Cases2 cases

1 to 2 of 2

1. IL-0109 A retried node rollover on a DFDS EKS cluster set the Auto Scaling group back to 15 instances, and the default termination policy removed every healthy worker node, leaving the cluster with no nodes

   DFDS2023sev1

   Symptoms

   At 09:33 UTC on 14 February 2023 end users told the platform team that Kubernetes logs were not arriving in CloudWatch as expected. The investigation went through fluentd and found that the nodes were running containerd as their container runtime instead of docker-shim. A scheduled node rollover the day before, at 07:30 UTC on 13 February, had set containerd as the default runtime by accident.

   Service outageResources deletedMonitoring outage

   Trigger

   A scheduled node rollover had accidentally made containerd the default container runtime, which stopped log shipping to CloudWatch; the unscheduled rollover to revert it timed out, and the pipeline run that followed reduced the Auto Scaling group from 30 to 15 instances.

   Root cause

   Configuration change

   Component

   Container runtimeNode launch templateNode poolTerraformTelemetry agent

   Contributing factor

   Automation amplificationUnreviewed defaultsUnrepresentative stagingNo configuration testsNo runbookHarmful manual action

   Detection

   End users reported the missing CloudWatch logs that started the work. The postmortem lists 2 minutes as the time to detection of the node failure.

   Remediation

   • ran the node rollover script to add new nodes, which failed on a timeout from Docker Hub
   • ran the node rollover script a second time, which added new nodes that the load balancer marked healthy at 11:01 UTC

   Action items

   • Document the rollover process, especially what can be done when it goes wrong
   • Investigate options for improving the rollover process
   • Add fluentd log collection to the QA test suite so logs are covered end to end
   • Add alarms for fluentd when too little or no log data is sent
   • Implement a staging environment
   • Implement guards against selecting an invalid container runtime

   Lessons

   When a change goes wrong, rolling back to a known working configuration is safer than rushing a fix, which can make the damage worse. Launching new worker nodes depends on the cloud-init script not failing in any way. The QA configuration differed from production, there was no staging environment, the QA tests did not cover fluentd log collection, and the rollover process had no documentation for when it fails.

   View case
2. IL-0073 Intermittent Kafka connectivity overloaded Docker daemons, set off a node rescheduling cascade and poisoned the Consul gossip mesh

   Target2019sev1

   Symptoms

   An upgrade of the network subsystem under Target's datacenter OpenStack, meant to cause only a brief disruption, ran into problems and left network connectivity disrupted for several hours. Kafka, which runs on that OpenStack, became intermittently available.

   Node NotReadyLoad imbalanceHigh latencyCredentials unavailable

   Trigger

   A network subsystem upgrade in the datacenter OpenStack infrastructure disrupted connectivity for several hours and made Kafka intermittently available.

   Root cause

   Dependency failure

   Component

   Container runtimeService discoveryTelemetry agentCredential broker for pods

   Contributing factor

   Excessive blast radiusAutomation amplificationSingle point of failureUnpatched known bug

   Remediation

   • enabled gossip encryption on Consul so messages from the poisoned mesh were rejected
   • stabilized the deployment engine and redeployed the development environment with encryption, terminating instances without it
   • upgraded Consul in place to 1.2.3 and then to 1.3.0

   Action items

   • Reduce how much of an outage a single Docker daemon can cause, together with running smaller and more numerous clusters

   Lessons

   Smaller and more numerous clusters limited the damage: the smaller development clusters and production were not hit the same way as the one large cluster, and dev and prod on one cluster would have been far worse. A Docker daemon shared by many workloads is a brittle failure point. Per-workload logging and metrics sidecars were judged the right design; a shared cluster-level agent would almost certainly have made the problem worse and harder to fix.

   View case

By root cause2