Library
  • Root cause
  • Component
  • Symptom
  • Contributing factor
  • Case

Cases2 cases

1 to 2 of 2

  1. Symptoms

    At 12:00 EST the health check system reported that the backend APIs, the frontend and the tunnels in the EU region were down. The team first took it for a transient error, such as an interrupted client request or a server evicted for memory or CPU pressure, but the main dashboard was offline as well.

    Root cause
    Component
    Contributing factor
    Detection

    The health check system sent a notification that the service was down; the team then found the main dashboard offline.

    Remediation
    • recreated the node pools by hand after the credential rotation failed to recreate them
    • duplicated the main cluster's services into a newly created GKE cluster
    • deleted the Kubernetes Service records in the old cluster so its load balancers could be attached to the new cluster
    • detached the StatefulSet from the old cluster and switched all writes to the main disk
    • exported the data from the temporary ClickHouse instance and inserted it into the main instance
    Action items
    • Change the deployment manifests so services can be switched between clusters quickly without a complicated persistent disk migration
    Lessons

    Recovery of existing infrastructure needs a time limit. Throughout the outage the old cluster looked about to recover, and the reluctance to give up on the salvage effort kept the team from starting over with a new cluster much sooner, which would have been the right call. On older GKE clusters, check the validity of the cluster CA certificate, for example by decoding masterAuth.clusterCaCertificate from gcloud container clusters describe and reading it with openssl x509. GKE's multiple availability zones made no difference in this failure.

  2. Symptoms

    At 08:05 UTC on 2021-06-21 the API service's Apdex (the share of requests that finish within a target time) and Rails request queuing began trending the wrong way. At 09:01 the team found the API deployment unhealthy: it needed more pods for the day's growing traffic, but no new nodes could be added to give it pod capacity. Users saw slow responses; the review found no sign that error rates rose.

    Trigger

    Google Cloud ran out of SSD persistent disk stock in us-east1, the region GitLab.com runs in, while the API service needed more nodes for rising user traffic.

    Root cause
    Component
    Contributing factor
    Detection

    There was no alert on resource availability. Apdex and Rails queuing began trending the wrong way at 08:05 UTC and an engineer declared the incident in Slack at 08:43; finding the cause took a lot of log digging: cluster autoscaler errors about node pools in backoff, then ZONE_RESOURCE_POOL_EXHAUSTED at the instance-group level, and finally Google Cloud support naming SSD as the exhausted resource.

    Remediation
    • added a node pool that uses pd-standard disks
    • scaled the new node pool by hand across all three zones to relieve the API
    • brought more pd-standard node pools online for the other blocked workloads
    • stopped production deployments and opened a Google Cloud support case about the cluster autoscaler not working with the new node pools
    • changed node pools and nodeSelectors so the remaining workloads ran on pd-standard nodes
    • removed the SSD-backed node pools after a day without problems
    Action items
    • Move Kubernetes services to pd-standard disks (delivery#1837)
    • Capture resource exhaustion errors as a metric
    Lessons

    A cloud region can run out of one specific resource, here SSD persistent disks, and the only error may be a generic ZONE_RESOURCE_POOL_EXHAUSTED that does not name it. GitLab could neither monitor nor reserve resource availability, so detection took 22 hours and the cause came from Google Cloud support. The workloads ran fine on pd-standard disks. A second problem found on the way, a cluster autoscaler that does not fall back to another matching node pool when one cannot scale, slowed the move to the new node pools.

Check your cluster for this

These 2 cases involve Node pool and Cluster autoscaler. 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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