Cases47 cases

1 to 5 of 47

1. IL-0071 Worker node running out of inodes while disk space looked fine, because containerd kept a full node_modules tree in each of several image layer snapshots

   Onkar Raghunath Shelke2026

   Symptoms

   A Kubernetes worker node paged with NodeFilesystemFilesFillingUp, the node_exporter mixin alert that predicts a filesystem will run out of inodes. The engineer checked disk space first: df -h showed the root filesystem 83% used, 103G used and 22G available. df -i showed inodes 67% used, 11M used and 5.3M free.

   Node disk full

   Trigger

   Several versions of a single-stage Node.js image, each shipping more than 40,000 files, were held on the node; containerd unpacked every layer with a distinct digest into its own snapshot, each with a full copy of node_modules.

   Root cause

   Capacity exhaustion

   Component

   Node root diskkubeletContainer runtime

   Contributing factor

   Unreviewed defaultsMonitoring blind spotUnknown limit

   Detection

   The NodeFilesystemFilesFillingUp alert from the node_exporter mixin fired on the predicted inode trend while inode use was at 67%.

   Action items

   • Build the image in multiple stages so the final image contains only the compiled assets
   • Add a flat alert on root filesystem inode use above 80% for 30 minutes alongside the predictive alert
   • Put a Prometheus query on a dashboard that shows, per node, inode usage minus disk usage, sorted from the highest
   • Remove unused images from nodes with crictl rmi --prune to free old snapshots

   Lessons

   kubelet's background cleanup works on bytes only. A workload heavy in small files gets no early intervention for inodes, and the first thing kubelet does at 5% free inodes is evict pods. Watch inode use on nodes separately from disk use, and keep dependency trees out of runtime images.

   View case
2. IL-0023 A training run crashed with an NCCL timeout after one InfiniBand port went down once and recovered

   Databricks2026

   Symptoms

   Seven hours into a distributed training run on Databricks AI, the job failed with an NCCL timeout.

   Training job failureNCCL timeoutLink flapping

   Trigger

   A single InfiniBand port used for RDMA NCCL collectives went down once and recovered.

   Root cause

   Dependency failure

   Component

   NCCLInfiniBand fabricPyTorch

   Contributing factor

   Monitoring blind spotUnreviewed defaults

   Detection

   The training job crashed with the NCCL timeout. The continuous health checks, which watched InfiniBand port flapping by count, had not flagged the single flap.

   Remediation

   • Tuned the NCCL_IB_TIMEOUT defaults to be more resilient to short port-down windows
   • Used the port-down signal to crash the job and restart it from a checkpoint instead of waiting for the watchdog
   • Fed the investigation into gpu-monitor, whose RDMA fabric port-down check is thresholded on cumulative downtime rather than flap count

   Action items

   • Tune the NCCL_IB_TIMEOUT defaults to be more resilient
   • Crash and restart the job from a checkpoint on the port-down signal instead of leaving GPUs idle until the watchdog fires
   • Threshold RDMA fabric port-down detection on cumulative downtime, not flap count, in gpu-monitor

   Lessons

   For training impact the signal that matters is cumulative port downtime, not the number of flaps: one long enough flap can crash a multi-day run. NCCL_IB_TIMEOUT, with an effective default of about seven seconds, decides the outcome long before the PyTorch watchdog timeout does. Health checks need to cover the whole node lifecycle: active checks before workloads start, passive checks for silent degradation under load, and periodic multi-node NCCL fabric checks in between.

   View case
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.

   View case
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.

   View case
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.

   View case

By root cause11