Cases15 cases

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1. 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.

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2. IL-0112 The daily node rollover put DFDS's EKS nodes on an AMI later recalled for a kernel issue, causing "PLEG is not healthy" NodeNotReady flaps and pod network timeouts

   DFDS2022

   Symptoms

   On 2022-10-31 a planned upgrade of the vpc-cni EKS add-on, started from the deployment pipeline at 13:45 UTC, timed out at 14:10. Only about 10 of the 45 aws-node pods had upgraded, 5 were stuck in Terminating and the other 30 had not been attempted.

   Node NotReadyUpgrade stallIntermittent timeoutsIntermittent connection failures

   Trigger

   At 07:30 UTC on 2022-10-31 the daily node rollover replaced the existing nodes with nodes built from AMI v20221027, the latest AWS EKS node AMI, released on 2022-10-29 and later recalled because of a kernel issue.

   Root cause

   Software defect

   Component

   Node operating system imageLinux kernelkubeletAWS VPC CNINode launch templateTerraform

   Contributing factor

   No staged rolloutAutomation amplificationOverlapping failuresMonitoring blind spotNo health probes

   Detection

   The team noticed nodes flapping between NodeNotReady and NodeReady ("KubeletNotReady PLEG is not healthy") at 14:30 UTC while watching the stalled vpc-cni upgrade, about seven hours after the rollover. Time to detection was 7 hours. Alerting did not catch the NodeNotReady events or the socket growth.

   Remediation

   • cordoned, drained and deleted 15 stuck nodes one at a time to complete the vpc-cni upgrade
   • changed the Terraform infrastructure-modules so a specific AMI could be set
   • rolled the EC2 Auto Scaling group launch configuration back to the previous known good AMI through the pipeline

   Action items

   • Reach out to teams with suggestions about readiness and liveness probes
   • Create a dashboard for readiness and liveness probe coverage
   • Write a wiki article on readiness and liveness probes and their usefulness
   • Modify infrastructure-modules to allow specifying a particular AMI
   • Schedule a discussion on whether to take the latest AMI immediately and how to test new AMIs before they are deployed
   • Implement alerting on abnormal NodeNotReady events and on increased socket use
   • Create a learning path for Kubernetes within the team
   • Collect logs from nodes centrally
   • Create a Prometheus exporter that retrieves the latest available AMI, with a dashboard and alerts

   Lessons

   When a new node AMI has just been rolled out, upgrading other cluster components on the same day makes it hard to tell which change is causing trouble; here the vpc-cni upgrade on the same day confused matters. Taking the latest AMI automatically with no way to pin one meant a rollback first needed a Terraform change. The team also concluded that it needs better communication paths with application teams during incidents.

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3. IL-0177 WebSocket connections to LINE's messaging-hub on Kubernetes dropped all at once whenever another service's change reloaded a shared HAProxy L7 load balancer, and the reconnect wave overloaded a token API and filled nf_conntrack on Nginx

   LINE+2022

   Symptoms

   The graph of WebSocket connections to messaging-hub, the messaging platform used by the Japanese food delivery service Demaecan, showed sudden deep drops even outside peak traffic. The drops kept coming back at irregular intervals. Messages still reached clients, because messaging-hub switches to Firebase Cloud Messaging when a WebSocket is gone.

   Connection resetsDatabase overloadIntermittent connection failuresHigh latency

   Trigger

   Another service that shared the same L7 load balancer deployed its own load balancer configuration in the Verda dashboard, which reloaded HAProxy; 30 minutes later, at the hard-stop-after limit, the old HAProxy process exited and closed every WebSocket connection it still held.

   Root cause

   Configuration change

   Component

   HAProxyCloud load balancerconntrack tableIngress controller

   Contributing factor

   Change with a delayed effectExcessive blast radiusRetry stormUnverified assumption

   Detection

   The developer noticed deep, sudden drops in the WebSocket connection graph that were not tied to peak traffic and kept recurring irregularly. The Nginx packet drops were found through network monitoring.

