Cases6 cases

1 to 5 of 6

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-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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3. IL-0153 DOKS worker nodes went NotReady at 06:00 UTC on three days because a systemd timer in the Debian image ran apt upgrade and killed it mid kernel upgrade, breaking node networking

   DigitalOcean2023

   Symptoms

   Customers of DigitalOcean Managed Kubernetes (DOKS) saw worker nodes go NotReady between 06:00 and 07:00 UTC and stay unavailable, taking down the services on them, until the nodes were replaced or rebooted; some nodes needed several replacements or reboots. The first sporadic reports on October 8 said replacing the node fixed it. The same thing happened again around 06:00 UTC on October 9, more widely and across many regions, and again on October 10. Only clusters on versions 1.27.6-do.0 and 1.28.2-do.0 were affected.

   Node NotReadyService outage

   Trigger

   A systemd timer in the DigitalOcean Debian image started apt upgrade daily at 06:00 UTC, with a random delay of up to 60 minutes.

   Root cause

   Configuration change

   Component

   Node operating system imageLinux kernelkubelet

   Contributing factor

   Unreviewed defaultsLimited debug accessChange with a delayed effect

   Detection

   Sporadic customer reports of failed worker nodes arrived at 07:43 UTC on October 8; internal incident response started at 07:10 UTC on October 9 after broader impact across regions.

   Remediation

   • Advised customers to replace or reboot affected worker nodes
   • Arranged SSH and console access to worker nodes while they were healthy, and live-debugged an affected node on October 10
   • Released a DaemonSet to all affected clusters that disables the apt upgrade systemd timer on every worker node
   • Released new node images without the auto-update process

   Action items

   • Disable the timer during node provisioning, then remove the DaemonSet from all clusters
   • Add a check to the internal conformance test suite for new cluster versions that the auto-upgrade process and other unwanted timers stay disabled
   • Support logging into worker nodes even when normal access paths are broken

   Lessons

   An operating system auto-update timer left enabled in a node image can change the kernel and systemd underneath Kubernetes, and a timeout that kills the upgrade half-way can leave a node without networking. Failures that start at the same time every day point to a scheduled job on the nodes, and systemd timers belong on that list next to cron jobs. When the failure takes out a node's network, an investigation that depends on logging into the node stalls; access paths that work while the node is broken shorten diagnosis.

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4. IL-0501 Every node of the UToronto Kubernetes cluster on Azure went NotReady and the JupyterHub became unreachable

   2i2c2022

   Symptoms

   On 30 August 2022 at 11:15 AM Pacific time, the UToronto JupyterHub at jupyter.utoronto.ca stopped responding and users could not reach it at all. The outage was reported through 2i2c's support desk.

   Service outageNode NotReady

   Trigger

   Microsoft Azure rolled out an Ubuntu update to all Ubuntu 18.04 nodes, which triggered a systemd bug that broke networking.

   Root cause

   Breaking change in an upgrade

   Component

   Node operating system image

   Contributing factor

   Limited debug access

   Detection

   The outage was reported at 11:15 AM through 2i2c's Freshdesk support desk because jupyter.utoronto.ca was not responding. At 11:25 AM the customer's representative forwarded the Microsoft advisory on the same ticket, which pointed the team to the cause.

   Remediation

   • Started an upgrade of the cluster's Kubernetes and node versions, which replaced all old nodes with new working ones

   Action items

   • Document how to SSH into the Kubernetes nodes
   • Find a way to be notified of Azure advisories

   Lessons

   Once the cause was known, a single action brought the hub back quickly. The team had no documented way to SSH into its Kubernetes nodes, and the customer's representative saw the Azure advisory and pointed it out on the support ticket, which saved a lot of time.

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5. IL-0113 The managed GKE control plane of Webhook Relay's main cluster failed because its API server certificates had never been rotated, and a manual credential rotation reported success without taking effect

   Webhook Relay2022

   Symptoms

   At 12:00 EST the health check system reported that the backend APIs, the frontend and the tunnels in the EU region were down. The team first took it for a transient error, such as an interrupted client request or a server evicted for memory or CPU pressure, but the main dashboard was offline as well.

   Service outageControl plane lagControl plane unresponsiveStale or inconsistent API readsCredentials unavailable

   Root cause

   Certificate or credential expiry

   Component

   GKE control planeKubernetes API serverNode poolRBAC and service accounts

   Contributing factor

   Limited debug accessSingle point of failure

   Detection

   The health check system sent a notification that the service was down; the team then found the main dashboard offline.

   Remediation

   • recreated the node pools by hand after the credential rotation failed to recreate them
   • duplicated the main cluster's services into a newly created GKE cluster
   • deleted the Kubernetes Service records in the old cluster so its load balancers could be attached to the new cluster
   • detached the StatefulSet from the old cluster and switched all writes to the main disk
   • exported the data from the temporary ClickHouse instance and inserted it into the main instance

   Action items

   • Change the deployment manifests so services can be switched between clusters quickly without a complicated persistent disk migration

   Lessons

   Recovery of existing infrastructure needs a time limit. Throughout the outage the old cluster looked about to recover, and the reluctance to give up on the salvage effort kept the team from starting over with a new cluster much sooner, which would have been the right call. On older GKE clusters, check the validity of the cluster CA certificate, for example by decoding masterAuth.clusterCaCertificate from gcloud container clusters describe and reading it with openssl x509. GKE's multiple availability zones made no difference in this failure.

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