Incident Library
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Cases14 cases

1 to 5 of 14

  1. Symptoms

    Every rolling update of the team's microservices on Amazon EKS, behind an Application Load Balancer, was followed by intermittent 502 and 503 errors for users. A Slack message at 3 a.m. read "배포는 성공했는데, 서비스는 죽었다" (the deployment succeeded, but the service died). Every pod was Ready and Datadog was green while users saw errors.

    Trigger

    A rolling update of a Deployment served through an ALB that registers pod IPs as targets, with no pod readiness gates.

    Root cause
    Component
    Contributing factor
    Detection

    A Slack message at 3 a.m. reported that the deployment had succeeded but the service was down; Datadog dashboards were green at the time.

    Remediation
    • labelled the namespace with elbv2.k8s.aws/pod-readiness-gate-inject=enabled so the AWS Load Balancer Controller injects pod readiness gates and Kubernetes waits for the ALB target to be healthy before deleting old pods
    Lessons

    A pod being Ready in Kubernetes does not mean the load balancer will send it traffic. Without a readiness gate, a rolling update can delete the old pods while the ALB still considers the new targets unhealthy, and dashboards that track pod status stay green during the errors. Making pods wait longer on shutdown reduced the errors but did not fix the start-up gap and made every deployment much slower; the readiness gate ties the pod's Ready state to the ALB's view of the target.

  2. Symptoms

    While the Spring Cloud Config Server was briefly down for an upgrade, every client application began to answer its liveness probe with a failure saying it could not reach the config server.

    Trigger

    A downtime upgrade of the Spring Cloud Config Server made it stop responding for a short time.

    Root cause
    Component
    Contributing factor
    Lessons

    A liveness probe that includes an external dependency turns a short outage of that dependency into a restart loop of every client. Keep external dependencies out of liveness checks and handle temporary problems with readiness probes. Libraries can add health indicators to Spring Boot's actuator health endpoint automatically, so check what the probe path actually reports. The author ran another Kubernetes environment for two years without liveness probes, relying on strict readiness probes and on the application terminating itself on unrecoverable faults.

  3. Symptoms

    During a Lunar New Year event in January 2024, a developer posted a message in Slack suspecting an outage. While several people were checking it, the problem grew into a full outage of Backpackr's web and app services, and the company decided to stop the event.

    Trigger

    An unexpected traffic surge, more than three times normal, during the Lunar New Year event in January 2024.

    Root cause
    Component
    Contributing factor
    Detection

    A developer reported a suspected outage in Slack; the outage became total while several people were still confirming it.

    Remediation
    • stopped the Lunar New Year event
    Action items
    • Raise net.netfilter.nf_conntrack_max on all nodes to memory bytes/16384/2, and three times that on Istio nodes, applied by the nodeOptimizer DaemonSet
    • Configure Istio mesh networking so sidecars communicate directly instead of through the ingressGateway
    • Upgrade the Istio nodes from c6.xlarge (12.5 Gbps) to n-type instances with up to 25 Gbps of network bandwidth
    • Strengthen monitoring with ENA network bandwidth and packet drops, detailed Istio ingressGateway and sidecar metrics, and CPU, memory and saturation
    Lessons

    When every request in a cluster goes through one ingress gateway node, that node's connection tracking table and network bandwidth set the limit for the whole service. The default nf_conntrack table size was too small for three times normal traffic, and once it filled, packets were dropped. After the outage Backpackr strengthened its monitoring of network bandwidth, packet drops and the Istio components.

  4. Symptoms

    Services on the production Amazon EKS cluster behind the idus marketplace threw intermittent errors whose cause was hard to pin down. Calls to external services such as RDS and Redis failed now and then, and whenever DNS traffic spiked, domain lookups answered slowly or failed outright.

    Trigger

    All CoreDNS pods were scheduled onto the same node, and that node's memory usage spiked until it ran out of memory.

    Root cause
    Component
    Contributing factor
    Detection

    The write-up does not say how the full outage was noticed. The earlier DNS trouble showed up as intermittent service errors, and investigating failed calls to RDS and Redis led to DNS resolution failures.

    Remediation
    • deployed NodeLocal DNSCache as a DaemonSet in front of CoreDNS
    • added a PodDisruptionBudget (maxUnavailable 1) and topologySpreadConstraints across zones for CoreDNS
    • lowered ndots from 5 to 2 with dnsConfig in the Deployments
    Lessons

    The CoreDNS setup that EKS creates by default (two pods) can be enough for a small or development cluster but becomes a bottleneck in a large production cluster, and DNS is a single point of failure that needs high availability and room to scale. Tuning the CoreDNS cache and buffer size only relieved the symptoms; the real load came from the extra queries that ndots:5 generates for external names. A CoreDNS pod without a memory limit can share a node's fate with everything else on it.

