Cases22 cases

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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-0155 Some DOKS clusters in NYC1 saw intermittent DNS timeouts and NodeNotReady events because CoreDNS ran on shared-CPU Droplet worker nodes

   DigitalOcean2026

   Symptoms

   From 20:31 UTC on July 9, 2026, DigitalOcean reported a problem with Kubernetes deployments in its NYC1 region. Application workloads could see intermittent DNS failures and NodeNotReady events.

   DNS resolution failuresIntermittent timeoutsNode NotReady

   Root cause

   Capacity exhaustion

   Component

   CoreDNSNode pool

   Contributing factor

   Unknown limit

   Lessons

   Cluster DNS is sensitive to latency, and CoreDNS on shared-CPU nodes can time out intermittently. DigitalOcean's advice is to place CoreDNS on dedicated-CPU node pools and to run enough CoreDNS replicas.

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3. IL-0150 A latent race condition in DynamoDB's DNS automation left an empty DNS record for dynamodb.us-east-1.amazonaws.com, and the DynamoDB and EC2 launch failures that followed caused EKS, ECS and Fargate container launch failures and cluster scaling delays in us-east-1

   Amazon Web Services2025

   Symptoms

   At 11:48 PM PDT on 19 October 2025, every system that reached DynamoDB in us-east-1 through the public regional endpoint started getting DNS failures and could not connect. This included customer traffic and internal AWS services that depend on DynamoDB. DynamoDB global table replicas in other regions still answered but fell behind in replication to and from us-east-1.

   DNS resolution failuresHigh error rateScale-up failureIntermittent connection failuresHigh latency

   Trigger

   One DNS Enactor, slowed by unusually high retry delays, applied a much older DNS plan to the regional DynamoDB endpoint just as another Enactor that had applied the newest plan ran its clean-up, which deleted that older plan and with it every IP address of the endpoint.

   Root cause

   Software defect

   Component

   Authoritative DNS servers and recordsApplication databaseCloud load balancer

   Contributing factor

   Automation amplificationRetry stormNo runbook

   Detection

   DNS failures to DynamoDB began immediately at 11:48 PM PDT and the engineering teams of affected AWS services were engaged at once; by 12:38 AM they had identified DynamoDB's DNS state as the source. The later NLB health check problem was detected by monitoring at 6:52 AM.

   Remediation

   • applied temporary mitigations by 1:15 AM that let some internal services reach DynamoDB and repaired key internal tooling
   • restored all DynamoDB DNS information by 2:25 AM through manual operator intervention
   • throttled incoming work and selectively restarted DWFM hosts from 4:14 AM so droplet leases could be re-established
   • reduced the load on Network Manager to bring network configuration propagation times back to normal by 10:36 AM
   • disabled automatic health check failover for NLB at 9:36 AM, and re-enabled it at 2:09 PM after EC2 recovered
   • relaxed the EC2 request throttles from 11:23 AM until all EC2 APIs and launches were normal at 1:50 PM

   Action items

   • Disable the DynamoDB DNS Planner and DNS Enactor automation worldwide
   • Fix the race condition and add protections against applying incorrect DNS plans before re-enabling the DNS automation
   • Add a velocity control to NLB that limits how much capacity a single load balancer can remove when health check failures cause availability zone failover
   • Build an additional EC2 test suite that exercises the DWFM recovery workflow at scale
   • Improve throttling in EC2 data propagation systems so incoming work is rate limited by the size of the waiting queue

   Lessons

   A single DNS record that automation can empty is enough to take a widely used regional endpoint off the network, and every service built on it fails in turn. A check made once at the start of a long operation can be stale by the time the operation writes its result. Recovery of the dependency is not recovery of the dependents: systems that lost state during the outage, such as EC2's droplet leases and network propagation backlog, can collapse under their own catch-up work, and container services that rely on new instance launches keep failing to launch and scale until that work is done.