   Remediation

   • separated the Merchant system's token issuance API into its own deployment so reconnect traffic did not delay other Merchant APIs
   • scaled out Nginx with pod anti-affinity and a label selector so each pod ran on a different node, which stopped the nf_conntrack packet drops
   • raised Ingress Nginx proxy-read-timeout and proxy-send-timeout and the load balancer timeout to 3600s, which did not stop the drops
   • moved messaging-hub from the shared L7 load balancer to an L4 load balancer, which ended the WebSocket drops

   Lessons

   A reload of a shared load balancer by a different team can cut long-lived connections, and with hard-stop-after the cut comes 30 minutes later, far from the change that caused it. The author's lesson is not to narrow the scope of the search too early: trusting that the infrastructure was fine kept the investigation inside the team's own systems for days. A reconnect wave from many WebSocket clients can overload the systems that issue connection tokens and fill nf_conntrack on proxy nodes. Scaling Nginx out only helps for conntrack if the pods land on different nodes, and anti-affinity leaves extra replicas Pending once they outnumber the nodes. That messaging-hub problems can still load the connected systems remains an open problem.

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4. IL-0056 Calls from EKS pods to services behind an NLB in another VPC failed intermittently because the AWS VPC CNI did source NAT on the node

   MindTickle2020

   Symptoms

   On the Saturday the production cluster was moved from kops to EKS, the platform became very slow and too unstable to keep running, and the team reverted.

   Intermittent connection failuresHigh latency

   Trigger

   Production was moved from a kops cluster using Weave networking to a new EKS cluster using the AWS VPC CNI, without external SNAT enabled.

   Root cause

   DNS or network fault

   Component

   AWS VPC CNI

   Contributing factor

   Unreviewed defaultsUnverified assumption

   Detection

   The team lead saw the platform behaving very slowly after the switch to EKS and phoned the author.

   Remediation

   • reverted production to the kops cluster
   • set AWS_VPC_K8S_CNI_EXTERNALSNAT=true in the AWS VPC CNI

   Action items

   • Monitor and alert on the AWS VPC CNI

   Lessons

   In a networking problem every component in the stack is a suspect; ruling out EKS early cost a lot of time. A DaemonSet that pinged every component from every other one exposed the anomaly that led to the fix. The team had only skimmed the AWS CNI documentation before the migration; knowing how the plugin works internally would have led to better judgements and hypotheses while debugging.

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5. IL-0012 CPU limits and an unpatched kernel CFS quota bug throttled multi-threaded services into stalls, failed health checks and timeouts

   Omio2020

   Symptoms

   For about six months, applications on Omio's Google Kubernetes Engine clusters stalled at random: they got stuck, stopped answering health check requests or lost network connections, and nothing the team looked at explained why.

   CPU throttlingProbe failuresHigh error rateHigh latencyIntermittent timeoutsIntermittent connection failures

   Trigger

   Multi-threaded workloads running with CPU limits on nodes whose kernel did not yet have the fix for a known CFS quota bug that causes unnecessary throttling.

   Root cause

   Software defect

   Component

   Container CPU limitLinux kernelkubelet

   Contributing factor

   Unknown limitMonitoring blind spotUnpatched known bug

   Detection

   Months of random stalls and failed health checks led the team to read the cgroup cpu.stat throttling counters inside the pods, which showed far more throttling than the limits should allow.

   Remediation

   • removed CPU limits until the kernel with the CFS bug fix was deployed across all clusters

   Action items

   • Deploy the kernel with the CFS quota bug fix across all clusters
   • Monitor utilisation, saturation and error metrics, API latency and error rates to confirm the change behaves as expected

   Lessons

   A CPU limit is a time quota per 100 ms period, not a share of cores, so a multi-threaded container can be stopped for most of each period long before it uses the cores it was given, and a kernel bug can make that worse. Throttling is invisible in standard Kubernetes metrics and has to be read from the cgroup counters. Removing limits trades away protection against a container that takes a whole node, which is only acceptable when runtimes are tuned.

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