Cases3 cases

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1. IL-0548 Buildkite's EKS Prometheus server was killed for memory again and again, so services stopped scaling and jobs lagged

   Buildkite2026

   Symptoms

   On 20 May 2026 a subset of Buildkite customers saw delays in notification delivery, build dispatch and the metrics services. The status page first reported delays to notifications for all customers and later narrowed it to a subset of customers.

   High latencyOOMKilledMonitoring outageAutoscaler misbehaviour

   Trigger

   Autoscaling during Buildkite's migration of production services from AWS Fargate to AWS EKS raised the number of pods that Prometheus tracked, and with it the memory the Prometheus server needed.

   Root cause

   Capacity exhaustion

   Component

   PrometheusContainer memory limitNode pool

   Contributing factor

   No resource requests or limits

   Remediation

   • Moved the load of the affected services back to AWS Fargate by following prepared documentation
   • Scaled up the services of a few customers with very large backlogs further by hand

   Action items

   • Upsize the underlying system nodes
   • Set higher requests and limits for the Prometheus server so it can handle more load
   • Review all new EKS resources and set any missing requests and limits
   • Add observability and monitors for EKS pod and node health

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2. IL-0466 Prow scheduled test job pods onto its service cluster, leaving hundreds Pending and starving Prow's own services

   Kubernetes project2019

   Symptoms

   On 8 February 2019 the Kubernetes project's Prow instance at prow.k8s.io became very slow: its controllers took about five minutes to respond to the hook, bot commands such as the cat command stopped working, and the prow.k8s.io site was hard to reach. GitHub status was normal, and the Prow service nodes were found running above 100% of CPU and memory.

   Pods PendingSustained high node CPUHigh latency

   Trigger

   At 13:20 PST an auto-generated bump PR for Prow was merged and deployed, and Prow started scheduling workloads onto the service cluster.

   Root cause

   Faulty deployment

   Component

   CI system

   Contributing factor

   Unrepresentative stagingNo resource requests or limitsLimited debug access

   Detection

   While working on a PR, a maintainer noticed the bot's cat command failing, found GitHub status normal, learned on Slack that the Prow nodes were above 100% CPU and memory, and then found 600 pending pods in the test-pods namespace.

   Remediation

   • Rolled Prow back to v20190206-b01b288 and cleaned up the pods and ProwJobs in the service cluster
   • Applied the revert by hand with kubectl apply after the deployment permission was missing
   • Sent a fix, PR 11209, so the client no longer overwrites a provided literal default client
   • Rolled Prow back to v20190125-2aca69d, from before the bug was introduced
   • Cleaned up the wrongly scheduled pods and ProwJobs again and confirmed Prow was healthy

   Action items

   • Find out why the bug did not show deterministically
   • Be more careful with sweeping Prow changes, with more end-to-end tests or a real staging Prow

   Lessons

   The behaviour was not deterministic, and the change was deployed on a Friday afternoon. Prow instances are set up differently, for example in how build clusters are defined, so another project's Prow running a version fine does not show it is safe for the Kubernetes Prow, which needs a staging environment that looks like it.

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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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