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4. IL-0101 After Google forced a GKE control plane upgrade to 1.33.2 at a deprecation deadline, no new node could initialise, so autoscaling and spot node replacement stopped for 15 hours

   Wealthy2025sev1

   Symptoms

   Nodes that already existed kept running, but every new node, spot or standard, on either v1.32 or v1.33, failed to initialise and could not join the cluster. Spot nodes that were preempted and recreated never came back, so services degraded progressively: background workers on spot instances, reporting services and order allocation stopped completely, and the APIs on existing nodes kept answering but could not scale for load spikes.

   Scale-up failureContainer crash loopService outageDNS resolution failures

   Trigger

   Google upgraded the GKE control plane from v1.32.x to v1.33.2 at the version deprecation deadline, although auto-upgrade was disabled on the node pools.

   Root cause

   Breaking change in an upgrade

   Component

   GKE control planeNode poolClient-side DNS resolver and cachePersistent volumes and stateful setsConfigMaps and Secrets

   Contributing factor

   Deferred upgradesSingle point of failureIneffective rollbackNo headroom

   Remediation

   • Created a new GKE cluster at v1.33, the version of the forced upgrade
   • Upgraded Istio to 1.27.1 for Kubernetes 1.33 compatibility
   • Updated the cert-manager configuration for Kubernetes 1.33
   • Migrated all production workloads to the new cluster and verified pod scheduling, DNS resolution, PVC mounting and external integrations
   • Decommissioned the broken cluster

   Action items

   • Configure all clusters to use the GKE stable release channel
   • Implement a separate management cluster for Argo CD, monitoring and other control components (Q4 2025)
   • Review spot instance usage for critical workloads and add on-demand fallback pools
   • Document the SRE upgrade procedure: management cluster first, production after a monitoring period of several days
   • Document a Kubernetes, Istio and cert-manager compatibility matrix
   • Create an automated upgrade testing pipeline
   • Reserve static IPs for quick migrations
   • Pin versions of Istio, cert-manager, CSI and DNS addons
   • Stay current with stable releases and regularly check GKE alerts and announcements to avoid deprecation deadlines

   Lessons

   GKE upgrades the control plane at a version's deprecation deadline even when auto-upgrade is disabled, so staying inside the supported version window is how to avoid a forced upgrade. A control plane upgrade can leave existing nodes healthy while every new node fails, and spot nodes that cannot rejoin after preemption turn this into cascading failures. Relying heavily on spot instances for critical workers turned a node join failure into a progressive outage. Kubernetes upgrades have to be planned together with dependent components such as Istio and cert-manager, and without a standby cluster on the same provider there was no immediate failover; the only recovery left was building a new cluster.

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5. IL-0104 After a CoreDNS pod was OOMKilled, the talk names three causes: Istio's aggressive DNS cache refresh on one reused client port, kube-proxy not cleaning up conntrack entries of terminated pods, and the 2-minute UDP conntrack timeout

   Cisco ThousandEyes2025

   Symptoms

   On 4 September 2025 a CoreDNS pod in a Cisco ThousandEyes Kubernetes cluster was OOMKilled.

   DNS resolution failuresStale endpoint routingOOMKilled

   Trigger

   A CoreDNS pod was OOMKilled.

   Root cause

   Software defect

   Component

   CoreDNSkube-proxyconntrack tableIstio

   Contributing factor

   Unreviewed defaults

   Lessons

   The closing slide sets three behaviours side by side: Istio's extremely aggressive DNS cache refresh with one client port reused for all DNS requests, kube-proxy's lack of a cleanup mechanism for stale conntrack entries of terminated pods (fixed in Kubernetes v1.32), and conntrack's default bidirectional UDP stream timeout of 2 minutes. The speaker describes Istio as making some very complex things simple but some very simple things complex, and presents Cilium, which looks up Services in maps instead of walking the KUBE-SERVICES chain and can replace kube-proxy entirely with its eBPF data path.

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