Cases53 cases

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1. IL-0246 New pods on Adyen's big data Kubernetes nodes could not start, failing with "Cilium API client timeout exceeded"

   Adyen2026

   Symptoms

   On Adyen's production big data Kubernetes clusters, new pods on some nodes could not start because setting up their network through Cilium timed out. Once one pod had timed out, every other pod trying to start on the same node timed out as well.

   Pods stuck in ContainerCreating

   Trigger

   Heavy data workloads landed on nodes where the interval of Cilium's periodic garbage collection of expired connection tracking entries had grown, during a quiet period, toward its 12-hour maximum.

   Root cause

   Configuration change

   Component

   Cilium

   Contributing factor

   Change with a delayed effectUnreviewed defaultsRetry stormNo alert on limit

   Remediation

   • Set a fixed connection tracking garbage collection interval, conntrackGCInterval: 60s, and rolled it out to the Cilium DaemonSet

   Action items

   • Alert on the connection tracking garbage collection duration and table size metrics

   Lessons

   Scaling parameters can interact with other logic months later as traffic grows: the larger table that solved capacity limits on 512GB nodes interacted with the garbage collection auto-scaling on the 2TB nodes. Logs only showed symptoms, so profiling and tracing across the container runtime, the CNI plugin, the Cilium agent and the kernel were needed, and simple arithmetic with measured numbers pointed at the cause early. Auto-scaling algorithms need bounds, and the 12-hour maximum interval was too long for this workload. A timeout protects the caller but does not cancel the work behind it. Adyen recommends that Cilium deployments with dynamic workloads alert on cilium_datapath_conntrack_gc_duration_seconds and cilium_datapath_conntrack_gc_entries and set conntrackGCInterval: 60s.

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2. IL-0435 Error rates rose as Aurora MySQL killed connections and declined queries to avoid OOM while load metrics stayed flat

   CyberAgent2026

   Symptoms

   An alert fired one day because an application's error rate had suddenly risen. The error log of the Aurora MySQL RDS instance for that time showed Aurora killing connections and declining queries to avoid running out of memory. The killed queries also made application latency worse.

   High error rateMemory pressure

   Trigger

   An application release added a batch job run every minute by a Kubernetes CronJob. Each run's short-lived pod connected to the database from a new pod IP.

   Root cause

   Capacity exhaustion

   Component

   DatabaseJobs and CronJobs

   Contributing factor

   Unreviewed defaultsChange with a delayed effect

   Detection

   An alert fired on the application's error rate.

   Remediation

   • Set performance_schema to manual management and capped two related Performance Schema parameters, unlimited by default, at 100 in the parameter group, then restarted the RDS instance

   Lessons

   When system alerts fire but the database's load metrics look calm, Performance Schema growth is worth suspecting. The two parameters that were capped are unlimited by default, so connections from ever-changing source addresses keep adding to the records. Capping them has a cost: once the cap is exceeded, per-host figures in Performance Insights are missing. Putting RDS Proxy between the application and Aurora MySQL to keep source hosts stable is another possible measure.

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3. IL-0183 kubeadm's one-year leaf certificates expired, so kube-apiserver could not reach etcd, restarted in a loop and left the cluster unreachable

   ckck8032026

   Symptoms

   The cluster could not be reached from outside. On the control plane node, sudo systemctl status kubelet showed TLS handshake errors such as "http: TLS handshake error from 101.198.0.183:19517: tls: client requested unsupported application protocols" and "http: TLS handshake error from 101.198.0.172:36328: tls: client offered only unsupported versions: [302 301]".

   Control plane unresponsiveContainer crash loop

   Trigger

   The leaf certificates that kubeadm had generated for the cluster, valid for one year by default, expired at 2026-02-20 16:16 UTC.

   Root cause

   Certificate or credential expiry

   Component

   Kubernetes API serveretcdkubeletCalico

   Contributing factor

   Unreviewed defaults

   Detection

   The author could not connect to the cluster from outside and went to the control plane node to read the kubelet logs.

   Remediation

   • renewed all expired certificates with sudo kubeadm certs renew all
   • restarted containerd and the kubelet so the static control plane pods restarted with the new certificates
   • copied the renewed /etc/kubernetes/admin.conf to $HOME/.kube/config

   Lessons

   A kubeadm cluster stops working one year after its leaf certificates were issued unless they are renewed. The first errors seen may be TLS handshake failures or a flag error that does not mention certificates; the decisive message is the x509 expiry in the kube-apiserver log for its connection to etcd. The author recommends checking expiry with kubeadm certs check-expiration and renewing from 30 days before. kubeadm upgrade apply renews the certificates too, so a cluster upgraded at least once a year avoids the problem; a cluster run for a long time without upgrades can renew from cron, for example with 0 3 1 1 * root kubeadm certs renew all && systemctl restart kubelet in /etc/cron.d/kubeadm-cert-renew.

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4. 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

   Yonghyun Kim2025

   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.

   High error rateService outage

   Trigger

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

   Root cause

   Faulty deployment

   Component

   Cloud load balancerIngress controllerPod termination

   Contributing factor

   Unverified assumptionUnreviewed defaultsMonitoring blind spot

   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.

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5. 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

   Yonghyun Kim2025

   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.

   Probe failuresContainer crash loopService outage

   Trigger

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

   Root cause

   Dependency failure

   Component

   Liveness and readiness probes

   Contributing factor

   Unreviewed defaultsAutomation amplificationExcessive blast radius

   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.

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