Cases3 cases

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1. IL-0029 Slow user-facing services from CPU throttling on containers that used far less CPU than their limit

   Buffer2020

   Symptoms

   Services on Buffer's kops-managed cluster on AWS (about 60 nodes and 1500 containers) answered more slowly than they should have. The container throttling metric showed that many containers were being throttled whatever their CPU usage was, including containers that never came close to their CPU limit.

   CPU throttlingHigh latencyNode NotReady

   Trigger

   CPU limits on user-facing services, enforced through CFS quota by a Linux kernel with a bug that throttled containers even when they were well under their limit.

   Root cause

   Software defect

   Component

   Container CPU limitLinux kernelNode operating system image

   Contributing factor

   Unpatched known bugUnverified assumption

   Detection

   While reviewing the container throttling metric, the team noticed many containers being throttled even though their CPU usage stayed far below their limits.

   Remediation

   • removed CPU limits from all services directly or indirectly on the users' critical path
   • moved those services to dedicated tainted nodes and gave them matching tolerations
   • set each service's CPU request to its peak usage over a few months plus a margin of more than 20%, and set memory requests the same way

   Action items

   • Move nodes to a kernel that contains the CFS throttling fix (4.19 or later), which for Buffer means a kops version whose host image is Ubuntu 20.04
   • After the kernel upgrade, check whether throttling is fully gone and update the post

   Lessons

   A CPU limit can slow a service down even when the service uses a small part of it, because of how the kernel enforces CFS quota on older kernels; the throttling metric shows it, the CPU usage graph does not. Upgrading the kernel is the safer fix. Removing limits works for latency, but only together with honest CPU and memory requests, isolation of the unbounded services, and monitoring of node usage, since unbounded containers can starve the kubelet.

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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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3. IL-0057 Higher p99 latency after moving a reverse proxy to EKS, from outgoing connections delayed by a SNAT conntrack race

   Prezi2019

   Symptoms

   Prometheus alerts fired on tail latency: from 7/24, the p99 response time of a few services went up after Prezi moved its Nginx-based reverse proxy, smartgateway, from dedicated Elastic Beanstalk machines onto shared Kubernetes worker nodes. The bump affected only the tail, so the earlier monitoring of average response times would most certainly have missed it.

   High latencyIntermittent timeouts

   Trigger

   Moving the reverse proxy onto shared EKS worker nodes, where its outgoing connections went through source NAT on the node and some SYN packets were dropped by a race in Netfilter's conntrack.

   Root cause

   DNS or network fault

   Component

   conntrack tableAWS VPC CNILinux kernelNode operating system image

   Contributing factor

   Unreviewed defaultsUnpatched known bug

   Detection

   Prometheus alerts on p99 latency fired after the reverse proxy migration.

   Remediation

   • added AWS X-Ray tracing and an $upstream_connect_time trace attribute to locate the delay
   • ran more pods of the service on more nodes, which reduced but did not end the delays
   • set AWS_VPC_K8S_CNI_EXTERNALSNAT=true on the AWS VPC CNI to turn off SNAT on the nodes

   Lessons

   Alerting on tail latency instead of averages caught a problem that would otherwise have gone unnoticed, and tracing turned a vague slowdown into a measured connect delay. Delays of exactly 1 or 3 seconds on new outgoing connections point to dropped SYN packets, and on EKS the SNAT done by the VPC CNI is where to look.

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