Cases6 cases

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1. IL-0148 A kube-scheduler regression in Kubernetes v1.27.14, v1.28.10 and v1.29.5 made the scheduler panic on a pod whose node affinity named a nonexistent node, so no pods were scheduled on affected AKS clusters

   Microsoft Azure2024

   Symptoms

   On AKS clusters running Kubernetes v1.27.14, v1.28.10 or v1.29.5, kube-scheduler panicked whenever the cluster contained a pod with a bad node affinity, and from then on no pod in the cluster was scheduled.

   Pods Pending

   Trigger

   A pod in the cluster with a required node affinity whose matchFields term names a node that does not exist.

   Root cause

   Software defect

   Component

   Pod scheduling and placement

   Contributing factor

   –

   Detection

   The AKS team found the bug; the issue does not say how.

   Remediation

   • AKS engineers fixed the impacted clusters
   • Rolled out a fixed kube-scheduler to all regions, applied to clusters automatically

   View case
2. IL-0182 Too few CoreDNS replicas on EKS were overloaded during traffic spikes, raising latency and cutting the network for task pods, and the over-provisioned, tightly spread CoreDNS pods added later blocked Karpenter consolidation

   이언철2024

   Symptoms

   When traffic changed sharply, CoreDNS came under heavy load, and the cluster saw network problems and a sudden rise in latency. The horizontal pod autoscaler then added pods, database connections surged, and the impact spread to the whole service.

   High latencyIntermittent connection failuresAutoscaler misbehaviour

   Trigger

   Sharp changes in traffic loaded the small number of CoreDNS pods beyond what they could handle.

   Root cause

   Capacity exhaustion

   Component

   CoreDNSKarpenterHorizontal pod autoscalerPod scheduling and placement

   Contributing factor

   Automation amplificationHarmful manual action

   Remediation

   • Set up the Cluster Proportional Autoscaler for CoreDNS with nodesPerReplica and coresPerReplica chosen for the instance types in use
   • Spread CoreDNS with soft topologySpreadConstraints (ScheduleAnyway) instead of anti-affinity so Karpenter could consolidate nodes
   • Added alert thresholds on CoreDNS error rate, panics, request duration and request rate
   • Deployed NodeLocal DNSCache so each node answers DNS from a local cache and forwards misses to the main CoreDNS

   Lessons

   A fixed, small number of CoreDNS pods cannot follow a cluster whose nodes and pods change quickly with traffic, and the damage spreads: DNS latency triggers autoscaling, which in turn floods the database with connections. Simply adding many CoreDNS replicas created a different problem, because with tainted nodes and a maxSkew: 1 spread constraint the pods piled onto a few nodes and blocked Karpenter consolidation. Scaling CoreDNS in proportion to the cluster and putting a DNS cache on every node handled both.

   View case
3. IL-0064 CPU-heavy pods packed onto one Google Cloud node drove it to kernel panics, and the crash moved from node to node until the site was down

   Moonlight2019

   Symptoms

   On Friday, January 18, Moonlight's API began returning occasional errors that looked like connection problems with its Redis database. The API checks Redis on every authenticated request to validate the session. At the same time the Kubernetes monitoring reported some nodes and pods as unresponsive.

   High error rateIntermittent connection failuresNode NotReadyService outageSustained high node CPU

   Trigger

   The Kubernetes scheduler put several CPU-heavy pods on the same node, including all three replicas of the website and the server-side rendering pods.

   Root cause

   Capacity exhaustion

   Component

   Pod scheduling and placementLinux kernelDatabase

   Contributing factor

   Single point of failureUnverified assumptionAutomation amplification

   Detection

   API errors that looked like Redis connectivity problems, and Kubernetes monitoring that showed nodes and pods as unresponsive. Google Cloud Support later found the link between sustained 100% CPU and kernel panics.

   Remediation

   • added anti-affinity rules to all major deployments so their pods spread across nodes

   Action items

   • Keep working with Google Cloud support to find and fix the cause of the node kernel panics

   Lessons

   Three replicas give no fault tolerance if the scheduler places them all on one node; anti-affinity rules make the spread explicit. A node failure that comes from the workload itself moves with the workload when the scheduler reschedules it.

   View case
4. IL-0036 A reindex Job that kept failing exhausted one node after another until the whole cluster was down

   Universe2017

   Symptoms

   Shortly after a search reindex job was started, problems with the Kubernetes cluster hit Universe's reporting and search APIs and the Kubernetes cluster UI itself. The status page opened an investigation into reports of search and reporting being unavailable at 14:37 EDT on 2017-07-21.

   Service outageNode NotReadyNode recreation loop

   Trigger

   A change to the search index definition, which removed some fields from full-text indexing, was shipped, and a reindex job was started as a Kubernetes Job to apply it.

   Root cause

   Capacity exhaustion

   Component

   Jobs and CronJobsPod scheduling and placement

   Contributing factor

   Unverified assumptionAutomation amplificationExcessive blast radius

   Detection

   The team saw problems with the Kubernetes cluster shortly after starting the job, and the status page cites reports of search and reporting being unavailable.

   Remediation

   • removed the reindex Job from the scheduler
   • let the nodes auto-heal back to healthy

   Lessons

   restartPolicy: Never on the pod template does not stop a Kubernetes Job from retrying: a Job replaces failed pods and may run more than once, so a job that can exhaust a node will keep moving to new nodes.

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