Cases52 cases

12 to 16 of 52

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-0233 A VPC control plane release broke IP alias routes in Nebius eu-west1, and Managed Kubernetes pods lost connectivity

   Nebius2025

   Symptoms

   On 13 March 2025 practically every pod of Nebius Managed Services for Kubernetes in the eu-west1 region lost network connectivity for at least some time between 13:36 and 18:11 UTC. Packets for the affected IP alias routes were not processed by the data plane. The eu-north1 region was not affected.

   Network unreachable

   Trigger

   A release of a VPC control plane component in eu-west1, deployed to canaries from 13:36 UTC and to all other nodes from 14:35 UTC, changed route distinguisher values for a new feature. The change made a long-standing bug in the handling of route announcements take effect for the update messages of Managed Kubernetes pods.

   Root cause

   Software defect

   Component

   Network control plane (SDN)BGP routing

   Contributing factor

   Ineffective rollbackGap in automated testsMonitoring blind spot

   Remediation

   • Restarted the host VPC component on the affected nodes one node at a time
   • Restarted the host VPC component on the remaining nodes in parallel once the restart was known to fix the problem

   Action items

   • Fix the bug in the VPC host component itself
   • Add an alert on a diverging number of routes
   • Add an alert on the number of drops in the data plane
   • Develop a way to reset endpoints without restarting the host VPC component
   • Move the VPC's persistent e2e test virtual machines to canary hosts
   • Add persistent e2e tests for Managed Services for Kubernetes
   • Run e2e tests more often
   • Add a deployment stage in which non-critical internal services receive updates first

   Lessons

   The failure needed both an existing IP alias at release time and a particular order of node updates, and neither the testing installation nor the canaries met both, so neither caught it. Missing alerts on technical metrics that deviate from normal, here the number of routes and the number of drops, made the response much slower.

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3. IL-0138 A failed Nvidia driver build left broken kernel packages on the only Kubernetes master node, which hit a kernel panic on reboot after a power outage and took the homelab cluster down for six days

   Barush Mendez2025

   Symptoms

   Every service in the homelab Kubernetes cluster stopped answering: the V-Rising server, Grafana, Jellyfin and Linkding were all unreachable, and the Kubernetes API was down, so the cluster could not be managed at all. The outage was found by hand, about 3 hours after it began, when someone tried to use the services.

   Control plane unresponsiveService outageMonitoring outage

   Trigger

   A power outage forced the Kubernetes master node to reboot.

   Root cause

   Software defect

   Component

   GPU driverLinux kernelKubernetes API server

   Contributing factor

   Ignored early signalChange with a delayed effectSingle point of failureMonitoring blind spot

   Detection

   Manually, when someone tried to access the services; there were no automated alerts.

   Remediation

   • Connected a monitor to the master node and read the boot logs, which showed the kernel panic
   • Booted the previous kernel version, which brought the system back
   • Removed the broken Nvidia packages with sudo apt-get remove --purge '^nvidia-.*' and ran sudo dpkg --configure -a
   • Found remaining broken packages with dpkg -l | grep -v ^ii and removed them by hand
   • Ran sudo apt-get install -f and reinstalled linux-headers-$(uname -r)
   • Rebooted and confirmed the kernel panic was gone

   Action items

   • Make sure GPU driver installations fully succeed, or revert immediately when they fail
   • Move the cluster to a high-availability control plane
   • Expose the Kubernetes API through a load balancer in the HA setup
   • Set up a ticketing system to record incidents and their resolution
   • Write predefined troubleshooting workflows to shorten response time
   • Add infrastructure monitoring such as Prometheus and Grafana
   • Add blackbox HTTP and TCP checks from outside the cluster for critical services

   Lessons

   A driver installation that fails half-way can leave kernel packages broken without any visible effect until the next reboot, which may come weeks later and at the worst time. A failed DKMS build should be fixed or reverted at once. With a single master node and no monitoring from outside the cluster, one unbootable machine meant a total outage that nobody was told about; the fix itself was simple once the boot logs were seen.

