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

Cases2 cases

1 to 2 of 2

  1. Symptoms

    Alerts fired at 3:13 PM PST on 11 December 2024, and from 3:16 PM every OpenAI service was significantly degraded or completely unavailable, including ChatGPT, the API and Sora. The status page reported API calls returning errors and difficulties logging in to platform.openai.com and ChatGPT. Customer impact peaked at 3:40 PM.

    Trigger

    At 2:23 PM PST a change introducing a new telemetry service, which collects detailed Kubernetes control plane metrics, was merged and the deployment pipeline started. Between 2:51 PM and 3:20 PM the change was applied to all clusters.

    Root cause
    Component
    Contributing factor
    Detection

    OpenAI's detection tools worked: alerts fired at 3:13 PM PST, a few minutes before customer impact began at 3:16 PM, and the root cause was identified at 3:16 PM.

    Remediation
    • Scaled clusters down to reduce the total Kubernetes API load
    • Blocked network access to the Kubernetes admin APIs so no new expensive requests arrived
    • Scaled up the Kubernetes API servers
    • Removed the telemetry service once enough control was back
    • Moved traffic from impacted clusters to healthy ones
    • Intervened manually in clusters where simultaneous resource downloads saturated resource limits
    Action items
    • Put every infrastructure configuration change through a phased rollout with continuous monitoring of both the service workloads and the clusters, including the Kubernetes control plane
    • Run fault injection tests in which the data plane runs without the control plane for longer, and tests that roll out bad changes on purpose to check detection and rollback
    • Build break-glass mechanisms so engineers can reach the Kubernetes API servers in any circumstances
    • Decouple the Kubernetes data plane from the control plane so that service discovery for critical workloads does not depend on the control plane
    • Speed up recovery with better caching and dynamic rate limiters for resources needed at cluster startup, and regular drills that rapidly replace an entire cluster
    Lessons

    Several systems and processes failed at once and interacted in unexpected ways: tests did not catch the change's effect on the Kubernetes control plane, DNS caching put a delay between the change and the failures, and being locked out of the control plane made remediation very slow. Because service discovery used Kubernetes DNS, the control plane was carrying load for product traffic even though the data plane was meant to be independent of it.

  2. 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.

Check your cluster for this

These 2 cases involve Kubernetes API server and Client-side DNS resolver and cache. 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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