Cases3 cases

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1. IL-0181 A traffic spike from an external community post pushed product API pods into CPU throttling under a 1-core limit, and an e-commerce product page slowed down while every pod stayed Running

   Kevin2026

   Symptoms

   An e-commerce platform suddenly became slow to respond, worst on the product lookup page, and users began reporting that the page would not open.

   CPU throttlingHigh latency

   Trigger

   A post on the external community Ppomppu linked to the platform and brought a large number of users in a short time, who called the product lookup API heavily.

   Root cause

   Capacity exhaustion

   Component

   Container CPU limitHorizontal pod autoscaler

   Contributing factor

   Monitoring blind spot

   Detection

   Users reported that the product page would not open. The dashboard showed all pods Running, and the namespace CPU usage near 100% and the CPU usage metrics pointed to throttling.

   Remediation

   • raised the HPA minReplicas from 5 to 10
   • raised the container CPU limit from 1 core to 2 cores

   Action items

   • Keep adjusting the CPU limit to a suitable level through monitoring

   Lessons

   CPU throttling is hard to notice: pods stay Running, nothing restarts and the logs show no clear error, so a pod list does not reveal it and CPU usage metrics do. Settings that had handled expected event traffic became the bottleneck as soon as one external post multiplied the traffic. The author concludes that the team needs more preparation for traffic pattern changes and more thought about resource settings.

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2. IL-0028 Temporal History service latency rose because its pods were CPU-throttled while CPU usage read below 60 percent

   Temporal2023

   Symptoms

   Request latency from the Temporal Server History service, the service that starts workflow executions and records their progress, was higher and less steady than it should be on a self-hosted Kubernetes deployment. The CPU dashboard showed the history pods using less than 60 percent of their CPU, yet the same pods were being throttled.

   CPU throttlingHigh latency

   Trigger

   Normal load on History pods that had a CPU limit of 2 cores on 8-core nodes, with the Go runtime's GOMAXPROCS left at its default of one per node core.

   Root cause

   Capacity exhaustion

   Component

   Container CPU limitLanguage runtime settingsPrometheus

   Contributing factor

   Unreviewed defaultsMonitoring blind spot

   Detection

   A Prometheus query for CPU throttling showed the history pods throttled although their CPU usage read under 60 percent.

   Remediation

   • set the GOMAXPROCS environment variable to 2 to match the 2-core CPU limit

   Action items

   • Set GOMAXPROCS automatically from the Kubernetes CPU limit, from Temporal release 1.21.0

   Lessons

   A CPU usage graph averaged over 15 seconds or more can sit far below 100 percent while a container is being throttled, so throttling needs its own metric. A Go program in a container should have GOMAXPROCS matched to its CPU limit, or to its CPU request when it has no limit. The author recommends not using CPU limits at all.

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3. 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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