Cases11 cases

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1. IL-0027 All CoreDNS pods landed on one node, the node ran out of memory, and DNS for the whole service went down

   Backpackr2025

   Symptoms

   Services on the production Amazon EKS cluster behind the idus marketplace threw intermittent errors whose cause was hard to pin down. Calls to external services such as RDS and Redis failed now and then, and whenever DNS traffic spiked, domain lookups answered slowly or failed outright.

   DNS resolution failuresIntermittent connection failuresHigh latencyOOMKilledService outage

   Trigger

   All CoreDNS pods were scheduled onto the same node, and that node's memory usage spiked until it ran out of memory.

   Root cause

   Capacity exhaustion

   Component

   CoreDNSClient-side DNS resolver and cachekubeletContainer memory limitNode pool

   Contributing factor

   Unreviewed defaultsSingle point of failureNo caching

   Detection

   The write-up does not say how the full outage was noticed. The earlier DNS trouble showed up as intermittent service errors, and investigating failed calls to RDS and Redis led to DNS resolution failures.

   Remediation

   • deployed NodeLocal DNSCache as a DaemonSet in front of CoreDNS
   • added a PodDisruptionBudget (maxUnavailable 1) and topologySpreadConstraints across zones for CoreDNS
   • lowered ndots from 5 to 2 with dnsConfig in the Deployments

   Lessons

   The CoreDNS setup that EKS creates by default (two pods) can be enough for a small or development cluster but becomes a bottleneck in a large production cluster, and DNS is a single point of failure that needs high availability and room to scale. Tuning the CoreDNS cache and buffer size only relieved the symptoms; the real load came from the extra queries that ndots:5 generates for external names. A CoreDNS pod without a memory limit can share a node's fate with everything else on it.

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2. IL-0182 Too few CoreDNS replicas on EKS were overloaded during traffic spikes, raising latency and cutting the network for task pods, and the over-provisioned, tightly spread CoreDNS pods added later blocked Karpenter consolidation

   이언철2024

   Symptoms

   When traffic changed sharply, CoreDNS came under heavy load, and the cluster saw network problems and a sudden rise in latency. The horizontal pod autoscaler then added pods, database connections surged, and the impact spread to the whole service.

   High latencyIntermittent connection failuresAutoscaler misbehaviour

   Trigger

   Sharp changes in traffic loaded the small number of CoreDNS pods beyond what they could handle.

   Root cause

   Capacity exhaustion

   Component

   CoreDNSKarpenterHorizontal pod autoscalerPod scheduling and placement

   Contributing factor

   Automation amplificationHarmful manual action

   Remediation

   • Set up the Cluster Proportional Autoscaler for CoreDNS with nodesPerReplica and coresPerReplica chosen for the instance types in use
   • Spread CoreDNS with soft topologySpreadConstraints (ScheduleAnyway) instead of anti-affinity so Karpenter could consolidate nodes
   • Added alert thresholds on CoreDNS error rate, panics, request duration and request rate
   • Deployed NodeLocal DNSCache so each node answers DNS from a local cache and forwards misses to the main CoreDNS

   Lessons

   A fixed, small number of CoreDNS pods cannot follow a cluster whose nodes and pods change quickly with traffic, and the damage spreads: DNS latency triggers autoscaling, which in turn floods the database with connections. Simply adding many CoreDNS replicas created a different problem, because with tainted nodes and a maxSkew: 1 spread constraint the pods piled onto a few nodes and blocked Karpenter consolidation. Scaling CoreDNS in proportion to the cluster and putting a DNS cache on every node handled both.

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3. IL-0202 A burst of queries and database writes exhausted connection pools and memory on Honeycomb's ingest hosts, and Kubernetes crash-loop back-off kept failed hosts offline so ingest capacity stayed too low to recover

   Honeycomb2023sev1

   Symptoms

   Standard alerting paged Honeycomb's on-call engineers about the performance and stability of event ingestion, and the automated load-shedding mechanisms tripped and sent further notifications. The status page described a higher than normal error rate and response time on the ingestion service.

