Incident Library
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Cases4 cases

1 to 4 of 4

  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

    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.

  3. 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.

  4. Symptoms

    On a Sunday, a team using one of Skyscanner's Kubernetes clusters manually paged the out-of-hours Kubernetes support because their service showed elevated latency, worst at the p90 and maximum levels.

    Trigger

    Upgrading the HAProxy ingress controller to v0.6 and going back from Service virtual IPs to individual pod IPs as backends, on the understanding that the sort-backends flag introduced in v0.5, false by default, meant backends were randomly shuffled.

    Root cause
    Component
    Contributing factor
    Detection

    A cluster user manually paged the out-of-hours Kubernetes support about elevated latency; Prometheus queries then showed the uneven request distribution across pods.

    Remediation
    • switched the affected service from pod IPs back to the Service virtual IP through a config change in the internal deployment tool Slingshot
    • patched the controller to keep each backend's servers in an insertion-ordered slice and changed the template to iterate over that slice
    • tested the patch in sandbox clusters and rolled it out to the production clusters
    Action items
    • Run the internal controller patch in production and return to pod IPs as the default backends
    • Raise the problem upstream with a GitHub issue and pull request
    • Test changes to cluster components against the specific behaviour and conditions that matter
    Lessons

    The team already knew that sorted backends cause hot-podding and still did not test the new flag well enough to see that it did not work; changes to cluster components need tests aimed at the exact behaviour that matters, under conditions like production. A keyword can behave differently in different parts of a language: range over a map is unordered in Go code but sorted in text/template. A small cluster component can do outsized harm to every user of the cluster and, in the end, to customers.

Check your cluster for this

These 4 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.

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