Incident Library
Applied filters ComponentTelemetry agentSymptomDNS resolution failuresFactorMonitoring blind spotClear allEvery case under the filters, with no sentence; newest first.
Suggested filters Search, and the values found in your words appear here.

Graph

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

Cases2 cases

1 to 2 of 2

  1. IL-0005 A new telemetry service overloaded the Kubernetes API servers of large clusters and broke DNS-based service discovery for all OpenAI services
    OpenAI2024
    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.

    Service outageHigh error rateControl plane unresponsiveDNS resolution failures
    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
    Configuration change
    Component
    Kubernetes API serverClient-side DNS resolver and cacheTelemetry agent
    Contributing factor
    Unrepresentative stagingNo staged rolloutCache-masked failureMonitoring blind spotLimited debug access
    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.

    View case
  2. IL-0075 Kubernetes API servers pegged on CPU when the cluster grew to about 290 nodes, traced to Datadog agents polling the API
    Civis Analytics2019
    Symptoms

    After Civis released its new script infrastructure on Kubernetes internally in September 2018, developers reported that kubectl commands were slow, and users intermittently could not start notebooks and services.

    Control plane lagControl plane unresponsiveHigh latencyHigh error rateDNS resolution failures
    Trigger

    Migrating clients' Python, R and container scripts from the in-house Bocce system to Kubernetes (the project called Croquet) pushed the cluster to about 290 EC2 instances under a large script load.

    Root cause
    Capacity exhaustion
    Component
    Kubernetes API serverTelemetry agentetcd
    Contributing factor
    Unknown limitUnverified assumptionMonitoring blind spotNo capacity planning
    Detection

    Developer reports of slow kubectl commands and user reports of notebooks and services failing to start, then high CPU on the master nodes.

    Remediation
    • moved internal users back to the old Bocce infrastructure
    • resized the three master nodes from m4.large to m4.4xlarge
    • changed the Datadog setup so per-node agents no longer polled the API server for service metadata
    • raised max-requests-inflight for non-mutating requests from 400 to 1200
    • grew the etcd EBS volumes from 20 to 100 GB (100 to 300 IOPS)
    • switched client scripts from the ClusterFirst DNS policy to Default
    Action items
    • Upgrade etcd from 2.2.1 to 3.x once Kops supports a safe migration
    • Solve the in-cluster DNS timeouts so client scripts can talk to other workloads in the cluster
    Lessons

    Kubernetes can reach thousands of nodes only after tuning, and third-party DaemonSets are a common cause of control plane load: an agent on every node that polls or watches the API server multiplies with the cluster size. Datadog, kube2iam and the fluentd metadata filter all showed this. A small configuration change often fixes it, but finding it took load testing and the API server access logs.

    View case
Check your cluster for this

These 2 cases involve Kubernetes API server and Telemetry agent. 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.

Open kubegap

By root cause2

Configuration change1 case

Component
Client-side DNS resolver and cacheKubernetes API serverTelemetry agent
Contributing factor
Cache-masked failureLimited debug accessMonitoring blind spotNo staged rolloutUnrepresentative staging
Organisation
OpenAI
  1. IL-0005 A new telemetry service overloaded the Kubernetes API servers of large clusters and broke DNS-based service discovery for all OpenAI services
    OpenAI2024

Capacity exhaustion1 case

Component
etcdKubernetes API serverTelemetry agent
Contributing factor
Unverified assumptionUnknown limitMonitoring blind spotNo capacity planning
Organisation
Civis Analytics
  1. IL-0075 Kubernetes API servers pegged on CPU when the cluster grew to about 290 nodes, traced to Datadog agents polling the API
    Civis Analytics2019