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1. IL-0433 Shifting API traffic from EC2 to EKS on Fargate overloaded two CoreDNS pods and caused mass name-resolution errors

   MonotaRO2023

   Symptoms

   When MonotaRO raised the share of requests for the remaining endpoints of its backend API sent to EKS from 1% to 25%, Sentry filled with errors saying the API could not reach the database and outside services. The errors were about DNS name resolution, and the traffic had to be shifted back to the EC2 version.

   DNS resolution failuresHigh error rate

   Trigger

   MonotaRO raised the share of requests for the remaining API endpoints routed by the ALB to the EKS version from 1% to 25%.

   Root cause

   Capacity exhaustion

   Component

   CoreDNSClient-side DNS resolver and cacheContainer CPU limitHorizontal pod autoscalerPod termination

   Contributing factor

   Unreviewed defaultsNo resource requests or limitsUnrepresentative stagingUnverified assumption

   Detection

   Sentry showed a large number of errors about connecting to the database and outside services right after the traffic share was raised.

   Remediation

   • Shifted the requests back to the EC2 version to stop the errors
   • Added an HPA to autoscale the CoreDNS pods as a stopgap, which let the migration to EKS finish

   Action items

   • Set ndots to 1 through dnsConfig in the application Deployments
   • Set cache 600 in the Corefile in the coredns ConfigMap to lengthen the CoreDNS cache TTL
   • Add lameduck 30s to the CoreDNS health plugin so CoreDNS keeps serving while it is scaled in
   • Consider reducing the differences between production and staging, weighed against cost

   Lessons

   MonotaRO assumed that a managed service would not cause problems and so skipped researching other companies' migrations, load testing and monitoring. Staging differed from production in its network layout, so ndots differed between them, and staging databases could not take a high-load test. Looking at the CoreDNS logs and resolv.conf instead of guessing found that ndots:5 made lookups of outside names try every search domain first.

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2. 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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