  5. Symptoms

    At 09:36 on 18 March 2025, during the upgrade of the live cluster to EKS 1.30, users reported that their pods were being terminated and were not restarting automatically, in both production and non-production environments. Automated infrastructure alerts fired as well, and production services had outages.

    Trigger

    Upgrading the live EKS cluster from 1.29 to 1.30, during which the descheduler attempted to reschedule all pods on the cordoned nodes simultaneously.

    Root cause
    Component
    Contributing factor
    Detection

    Users reported at 09:36 that pods were being terminated and not restarting, and automated infrastructure alerts fired.

    Remediation
    • Removed the cordon annotation so pods could be rescheduled
    • Deleted stuck 1.30 nodes to force their recreation
    • Added a new node group of 20 nodes running 1.30 by hand
    • Cordoned and drained all old 1.29 nodes onto the new 1.30 nodes
    Action items
    • Raise an AWS Production System Down support case (Case ID: 174229643100496)
    • Get guidance from AWS support on querying control-plane logs to find the root cause of similar issues
    • Document a runbook for emergency scaling of a new node group in case of unexpected capacity shortages
    • Alert on an imbalance between the autoscaling group and the Kubernetes nodes
    • Review the descheduler configuration to understand why it throws pods off cordoned nodes, and fix it where possible
    • Alert on spikes in the pending pod count
    Lessons

    The write-up lists review actions rather than lessons. They show the team had no documented way to add an emergency node group, no alert on a mismatch between the autoscaling group and the nodes Kubernetes sees, no alert on a spike in pending pods, and no clear understanding of how the descheduler treats cordoned nodes.

Check your cluster for this

These 14 cases involve Node pool and Ingress controller. 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 cause8

Faulty deployment2 cases

  1. IL-0188 Rolling updates on EKS returned 502 errors for about 30 seconds because Kubernetes deleted old pods as soon as new pods passed readiness, before the ALB had marked the new pod targets healthy
  2. IL-0020 During an EKS 1.30 upgrade the descheduler tried to reschedule every pod on the cordoned nodes at once and production workloads went down

Dependency failure1 case

Organisation
  1. IL-0187 A brief Spring Cloud Config Server downtime failed every client application's liveness probe, because the actuator health check included the config server, and Kubernetes restarted the pods in a kill loop

Capacity exhaustion3 cases

  1. IL-0170 During a Lunar New Year event, traffic more than three times normal filled the nf_conntrack table on the EKS node running the Istio ingress gateway, dropping packets and taking down Backpackr's web and app services
  2. IL-0027 All CoreDNS pods landed on one node, the node ran out of memory, and DNS for the whole service went down
  3. IL-0040 Parallel Helm installs saturated small gp2 root volumes on a kops cluster and left all three workers NotReady with the kubelet or Docker down

Configuration change4 cases

  1. IL-0153 DOKS worker nodes went NotReady at 06:00 UTC on three days because a systemd timer in the Debian image ran apt upgrade and killed it mid kernel upgrade, breaking node networking
  2. IL-0109 A retried node rollover on a DFDS EKS cluster set the Auto Scaling group back to 15 instances, and the default termination policy removed every healthy worker node, leaving the cluster with no nodes
    DFDS2023
  3. IL-0191 A label selector accidentally added to the Loki distributor Service matched only the new pods of a rollout, which took all write traffic, were OOM-killed and stalled the rollout
  4. IL-0055 A failing CronJob with the default concurrency policy piled up thousands of restarting pods and took down a cluster
    Target2018

Breaking change in an upgrade1 case

Organisation
  1. IL-0198 An automatic Ubuntu security update restarted systemd-networkd, which flushed the IP rules Cilium relied on and cut more than 60 percent of Datadog's Kubernetes nodes off the network across regions and cloud providers

Operator error1 case

Organisation
  1. IL-0054 All ingress traffic lost while draining a GKE node pool because the load balancer only targeted new nodes without ingress-nginx pods

unknown1 case

Organisation
  1. IL-0059 Single-master Kubernetes control plane kept dying because the kubelet restarted a healthy etcd on a failing kubeadm liveness probe

Software defect1 case

Contributing factor
Organisation
  1. IL-0072 High etcd write latency made the API servers return stale data, and the production cluster flipped between two views of its state