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4. IL-0489 Rejoining a failed node of Magic Pages' three-node MySQL cluster broke its metadata and took customer websites down

   Magic Pages2025

   Symptoms

   On 15 January 2025, from about 09:55 to 11:36 CET, a database outage at Magic Pages took down customer websites. It began when the founder tried to reconnect a failed third node to the high-availability MySQL cluster and the database server reported ambiguous errors about missing metadata information.

   Service outage

   Trigger

   One of the three nodes of the MySQL cluster went down, and reconnecting it failed with errors about missing metadata information.

   Root cause

   Dependency failure

   Component

   DatabasePersistent volumes and stateful sets

   Contributing factor

   Harmful manual actionSingle point of failureMonitoring blind spot

   Remediation

   • Restarted the storage server to fix PVC provisioning
   • Let the MySQL nodes restore and sync with each other for about 45 minutes to rebuild the cluster metadata
   • Cleaned up the corrupted cluster metadata
   • Redeployed MySQL Router

   Action items

   • Monitor the health of the storage system more closely
   • Improve the failover procedures for MySQL Router
   • Investigate and fix the status page so it shows reliable information

   Lessons

   The cluster survived the first node failure as designed and lost no data, but its metadata turned out to be a single point of failure. Better monitoring of PVC health could help prevent similar issues, the 45-minute recovery leaves room for improvement, and the status page has to detect failing customer sites reliably.

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5. IL-0005 A new telemetry service overloaded the Kubernetes API servers of large clusters and broke DNS-based service discovery for all OpenAI services

   OpenAI2024

   Symptoms

   Alerts fired at 3:13 PM PST on 11 December 2024, and from 3:16 PM every OpenAI service was significantly degraded or completely unavailable, including ChatGPT, the API and Sora. The status page reported API calls returning errors and difficulties logging in to platform.openai.com and ChatGPT. Customer impact peaked at 3:40 PM.

   Service outageHigh error rateControl plane unresponsiveDNS resolution failures

   Trigger

   At 2:23 PM PST a change introducing a new telemetry service, which collects detailed Kubernetes control plane metrics, was merged and the deployment pipeline started. Between 2:51 PM and 3:20 PM the change was applied to all clusters.

   Root cause

   Configuration change

   Component

   Kubernetes API serverClient-side DNS resolver and cacheTelemetry agent

   Contributing factor

   Unrepresentative stagingNo staged rolloutCache-masked failureMonitoring blind spotLimited debug access

   Detection

   OpenAI's detection tools worked: alerts fired at 3:13 PM PST, a few minutes before customer impact began at 3:16 PM, and the root cause was identified at 3:16 PM.

   Remediation

   • Scaled clusters down to reduce the total Kubernetes API load
   • Blocked network access to the Kubernetes admin APIs so no new expensive requests arrived
   • Scaled up the Kubernetes API servers
   • Removed the telemetry service once enough control was back
   • Moved traffic from impacted clusters to healthy ones
   • Intervened manually in clusters where simultaneous resource downloads saturated resource limits

   Action items

   • Put every infrastructure configuration change through a phased rollout with continuous monitoring of both the service workloads and the clusters, including the Kubernetes control plane
   • Run fault injection tests in which the data plane runs without the control plane for longer, and tests that roll out bad changes on purpose to check detection and rollback
   • Build break-glass mechanisms so engineers can reach the Kubernetes API servers in any circumstances
   • Decouple the Kubernetes data plane from the control plane so that service discovery for critical workloads does not depend on the control plane
   • Speed up recovery with better caching and dynamic rate limiters for resources needed at cluster startup, and regular drills that rapidly replace an entire cluster

   Lessons

   Several systems and processes failed at once and interacted in unexpected ways: tests did not catch the change's effect on the Kubernetes control plane, DNS caching put a delay between the change and the failures, and being locked out of the control plane made remediation very slow. Because service discovery used Kubernetes DNS, the control plane was carrying load for product traffic even though the data plane was meant to be independent of it.

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