Cases3 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-0019 An EKS Terraform module upgrade shrank node root disks from 100 GB to 20 GB and image pulls failed with no space left on device

   UK Ministry of Justice2023

   Symptoms

   At 13:42 the RootVolUtilisation-Critical alert fired in the high-priority alerts channel. At 14:03 a user reported image pull errors with the message "no space left on device".

   Node disk fullImage pull failuresPod evictionPods stuck in ContainerCreatingIP address exhaustion

   Trigger

   Upgrading the EKS Terraform module to version 18, which changed the node launch template, and draining workloads onto the new nodes it created.

   Root cause

   Configuration change

   Component

   Node root diskNode launch templateNode poolAWS VPC CNICoreDNS

   Contributing factor

   Missed in reviewNo configuration testsNo alert on limit

   Detection

   The RootVolUtilisation-Critical alert fired in the high-priority alerts channel at 13:42, and a user reported "no space left on device" image pull errors at 14:03.

   Remediation

   • Uncordoned the old default node group and drained the new nodes to move workloads back
   • Identified the launch template change that cut the root volume to 20 GB and applied a fix on the manager and live clusters
   • Drained the old node group onto the fixed nodes

   Action items

   • Update the runbook to compare launch template changes during an EKS module upgrade
   • Create a test setup that pulls images of different sizes, like live
   • Update the RootVolUtilisation alert runbook to check the disk space configuration
   • Scale CoreDNS dynamically based on the number of nodes
   • Investigate whether IPv6 can solve the IP prefix starvation problem
   • Add drift testing to identify when a terraform plan shows a change to the launch template
   • Set up logging for the CNI and ipamd logs and alerts on errors related to IP prefix starvation

   Lessons

   The write-up lists review actions rather than lessons. They aim at two gaps: the launch template changes brought by a module upgrade were not compared or drift-tested before rollout, and there were no CNI or ipamd logs or alerts to show the IP prefix starvation the team suspected during the drain.

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3. IL-0035 Worker nodes went NotReady after a new ElastAlert rule without memory limits used up node memory

   NU.nl2019

   Symptoms

   The NU.nl development team got reports that several services on one of its Kubernetes clusters behaved erratically: occasional error pages, slow responses and timeouts. The affected services were mostly internal tools and dashboards.

   Node NotReadyHigh error rateHigh latencyIntermittent timeoutsMonitoring outageOOMKilled

   Trigger

   A new ElastAlert rule, deployed shortly before from the command line, that fired if the Cloudfront-* Elasticsearch indexes stopped receiving documents; those indexes take a few million requests per hour, and the rule made ElastAlert's memory grow without bound.

   Root cause

   Configuration change

   Component

   Container memory limitkubeletPrometheus

   Contributing factor

   No resource requests or limitsUnreviewed defaultsNo alert on limitMonitoring blind spotExcessive blast radius

   Detection

   Users reported error pages, slow responses and timeouts from several services; no alert fired.

   Remediation

   • terminated the NotReady nodes one by one so the auto-scaling group replaced them (the new nodes also went NotReady, but Prometheus and Grafana landed on a stable node)
   • found the ElastAlert pods' memory growth on the Grafana pod memory dashboard
   • fixed the ElastAlert problem that caused the memory growth

   Action items

   • Define alerts on resource usage such as CPU, memory and disk space
   • Keep researching an alerting strategy that combines Elasticsearch, Prometheus Alertmanager and New Relic
   • Consider exporting metrics out of the cluster and running Grafana outside it, on EC2 or a separate Kubernetes cluster
   • Either visualise the important metrics in the ELK stack or make Prometheus and Grafana more available, and improve the metrics strategy
   • Set resource limits for ElastAlert through the Helm values
   • Configure namespaces with default memory requests and limits
   • Isolate customer-facing workloads from build, logging and monitoring workloads (a dedicated node group, or later a separate tools cluster)
   • Deploy everything through automation such as Jenkins pipelines, and consider a GitOps approach
   • Deploy through a pipeline that includes a smoke test step
   • Make Kubernetes and cloud infrastructure work part of the team's sprints, picked up by all team members while pairing with more experienced members, and hold workshops that go deep into specific topics

   Lessons

   Fixing the root cause covers only a small part of what an incident can teach; the chain of smaller failures around it is where most of the learning is. A pod without a memory limit can take whole nodes down, so limits need defaults at the namespace level. Monitoring that runs inside the cluster it watches goes blind with it. Changes installed by hand from the command line leave the rest of the team without a record of what changed.

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