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

Cases2 cases

1 to 2 of 2

  1. Symptoms

    An operation to upgrade the stats agent in Kubernetes was announced in advance, with a warning that metrics might be unavailable to services for a few minutes. A few minutes later a reply came in saying that all of the sender's Kubernetes services seemed to be down.

    Trigger

    A scheduled upgrade of the stats agent sidecar, during which the agent container crashed.

    Root cause
    Component
    Contributing factor
    Detection

    A reply to the operation announcement, a few minutes later, said that all of the sender's Kubernetes services appeared to be down.

    Action items
    • Mark logging, tracing and stats agent containers as non-critical and leave them out of the service mesh's pod readiness check
    Lessons

    Every container in a pod affects the pod's readiness, so each sidecar needs a deliberate decision about how its health should count. A sidecar that crashes is not ready even when it has no probes, and any logic that requires all containers to be ready will take the pod out of traffic.

  2. Symptoms

    An upgrade of the network subsystem under Target's datacenter OpenStack, meant to cause only a brief disruption, ran into problems and left network connectivity disrupted for several hours. Kafka, which runs on that OpenStack, became intermittently available.

    Trigger

    A network subsystem upgrade in the datacenter OpenStack infrastructure disrupted connectivity for several hours and made Kafka intermittently available.

    Root cause
    Component
    Contributing factor
    Remediation
    • enabled gossip encryption on Consul so messages from the poisoned mesh were rejected
    • stabilized the deployment engine and redeployed the development environment with encryption, terminating instances without it
    • upgraded Consul in place to 1.2.3 and then to 1.3.0
    Action items
    • Reduce how much of an outage a single Docker daemon can cause, together with running smaller and more numerous clusters
    Lessons

    Smaller and more numerous clusters limited the damage: the smaller development clusters and production were not hit the same way as the one large cluster, and dev and prod on one cluster would have been far worse. A Docker daemon shared by many workloads is a brittle failure point. Per-workload logging and metrics sidecars were judged the right design; a shared cluster-level agent would almost certainly have made the problem worse and harder to fix.

Check your cluster for this

These 2 cases involve Service discovery 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.

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