Incident Library
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Cases2 cases

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  1. Symptoms

    The graph of WebSocket connections to messaging-hub, the messaging platform used by the Japanese food delivery service Demaecan, showed sudden deep drops even outside peak traffic. The drops kept coming back at irregular intervals. Messages still reached clients, because messaging-hub switches to Firebase Cloud Messaging when a WebSocket is gone.

    Trigger

    Another service that shared the same L7 load balancer deployed its own load balancer configuration in the Verda dashboard, which reloaded HAProxy; 30 minutes later, at the hard-stop-after limit, the old HAProxy process exited and closed every WebSocket connection it still held.

    Root cause
    Component
    Contributing factor
    Detection

    The developer noticed deep, sudden drops in the WebSocket connection graph that were not tied to peak traffic and kept recurring irregularly. The Nginx packet drops were found through network monitoring.

    Remediation
    • separated the Merchant system's token issuance API into its own deployment so reconnect traffic did not delay other Merchant APIs
    • scaled out Nginx with pod anti-affinity and a label selector so each pod ran on a different node, which stopped the nf_conntrack packet drops
    • raised Ingress Nginx proxy-read-timeout and proxy-send-timeout and the load balancer timeout to 3600s, which did not stop the drops
    • moved messaging-hub from the shared L7 load balancer to an L4 load balancer, which ended the WebSocket drops
    Lessons

    A reload of a shared load balancer by a different team can cut long-lived connections, and with hard-stop-after the cut comes 30 minutes later, far from the change that caused it. The author's lesson is not to narrow the scope of the search too early: trusting that the infrastructure was fine kept the investigation inside the team's own systems for days. A reconnect wave from many WebSocket clients can overload the systems that issue connection tokens and fill nf_conntrack on proxy nodes. Scaling Nginx out only helps for conntrack if the pods land on different nodes, and anti-affinity leaves extra replicas Pending once they outnumber the nodes. That messaging-hub problems can still load the connected systems remains an open problem.

  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 conntrack table and HAProxy. 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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