Cases5 cases

1 to 5 of 5

1. IL-0102 CoreDNS pods on a production EKS cluster ran out of memory under a burst of new pods, endpoints and nodes, taking down the Buildkite web UI, APIs and job dispatch

   Buildkite2026

   Symptoms

   From 22:47 UTC on 25 August 2026 the Buildkite web interface, REST API, Agent API, job queue and notifications stopped working for every customer. No one could create a build from the UI or the REST API, job dispatch was delayed, and some builds that were already running when the outage began stayed stuck until the services recovered.

   Service outageDNS resolution failuresOOMKilledAutoscaler misbehaviour

   Trigger

   At 22:44 UTC an application deploy created a surge of new pods, which used up the free capacity on the existing nodes and made the EKS cluster scale out. Background worker pools with unusually high maximum replica counts were scaling out at the same time and kept asking for more pods while new nodes were still being provisioned. Together they produced an unusually high rate of change in applications, network endpoints and nodes, and with it a sudden rise in demand on CoreDNS.

   Root cause

   Capacity exhaustion

   Component

   CoreDNSContainer memory limitHorizontal pod autoscalerNode pool

   Contributing factor

   No headroomAutomation amplificationRetry stormMonitoring blind spot

   Detection

   The first status post, at 22:55 UTC, eight minutes after the outage began, said something had gone wrong, and at 23:05 UTC the status page said a sitewide outage was being investigated. CoreDNS query duration had been rising as the cluster grew, but a defect in a key monitoring query and too little monitoring of query volume meant this went unnoticed before the outage.

   Remediation

   • paused further application deploys
   • scaled CoreDNS from three to twelve pods
   • raised each CoreDNS pod's memory limit from 512 MiB to 2 GiB
   • expanded the system node pool that runs CoreDNS

   Action items

   • Increase immediate CoreDNS headroom: twelve pods instead of three, a 2 GiB memory limit instead of 512 MiB, and a larger system node pool
   • Enable EKS-managed CoreDNS autoscaling with a tested minimum replica count and enough capacity to spread the replicas across nodes and availability zones
   • Add direct alerts on CoreDNS query duration, query volume, goroutine growth, memory pressure, OOM restarts and available replicas, and track longer-term trends in capacity planning
   • Find other cluster-critical services that lack tested capacity, direct alerting, spread across failure domains, and either safe autoscaling or documented static headroom, and fix them

   Lessons

   A CoreDNS deployment sized once and left at a fixed size can carry a growing cluster for months and then fail on the first burst of churn it cannot absorb. When query processing slows, pending queries take up memory, the pods hit their limits and restart, and client retries keep the service down after the restart. Server-side DNS query duration and volume need their own alerts: here client-side latency was masked by unrelated application speed-ups, and a broken monitoring query hid the server-side trend. Maximum replica counts raised as a temporary measure remove the cap that normally stops runaway scaling when new nodes are slow to arrive.

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2. IL-0471 GitHub Actions runs failed or sat queued for 9 hours after a routine deployment's pod replacement saturated capacity

   GitHub2026

   Symptoms

   From 15:05 UTC on 6 August 2026 GitHub Actions workflow runs failed to start, failed partway through or stayed queued for a long time, on both GitHub-hosted and self-hosted runners. Some requests to the Actions REST API returned errors, and some customers saw unexpected rate limiting in their workflows.

   High error rateHigh latency

   Trigger

   A routine deployment to an internal Actions service that processes events and generates Actions jobs replaced pods, and the remaining capacity became saturated.

   Root cause

   Capacity exhaustion

   Component

   CI system

   Contributing factor

   No headroomExcessive blast radiusHarmful manual action

   Remediation

   • Expanded capacity of the affected services
   • Throttled incoming webhook-triggered work
   • Increased processing capacity for the backlog of affected events
   • Deployed changes that stopped runners from repeatedly trying to acquire invalid jobs
   • Deployed a fix for self-hosted runners that were not picking up jobs and enabled it incrementally
   • Paused migrations with GitHub Enterprise Importer
   • Restored webhook-triggered Actions workflows to full throughput
   • Rolled back the mitigation that left some ARC runners offline, and recovered those runners by hand
   • Shared CLI and UI steps for customers to clear jobs that could not be retried or canceled

   Action items

   • Improve deployment and capacity safeguards for the affected services
   • Strengthen monitoring for the conditions that preceded the incident
   • Improve the resiliency and recovery of queued work and runner assignment
   • Add automatic recovery for self-hosted runners in upcoming Runner and ARC releases
   • Reduce the risk of cascading failures and speed up recovery during large Actions disruptions

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3. IL-0465 IONOS Container Registry image pushes and pulls failed with 504 Gateway Timeout

   IONOS Cloud2026

   Symptoms

   On 4 March 2026 customers of the IONOS Container Registry got 504 Gateway Timeout errors when pushing or pulling container images, and deployments that relied on the registry were blocked. The status page first reported an increased error count on the registry at 09:23 UTC, saying customers might be unable to pull images.

