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

Cases2 cases

1 to 2 of 2

  1. Symptoms

    At 07:53 UTC on 2022-03-31 the rate of 50x errors on GitLab.com went up, and at 07:54 PagerDuty paged the engineer on call (EOC) about the rising error rate. Users saw slow pages and intermittent 50x error pages. The incident lists the Web, API, Git and Pages services as affected.

    Trigger

    A k8s-workloads/gitlab-com pipeline deployed merge request !1643, a GitLab chart bump that changed the port names of Kubernetes Services and Deployments (related to the Prometheus service and pod monitors), starting with the canary environment gprd-cny at 07:49 UTC.

    Root cause
    Component
    Contributing factor
    Detection

    A PagerDuty alert on the rising error rate paged the EOC at 07:54 UTC, one minute after the 50x errors went up. The engineer deploying the change noticed the errors and linked them to it.

    Remediation
    • drained the canary environment and re-enabled it after twenty minutes
    • cancelled the deployment jobs for clusters c and d before they started
    • drained gprd-us-east1-d in HAProxy and re-enabled it five minutes later
    • raised the minimum replica count above 100 (merge request !1650)
    • drained gprd-us-east1-c in HAProxy, deployed the change to it, and re-enabled it
    • reverted the minimum replica increase (merge request !1652)
    Action items
    • Implement graceful draining of a whole regional cluster
    • Create a Chatops command that puts PagerDuty into maintenance mode
    Lessons

    Renaming ports on Kubernetes Services and Deployments behind a GCP load balancer made the load balancer reconfigure, and requests were slow or timed out until the new configuration had fully applied, which took minutes. GitLab notes that alerting worked well. The follow-up work is a way to drain a whole regional cluster gracefully and a Chatops command to put PagerDuty into maintenance mode.

  2. Symptoms

    Shortly after a round of changes, loveholidays saw connectivity problems and raised error rates on its aggregation service. The service is a proxy between two other services of more than 100 instances each, handles over 10,000 requests per second continuously and can spike to four times that.

    Trigger

    Three changes together: a change in service architecture, halving the RAM of the nodes that run the aggregation service, and sharding a downstream service, which doubled the number of connections to track.

    Root cause
    Component
    Contributing factor
    Detection

    Rising error rates and climbing connection reset errors in HAProxy, then node_exporter conntrack metrics compared with their limit.

    Remediation
    • moved the aggregation service to nodes with more RAM
    • limited the number of aggregation service pods per node
    Action items
    • Alert when conntrack table use goes above 75%, with (node_nf_conntrack_entries / on (pod) node_nf_conntrack_entries_limit / on (pod) group_right kube_pod_info) > 0.75
    Lessons

    The conntrack limit is tied to node memory, so resizing a node pool silently resizes the connection table, and on GKE the ratio did not match the commonly cited formula. A high-traffic proxy on small nodes can exhaust it, and the symptoms are connection resets and timeouts, so conntrack use needs its own metric and alert.

Check your cluster for this

These 2 cases involve HAProxy 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.

By root cause2