Cases4 cases

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1. IL-0440 Pods on Cybozu's Neco Kubernetes platform got frequent no such host and i/o timeout DNS errors from dropped packets

   Cybozu2024

   Symptoms

   A team using Neco, the Kubernetes platform run by Cybozu's Cloud Platform team, reported what looked like a performance problem in the cluster DNS servers. An application in a pod that resolved cluster Services at hundreds of requests per second frequently got `no such host` and `i/o timeout` errors.

   DNS resolution failuresIntermittent timeoutsPacket loss

   Trigger

   The number of MySQL clusters watched by the monitoring application increased, and after that the name resolution errors became frequent.

   Root cause

   Capacity exhaustion

   Component

   Linux kernelClient-side DNS resolver and cache

   Contributing factor

   Unreviewed defaults

   Detection

   A team using the platform contacted the Cloud Platform team, describing the errors as a performance problem in the cluster DNS server.

   Remediation

   • Raised the kernel parameter netdev_max_backlog on the nodes
   • Set up alerts on softnet
   • Wrote a runbook for the softnet alerts

   Action items

   • Evaluate Cilium's netkit device so pod packets bypass the backlog queue

   Lessons

   Raising netdev_max_backlog makes the same failure less likely and the new alerts catch it early, but it is not a root fix: a pod that sends more packets can fill the backlog queue again. Cybozu sees replacing the veth interface with netkit, which skips the backlog queue, as the root fix.

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2. IL-0176 Prometheus in a 235-node Kubernetes cluster crash-looped because over two million series exhausted host memory and the kernel OOM killer chose it first, as Burstable QoS gave it oom_score_adj 999

   LINE2020

   Symptoms

   One Monday morning the author opened the Prometheus dashboard to check a Kubernetes cluster for alerts and could not reach it. The Prometheus pod was in CrashLoopBackOff: kubectl get pods showed it 1/2 ready with 950 restarts over 3d20h on worker3.

   Container crash loopOOMKilledMonitoring outageProbe failures

   Trigger

   After start, the Prometheus time series count rose to 2,162,256 within five minutes and its memory use filled the 8GB host.

   Root cause

   Capacity exhaustion

   Component

   PrometheuskubeletLinux kernel

   Contributing factor

   No resource requests or limitsNo capacity planning

   Detection

   The author found it on a Monday morning when the Prometheus dashboard, opened to check the cluster for alerts, could not be reached.

   Remediation

   • removed most of the unnecessary settings from the Prometheus ConfigMap, after which the container stopped crashing

   Action items

   • Set appropriate resource requests and limits on the Prometheus pod
   • Reduce unneeded metrics by adjusting scrape targets or scrape interval
   • Estimate Prometheus memory needs, provide dedicated hosts with a node label such as deploy-topology: Prometheus, and pin the Prometheus pod to them with a nodeSelector

   Lessons

   Exit code 137 together with an OOM event in the kubelet log means the kernel killed the container, not kubelet. Under node memory pressure the kernel kills the process with the highest score, and kubelet derives each container's oom_score_adj from the pod's QoS class: a container without a memory request in a pod where another container has one ends up Burstable with a score near 1000, so it is killed first. In a large cluster, Prometheus memory needs to be estimated from the expected series count and requested explicitly.

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3. IL-0064 CPU-heavy pods packed onto one Google Cloud node drove it to kernel panics, and the crash moved from node to node until the site was down

   Moonlight2019

   Symptoms

   On Friday, January 18, Moonlight's API began returning occasional errors that looked like connection problems with its Redis database. The API checks Redis on every authenticated request to validate the session. At the same time the Kubernetes monitoring reported some nodes and pods as unresponsive.

   High error rateIntermittent connection failuresNode NotReadyService outageSustained high node CPU

   Trigger

   The Kubernetes scheduler put several CPU-heavy pods on the same node, including all three replicas of the website and the server-side rendering pods.

   Root cause

   Capacity exhaustion

   Component

   Pod scheduling and placementLinux kernelDatabase

   Contributing factor

   Single point of failureUnverified assumptionAutomation amplification

   Detection

   API errors that looked like Redis connectivity problems, and Kubernetes monitoring that showed nodes and pods as unresponsive. Google Cloud Support later found the link between sustained 100% CPU and kernel panics.

   Remediation

   • added anti-affinity rules to all major deployments so their pods spread across nodes

   Action items

   • Keep working with Google Cloud support to find and fix the cause of the node kernel panics

   Lessons

   Three replicas give no fault tolerance if the scheduler places them all on one node; anti-affinity rules make the spread explicit. A node failure that comes from the workload itself moves with the workload when the scheduler reschedules it.

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4. IL-0472 Node-to-pod traffic and DNS failed on Tinder's 605-node Flannel cluster when every node's ARP cache passed gc_thresh3

   Tinder2019

   Symptoms

   In the early morning of 8 January 2019 Tinder's platform had a persistent outage. Communication from nodes to pods failed, and so did DNS lookups, because DNS ran inside the Kubernetes cluster.

   Service outageNetwork unreachableDNS resolution failures

   Trigger

   Earlier that morning, pod and node counts on the cluster were scaled up in response to an unrelated increase in platform latency.

   Root cause

   Capacity exhaustion

   Component

   Linux kernelCNI plugin

   Contributing factor

   Unreviewed defaults

   Remediation

   • Raised gc_thresh1, gc_thresh2 and gc_thresh3 on the nodes
   • Restarted Flannel so it re-registered the missing networks

   Lessons

   Tinder lists the outage among its learnings about network fabric limits: with Flannel over VXLAN, every node, and every pair of nodes that exchange pod traffic, adds entries to each node's ARP table, so at 605 nodes the default gc_thresh3 hard cap was exceeded on all nodes.

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