Cases17 cases

1 to 5 of 17

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-0350 Hosts in an IONOS TXL cluster lost connectivity when an InfiniBand fabric resweep failed on a full multicast table

   IONOS Cloud2026

   Symptoms

   On 3 March 2026, during a scheduled maintenance to replace a faulty switch in a cluster in IONOS Cloud's TXL data center, monitoring alerts fired and the hosts in that cluster lost network connectivity. Customers saw intermittent network degradation or outages, and the Data Center Designer (DCD) web frontend and the AI Model Hub became unreachable.

   Network unreachableLink flappingService outage

   Trigger

   During a scheduled maintenance to replace a faulty switch, Topology 2 of 2 in the affected cluster was disabled as planned, and a link flap on a switch in Topology 1 then flooded the InfiniBand control plane with alerts and started a fabric resweep.

   Root cause

   Capacity exhaustion

   Component

   InfiniBand fabricNetwork control plane (SDN)BGP routingNIC, cable and switch port linkModel hubDatabase

   Contributing factor

   Automation amplificationExcessive blast radius

   Detection

   IONOS began investigating monitoring alerts in TXL; the first status update was posted at 11:36 UTC.

   Remediation

   • Switched from the affected control plane to its standby
   • Rebooted the locked-up gateways
   • Recovered Topology 1 from the scheduled maintenance
   • Paused provisioning in the cluster that had a backlog of queued jobs
   • Re-enabled provisioning once network connectivity improved and watched job execution
   • Re-established connectivity for the systems that support the DCD

   Action items

   • Adjusted the control-plane configuration to use less aggressive failover timers and reduced heavy sweep activity, lowering the load on the forwarding table
   • Reduced the number of multicast group memberships to relieve the forwarding table
   • Set up continuous monitoring of forwarding table use and control-plane memory with alerts before capacity limits are reached
   • Move the control plane to dedicated high-performance servers, separate from data-plane traffic
   • Audit IPv6 configurations and IPoIB driver settings
   • Modernise the interconnect fabric, including switches, gateways and drivers
   • Hardened IAM maintenance protocols so that critical identity services keep working when the primary Cloud APIs are temporarily unavailable
   • Move IAM to a dedicated platform without a dependency on the standard managed Kubernetes clusters
   • Introduce a dedicated provisioning provider for DBaaS and Managed Kubernetes
   • Add automated circuit breakers to the provisioning engine against overload and queue buildup from self-healing services
   • Improve visibility and control over job execution and prioritisation in the provisioning queue
   • Change the provisioning queue architecture to decouple services and reduce spillover

   Lessons

   IONOS identified the linking of services through the shared provisioning queue as a key reason the degradation lasted as long as it did. Alongside making each service more robust, it is decoupling the provisioning queue and addressing dependencies between services to reduce the blast radius of future incidents.

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3. IL-0067 Redis filled its persistent volume, corrupted its append-only file and crash-looped on every restart

   BC Government2025sev1

   Symptoms

   The Klamm production application was completely unavailable for about 30 minutes outside working hours. Klamm's queue workers timed out because they could not reach the Redis cache, and the services that depend on it were badly affected.

   Service outageContainer crash loopPersistent volume full

   Trigger

   A large, unexpected spike in I/O from a Klamm Laravel worker sharply increased requests to the Redis cache.

   Root cause

   Capacity exhaustion

   Component

   Persistent volumes and stateful setsIn-memory cache

   Contributing factor

   No capacity planning

   Remediation

   • backed off the Redis StatefulSet and the deployment
   • deleted the corrupted PVC and rebuilt it
   • restarted the StatefulSet and the deployment

   Action items

   • Increase the Klamm Redis cache volume to 100Mi
   • Integrate Sysdig monitoring on the namespace
   • Enable replica pods on the Redis cluster

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4. IL-0080 kube-dns on Mercari's GKE clusters could not keep up with peak-hour DNS query rates, and a Sev1 production outage followed when several services failed DNS resolution

   Mercari2025sev1

   Symptoms

   During peak hours, when Mercari's GKE clusters handle very high request rates, the platform team saw kube-dns reaching its limits. DNS query rates were high enough to overwhelm the kube-dns service.

   DNS resolution failuresIntermittent timeoutsService outage

   Trigger

   Peak-hour traffic on the GKE clusters produced more DNS queries than the few kube-dns pods could answer.

   Root cause

   Capacity exhaustion

   Component

   kube-dns

   Contributing factor

   No cachingSingle point of failure

   Detection

   During peak hours the team saw frequent NXDOMAIN and truncated-response errors and recurring "failed to refresh DNS cache" errors on the clusters, which showed that kube-dns had reached its limits.

   Remediation

   • deployed NodeLocal DNSCache on the Laboratory GKE cluster with a specific nodeAffinity to measure its effect
   • rolled NodeLocal DNSCache out gradually to the Dev and Prod environments by labelling node pools so its pods could be deployed there

   Lessons

   When every pod in a busy cluster resolves names through a few central kube-dns pods, DNS becomes a shared bottleneck, and a DNS failure quickly turns into higher latency, timeouts and cascading outages across microservices. A cache on each node takes most of that load off kube-dns and keeps resolution local to the node. Rolling the cache out first to an internal test cluster with nodeAffinity, and then node pool by node pool through labels, kept the blast radius of the change small.

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5. IL-0082 Losing one CoreDNS pod during a planned EKS node reroll overloaded the remaining CoreDNS pods, causing DNS timeouts and errors across multiple services at RebelMouse

   RebelMouse2025

   Symptoms

   At 13:42 UTC on 19 May 2025, RebelMouse's monitoring tools reported a higher error rate and degraded performance across services. A routine reroll of the EKS nodes had started 27 minutes earlier, at 13:15 UTC.

   DNS resolution failuresHigh latencyHigh error rateService outage

   Trigger

   At 13:15 UTC RebelMouse started its regular reroll of EKS nodes to apply security patches. The planned, automatic termination of an EC2 instance terminated one of the CoreDNS pods.

   Root cause

   Capacity exhaustion

   Component

   CoreDNSNode pool

   Contributing factor

   Change during peak hours

   Detection

   Monitoring tools detected an increased error rate and degraded service performance at 13:42 UTC, and the tech team started investigating at 13:44 UTC.

   Remediation

   • rescheduled the affected CoreDNS pod onto a new EC2 instance
   • scaled up the CoreDNS deployment to more replicas so DNS load was spread evenly across all pods

   Action items

   • Add scale-in protection so CoreDNS pods are not terminated unexpectedly during peak load
   • Review the CoreDNS configuration so that vertical scaling of the pods works
   • Hold regular capacity reviews so the CoreDNS deployment is provisioned for peak load

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