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

Cases2 cases

1 to 2 of 2

  1. Symptoms

    From 12:13 to 15:30 PDT on 25 March 2026, voice and video on Discord were heavily degraded. Users mostly could not start or join calls and saw "Awaiting Endpoint" in their call status.

    Trigger

    A routine change to tune the sessions service cluster on Kubernetes gave each pod more CPU and memory and lowered the pod count in proportion. When it was deployed to the first zone at 12:13 PDT, Kubernetes terminated 50% of that zone's pods, and the termination grace period ran out before the sessions could be handed off.

    Root cause
    Component
    Contributing factor
    Remediation
    • Set low syncer spawn rate limits in the calls, streams and guilds services and restarted the voice syncers cluster
    • Restarted voice syncers instances one at a time, and four of the first five recovered fully
    • Brought 15 new voice syncers instances online, doubling capacity and halving the syncers per instance
    • Restarted the last backed-up voice syncers instance, leaving the doubled cluster fully healthy
    Action items
    • Add a validating admission webhook that rejects a scale-down of Elixir workloads until each pod has handed off its processes
    • Replace the Holster.Pool supervisor with a PartitionSupervisor so outgoing connections are created and checked out concurrently
    • Move gun connection process lifecycle management into Holster.Pool so the gun connection supervisor is no longer used
    • Tune the upstream rate limits that call into voice syncers, with load shedding tooling that can force them
    • Add rate limits on endpoint selection in voice syncers to limit outbound connection creation
    • Monitor the HTTP connection pooling mailboxes
    • Review and expand instrumentation and logging for service discovery announcement and voice syncer RPCs to SFUs
    • Monitor the most critical processes across all services instead of aggregate metrics such as overall mailbox health
    • Bring more workloads into Kubernetes so capacity can be scaled with a single command instead of provisioning VMs by hand
    • Re-architect the A/V system to scale its control plane services horizontally across multiple regions
    Lessons

    Discord says a large enough traffic spike will find a bottleneck somewhere; in earlier large-load incidents its ScyllaDB databases limited recovery, and this time the bottleneck moved to the voice services. Facing a capacity constraint, the team asks whether to increase supply, such as adding services, or reduce demand, such as rate limiting inbound traffic. During an incident it matters to know the toolbox, and Elixir and the BEAM give engineers many introspection options. Any slowdown in the hot loop of a single-threaded process can degrade both the service and the user experience.

  2. Symptoms

    Every rolling update of the team's microservices on Amazon EKS, behind an Application Load Balancer, was followed by intermittent 502 and 503 errors for users. A Slack message at 3 a.m. read "배포는 성공했는데, 서비스는 죽었다" (the deployment succeeded, but the service died). Every pod was Ready and Datadog was green while users saw errors.

    Trigger

    A rolling update of a Deployment served through an ALB that registers pod IPs as targets, with no pod readiness gates.

    Root cause
    Component
    Contributing factor
    Detection

    A Slack message at 3 a.m. reported that the deployment had succeeded but the service was down; Datadog dashboards were green at the time.

    Remediation
    • labelled the namespace with elbv2.k8s.aws/pod-readiness-gate-inject=enabled so the AWS Load Balancer Controller injects pod readiness gates and Kubernetes waits for the ALB target to be healthy before deleting old pods
    Lessons

    A pod being Ready in Kubernetes does not mean the load balancer will send it traffic. Without a readiness gate, a rolling update can delete the old pods while the ALB still considers the new targets unhealthy, and dashboards that track pod status stay green during the errors. Making pods wait longer on shutdown reduced the errors but did not fix the start-up gap and made every deployment much slower; the readiness gate ties the pod's Ready state to the ALB's view of the target.

Check your cluster for this

These 2 cases involve Pod termination and Cloud load balancer. 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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