   Image pull failuresHigh error rate

   Trigger

   A migration combined with a temporary capacity constraint placed two critical control plane components on the same proxy instance, despite anti-affinity rules; the shared proxy then reached its concurrent connection limit.

   Root cause

   Capacity exhaustion

   Component

   Container registry and its credentialsManaged Kubernetes control plane and provider APIPod scheduling and placement

   Contributing factor

   No headroomSingle point of failureNo alert on limit

   Remediation

   • Rolled back a component of the Kubernetes control plane to an earlier version
   • Provisioned additional proxy capacity
   • Moved the affected control plane components onto separate proxy instances

   Action items

   • Redesign registry-to-API connectivity so each node uses its own local proxy
   • Adjust alert thresholds to warn before proxy connection use approaches capacity
   • Deploy more infrastructure clusters and redistribute registries so no cluster exceeds safe capacity, with automation that keeps balancing load

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4. IL-0044 Prometheus OOMKilled in a loop while replaying its write-ahead log after a kube-prometheus-stack upgrade

   Saifeddine Rajhi2024

   Symptoms

   After an upgrade of the kube-prometheus-stack Helm chart, several pods stayed not ready. Among them was prometheus-prometheus-operator-prometheus-0, showing 3/4 Running and a recent termination.

   OOMKilledProbe failuresMonitoring outage

   Trigger

   During the kube-prometheus-stack Helm chart upgrade, Prometheus had to replay its write-ahead log on startup, and the replay needed more memory than its limit allowed.

   Root cause

   Capacity exhaustion

   Component

   PrometheusContainer memory limit

   Contributing factor

   Unknown limitNo headroom

   Detection

   The team noticed pods not ready after the Helm chart upgrade and found the OOMKilled termination with kubectl describe pod.

   Remediation

   • ran promtool tsdb analyze inside the Prometheus pod to find high-cardinality metrics such as haproxy_server_http_responses_total

   Action items

   • Verify that the memory overhead during WAL replay stays within acceptable limits, for example 10 to 15%, and do not run Prometheus close to its memory limit
   • Optimise storage, scraping and remote write configuration and upgrade to newer Prometheus releases
   • Recover automatically from OOM crash loops, for example by deleting the WAL on OOM events
   • Enforce scrape limits so Prometheus stops scraping targets when memory use passes a threshold

   Lessons

   Size Prometheus for its restart, not only for steady state: WAL replay can need far more memory than normal running, and a Prometheus that already runs near its limit can be OOM-killed during the replay. Much of Prometheus memory use comes from cardinality, which promtool tsdb analyze can measure even when Prometheus itself is down.

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5. IL-0070 Critical pods on core nodes evicted when Prometheus began scraping about 200 nodes after a dask-gateway scale-up

   2i2c2023

   Symptoms

   The hub pod of the prod JupyterHub on the leap cluster (leap.2i2c.org) restarted unexpectedly during a large node scale-up, and a user opened a support ticket. The ingress-nginx and proxy pods kept running.

   Pod evictionPods Pending

   Trigger

   A user's dask-gateway workload added about 100 dask-worker nodes within a minute, and Prometheus started scraping the metrics exporters on about 200 nodes.

   Root cause

   Capacity exhaustion

   Component

   PrometheusNode pool

   Contributing factor

   No headroomExcessive blast radiusMonitoring blind spot

   Detection

   A user support ticket (2i2c Freshdesk ticket 414) about the leap hub; the engineer then looked at Grafana and the cluster's nodes and events.

   Action items

   • Configure CPU and memory requests for the hub and proxy pods (#2127; at least 100m CPU was proposed)
   • Make the Grafana OOMKiller dashboard work (#2213)
   • Move LEAP cloud infrastructure to shared nodes (#2209)
   • Update resource requests and limits, mainly for ingress-nginx and Prometheus (#2324)
   • Decide whether Prometheus should scrape dask worker pods (#2366)

   Lessons

   Prometheus load grows with the number of nodes, so a burst of autoscaled nodes can push a nearly full core node over the edge and evict the pods that matter most. Core pods that request 10m of CPU get a tenth of the CPU share of busy neighbours that request 100m. Kubernetes events expire after an hour, so evidence of OOM kills needs a dashboard that keeps it.

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