   High error rateHigh latencyContainer crash loopService outageDatabase overload

   Trigger

   An abnormally large number of queries arrived from many separate connections within a few minutes.

   Root cause

   Capacity exhaustion

   Component

   Application databaseIn-memory cache

   Contributing factor

   Retry stormAutomation amplification

   Detection

   Standard alerting notified the on-call engineers about ingestion performance and stability, and the automated load-shedding mechanisms added notifications when they tripped. The first status post, about alerts on a higher than normal error rate and response time, went up at 18:28 PST (02:28 UTC), 13 minutes after the 02:15 UTC start.

   Remediation

   • Scaled the ingest fleet up manually and aggressively
   • Cut off most ingest for a few minutes so the ingest hosts could come back with enough capacity
   • Temporarily lowered the rate limits for the most aggressive traffic sources until their traffic subsided

   Action items

   • Keep the ingest fleet scaled up to prevent a repeat while longer-term fixes are worked on
   • Decide on the projects that will address these failure paths

   Lessons

   Load shedding and auto-scaling were in place, yet a burst that arrived faster than the cache could spread its writes still took the ingest hosts down. During recovery, Kubernetes' crash loop back-off worked against the operators: the hosts that had failed stayed offline while the fleet needed every host. Clients that retry whole OTel batches after a partial drop can multiply traffic many times over, so per-source rate limits were what finally stabilised ingest.

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4. IL-0028 Temporal History service latency rose because its pods were CPU-throttled while CPU usage read below 60 percent

   Temporal2023

   Symptoms

   Request latency from the Temporal Server History service, the service that starts workflow executions and records their progress, was higher and less steady than it should be on a self-hosted Kubernetes deployment. The CPU dashboard showed the history pods using less than 60 percent of their CPU, yet the same pods were being throttled.

   CPU throttlingHigh latency

   Trigger

   Normal load on History pods that had a CPU limit of 2 cores on 8-core nodes, with the Go runtime's GOMAXPROCS left at its default of one per node core.

   Root cause

   Capacity exhaustion

   Component

   Container CPU limitLanguage runtime settingsPrometheus

   Contributing factor

   Unreviewed defaultsMonitoring blind spot

   Detection

   A Prometheus query for CPU throttling showed the history pods throttled although their CPU usage read under 60 percent.

   Remediation

   • set the GOMAXPROCS environment variable to 2 to match the 2-core CPU limit

   Action items

   • Set GOMAXPROCS automatically from the Kubernetes CPU limit, from Temporal release 1.21.0

   Lessons

   A CPU usage graph averaged over 15 seconds or more can sit far below 100 percent while a container is being throttled, so throttling needs its own metric. A Go program in a container should have GOMAXPROCS matched to its CPU limit, or to its CPU request when it has no limit. The author recommends not using CPU limits at all.

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5. IL-0140 The GKE API in us-central1 returned errors and high latency for 3 hours 19 minutes after an unexpected rise in backend traffic that client retries made worse

   Google Cloud2022

   Symptoms

   From 03:03 US/Pacific on 14 June 2022, calls to the GKE API in us-central1 through gcloud and the Google Cloud Console were slow and returned errors. Retrying sometimes succeeded.

   High error rateHigh latencyControl plane lag

   Trigger

   An unexpected increase in backend traffic to the GKE API in us-central1.

   Root cause

   Capacity exhaustion

   Component

   GKE control plane

   Contributing factor

   Retry storm

   Remediation

   • scaled up the GKE control plane to handle the increased traffic

   Lessons

   Client retries against an overloaded management API add to the overload. When the cloud provider's control plane is degraded, running workloads may be fine while cluster operations fail, and the status page offered no workaround beyond advising customers to use another region if they could.

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