Cases4 cases

1 to 4 of 4

1. IL-0085 A control plane node of Magic Pages' Kubernetes cluster on Hetzner Cloud ran out of memory, the cluster's own calls then used up the Hetzner API rate limit, and all customer websites were down for about 7.5 hours

   Magic Pages2025

   Symptoms

   At around 09:33 UTC one of the three control plane nodes of the Kubernetes cluster that hosts Magic Pages went offline. Looking at the node showed that it had run out of memory.

   Service outageNode NotReadyCloud API rate limit exhausted

   Trigger

   One of the three control plane nodes ran out of memory and went offline at about 09:33 UTC; the attempts to replace it with hetzner-k3s failed while the cluster's own components were exhausting the Hetzner API rate limit.

   Root cause

   Capacity exhaustion

   Component

   Cloud controller managerPersistent volumes and stateful sets

   Contributing factor

   Unknown limitRetry stormMonitoring blind spotLimited debug accessSingle point of failureNo runbook

   Detection

   The founder found the control plane node offline at about 09:33 UTC. Monitoring alerts about unreachable customer websites started around the time the replacement attempts failed, and about half an hour of investigation identified the exhausted Hetzner API rate limit.

   Remediation

   • powered off all servers of the Kubernetes cluster to stop the API requests so the Hetzner rate limit could recover
   • waited until ratelimit-remaining had built up a buffer before starting recovery
   • restarted the control plane servers
   • started the essential Longhorn components by hand so Longhorn could heal and make storage volumes available
   • restarted the remaining websites by hand

   Action items

   • Monitor the Hetzner API call rate coming from the Kubernetes cluster nodes themselves to get early warning of high usage
   • Get a higher Hetzner API rate limit; Hetzner support was contacted
   • Investigate how to reduce the cluster's reliance on a single API, including alternatives to hetzner-k3s
   • Document and test a procedure for recovering Longhorn after a full cluster shutdown

   Lessons

   The running cluster, not only the deployment tooling, consumed the cloud provider's API quota, and in a failure it consumed it fast enough that the quota never recovered. The default limit of 3600 requests per hour was not enough for failure and recovery scenarios, and API usage from inside the cluster was not monitored. A tool that hides the provider's API errors slows diagnosis. After a full, non-graceful shutdown, Longhorn needed manual intervention to come back, which lengthened the outage.

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2. IL-0169 The Scaleway Kubernetes API in FR-PAR ran out of memory under simultaneous deployments, and because it also handled node authentication, Kapsule and Kosmos nodes went NotReady

   Scaleway2024

   Symptoms

   At 23:48 CET on 24 January 2024 the Scaleway Kubernetes API for the FR-PAR region ran out of memory. At the same minute Scaleway saw requests to its API Gateway spike.

   Node NotReadyOOMKilledIntermittent timeoutsMonitoring outage

   Trigger

   Simultaneous deployments in the FR-PAR region pushed the Scaleway Kubernetes API out of memory.

   Root cause

   Capacity exhaustion

   Component

   Container memory limitkubelet

   Contributing factor

   Single point of failureRetry storm

   Detection

   Increased load on the API Gateway was detected at 23:48, the minute the incident began, and the incident was escalated to on-call engineers at 00:00:10.

   Remediation

   • raised the memory limit and garbage collection thresholds of the Kubernetes managed services
   • deployed additional replicas of key services
   • purged the registry cache and monitored the clusters as they stabilised
   • adjusted service configurations after the incident to keep the platform stable

   Action items

   • Implement a local authentication cache
   • Further develop node authentication
   • Enhance monitoring, adjust rate limiting strategies and improve incident response protocols

   Lessons

   Because the Kubernetes API also handled node authentication, its memory exhaustion made some nodes NotReady, and the endless retries of those nodes added load on the API Gateway. Scaleway plans a local authentication cache and further work on node authentication.

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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 kernelApplication database

   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-0040 Parallel Helm installs saturated small gp2 root volumes on a kops cluster and left all three workers NotReady with the kubelet or Docker down

   Saltside2017

   Symptoms

   While running the CI process on a local machine to test an application Helm chart, the SRE lead watched the pods with watch kubectl get pods. All pods reached Running, and then some went back to Pending. Two hours later the problem had grown and some pods showed the phase Unknown.

   Pods PendingImage pull failuresNode NotReadyService outage

   Trigger

   A CI run installed the application's Helm charts for all four markets (Efritin.com, Tonaton.com, Ikman.lk and Bikroy.com) on the nonproduction cluster at the same time.

   Root cause

   Capacity exhaustion

   Component

   Node root diskkubeletContainer runtimeHelm

   Contributing factor

   Unreviewed defaultsMonitoring blind spotIgnored early signalSingle point of failure

   Detection

   The SRE lead saw pods go from Running back to Pending while watching kubectl get pods during the CI run; no monitoring raised an alert, even when all three workers were not ready.

   Remediation

   • terminated the three worker nodes and let the auto scaling group replace them

   Action items

   • Fix the Datadog integration for the nonproduction AWS account and repeat the installs with telemetry
   • Increase the worker root EBS volumes from 20GB to 120GB
   • Keep common base images pulled on every node with a DaemonSet until kops supports user hooks
   • Configure different allocation limits for the different layers in Kubernetes
   • Discuss a highly available Tiller with the Helm team
   • Find a way to throttle the number of newly created pods

   Lessons

   An ordinary deploy of the application could collapse the cluster, because the default 20GB gp2 root volumes could not keep up with parallel image pulls and container starts. Without node metrics and pod phase metrics, three NotReady workers raised no alert. A single Tiller replica can block the deploy that would fix an outage.

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