Cases3 cases

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1. 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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2. IL-0231 Deleting EKS managed node groups also deleted Karpenter's instance profile, and every Stytch worker node shut down

   Stytch2023

   Symptoms

   On 23 February 2023 Stytch's EKS worker nodes went Ready:Unknown one after another, Karpenter failed to launch replacement nodes, and with fewer and fewer pods scheduled the Live Stytch API went down for 14 minutes, returning 5XX on every call.

   Node NotReadyScale-up failurePod evictionPods PendingService outage

   Trigger

   At 14:21 UTC Stytch landed a change to remove unused EKS managed node groups, because it had decided to run its data plane applications fully on nodes provisioned by Karpenter. Because Crossplane resources use an orphan deletion policy, engineers deleted the node groups in the AWS console, first in staging and then in production.

   Root cause

   Configuration change

   Component

   KarpenterNode poolConfiguration templating

   Contributing factor

   Unverified assumptionShared credentialChange with a delayed effectIgnored early signal

   Detection

   Low-urgency alerts that Karpenter could not find the instance profile fired in staging at 16:06 UTC and in production at 17:48 UTC but were not treated as an incident. The incident process started at 20:26 UTC when node launch failures spiked the Karpenter error rate, and Kubernetes monitoring alerts in production followed at 20:44 UTC. The support team flagged the outage at 21:14 UTC.

   Remediation

   • Reverted the node group deletion, which recreated the managed node groups
   • Manually scaled up one of the Live API node groups with more and much larger nodes and removed its taints
   • Removed node selectors from the applications one after another so their pods could be scheduled

   Action items

   • Treat Karpenter errors as high-urgency alerts that page the on-call engineer
   • Tune cluster alerting thresholds to send a high-urgency page earlier
   • Add alerts for nodes that stay NotReady or Unknown for long periods
   • Raise the thresholds of other deployment alerts, such as unschedulable pods and replicas not available
   • Give Karpenter its own IAM instance profile, separate from the one used by the node groups
   • Separate other AWS resources that were shared between uses, specifically IAM and security groups
   • Rework the EKS and Karpenter configuration so system instance profiles are unique, static per cluster and generated by Karpenter
   • Introduce a cloud visualization tool for engineers making infrastructure changes
   • Add live system and product metrics to the status page
   • Ask AWS to confirm the undocumented actions and side effects of deleting a node group
   • Correct the shared use of IAM roles and security groups across the infrastructure

   Lessons

   The IAM role for Karpenter's self-managed nodes should never have been the same as the one for the managed node groups; sharing it is what let the node group deletion break Karpenter, and the roles had been left combined for speed and simplicity when Karpenter was first set up. The hours-long delay between the configuration change and its effect made the incident hard to assess, and AWS confirmed that this delay is expected, so Stytch will allow for it in future IAM instance profile changes.

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3. IL-0035 Worker nodes went NotReady after a new ElastAlert rule without memory limits used up node memory

   NU.nl2019

   Symptoms

   The NU.nl development team got reports that several services on one of its Kubernetes clusters behaved erratically: occasional error pages, slow responses and timeouts. The affected services were mostly internal tools and dashboards.

   Node NotReadyHigh error rateHigh latencyIntermittent timeoutsMonitoring outageOOMKilled

   Trigger

   A new ElastAlert rule, deployed shortly before from the command line, that fired if the Cloudfront-* Elasticsearch indexes stopped receiving documents; those indexes take a few million requests per hour, and the rule made ElastAlert's memory grow without bound.

   Root cause

   Configuration change

   Component

   Container memory limitkubeletPrometheus

   Contributing factor

   No resource requests or limitsUnreviewed defaultsNo alert on limitMonitoring blind spotExcessive blast radius

   Detection

   Users reported error pages, slow responses and timeouts from several services; no alert fired.

   Remediation

   • terminated the NotReady nodes one by one so the auto-scaling group replaced them (the new nodes also went NotReady, but Prometheus and Grafana landed on a stable node)
   • found the ElastAlert pods' memory growth on the Grafana pod memory dashboard
   • fixed the ElastAlert problem that caused the memory growth

   Action items

   • Define alerts on resource usage such as CPU, memory and disk space
   • Keep researching an alerting strategy that combines Elasticsearch, Prometheus Alertmanager and New Relic
   • Consider exporting metrics out of the cluster and running Grafana outside it, on EC2 or a separate Kubernetes cluster
   • Either visualise the important metrics in the ELK stack or make Prometheus and Grafana more available, and improve the metrics strategy
   • Set resource limits for ElastAlert through the Helm values
   • Configure namespaces with default memory requests and limits
   • Isolate customer-facing workloads from build, logging and monitoring workloads (a dedicated node group, or later a separate tools cluster)
   • Deploy everything through automation such as Jenkins pipelines, and consider a GitOps approach
   • Deploy through a pipeline that includes a smoke test step
   • Make Kubernetes and cloud infrastructure work part of the team's sprints, picked up by all team members while pairing with more experienced members, and hold workshops that go deep into specific topics

   Lessons

   Fixing the root cause covers only a small part of what an incident can teach; the chain of smaller failures around it is where most of the learning is. A pod without a memory limit can take whole nodes down, so limits need defaults at the namespace level. Monitoring that runs inside the cluster it watches goes blind with it. Changes installed by hand from the command line leave the rest of the team without a record of what changed.

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