Incident Library
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  • Root cause
  • Component
  • Symptom
  • Contributing factor
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Cases2 cases

1 to 2 of 2

  1. Symptoms

    From 12:17 to 12:49 PDT on 17 July 2018, Google Cloud HTTP(S) Load Balancers answered a share of requests with 502 return codes, and connections to TCP/SSL Proxy Load Balancers were reset. The share of failed requests moved between 33% and 87% during those 32 minutes, and automated monitoring alerted Google engineers at 12:19.

    Trigger

    A configuration change in the production environment intermittently set off a bug in a new feature of the second-layer Google Front Ends.

    Root cause
    Component
    Contributing factor
    Detection

    Automated monitoring alerted Google's engineering team at 12:19 PDT, two minutes after the 502s began.

    Remediation
    • Reverted the configuration change at 12:44 PDT, which stopped the GFE restarts
    Action items
    • Add safeguards that disable features not yet in service
    • Harden the GFE testing stack against latent bugs in production binaries that make a task restart
    • Isolate the shards of GFE pools further from each other to reduce the scope of failures
    • Build a consolidated dashboard of all configuration changes for GFE pools
  2. Symptoms

    On 30 January 2017, Google Compute Engine instances, Cloud VPN tunnels and GCE network load balancers that were created or live migrated between 10:36 and 12:42 PDT could not be reached on their public IP addresses. Resources created earlier, and instances that were not live migrated, kept working.

    Trigger

    A large set of updates was applied to a rarely used load balancing configuration on the shared layer 2 load balancers.

    Root cause
    Component
    Contributing factor
    Remediation
    • Restarted the jobs that program changes into the network load balancers, so the stuck changes were processed as a batch
    • Applied the fix zone by zone between 11:36 and 12:42
    Action items
    • Increase the canary timeout so updates that hit the inefficient code path only slow network changes instead of stopping them
    • Improve the inefficient code path
    • Write new tests that check behaviour on a wider range of configurations
    • Accelerate replacing global propagation of address configuration with decentralized routing
    • Add metrics and alerting that identify this kind of issue sooner
Check your cluster for this

These 2 cases involve Cloud load balancer and CDN and edge network. 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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