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Cases2 cases

1 to 2 of 2

  1. Symptoms

    On a Tuesday morning a developer reported that some production logs were missing. Only the logs from a single node were gone; the other nodes kept shipping normally through fluent-bit, the log shipper the team had run for more than three years.

    Root cause
    Component
    Contributing factor
    Detection

    A developer reported missing production logs; no alert fired because fluent-bit's metrics showed no errors.

    Remediation
    • added CPU to the Elasticsearch virtual machines, which cleared the write queue rejections
    • rolled out Fluentd one node at a time, using node affinity to place Fluentd and anti-affinity to remove fluent-bit
    • lowered Fluentd's flush_thread_count to 1 and set flush_interval to 5 seconds so the backlog of old logs did not overflow the buffer
    • enabled Fluentd's Position DB so already-ingested data was skipped
    Action items
    • Build monitoring for Fluentd on its Prometheus integration, including a throughput alert with separate day and night thresholds or a black-box log check
    Lessons

    A log shipper can fail without an error message and with metrics that simply stop moving, so error-based alerts never fire; throughput alerts or end-to-end checks can catch it. Open source software can break in ways nobody expects, and sometimes the way out is to switch to a different shipper fast, node by node. A new shipper that starts from scratch replays every old log on the node and needs to be slowed down.

  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 Telemetry agent and Container runtime. 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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