Cases3 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-0461 Every rollout of Datadog's metrics query service on Kubernetes raised errors that the logs blamed on DNS

   Datadog2022

   Symptoms

   From September 2021, each rollout of an update to Datadog's metrics query service, which serves real-time and historical metric data to the web frontend and to monitor evaluation, came with a rise in errors. Clients retried failed queries, so few errors reached users, but the retries slowed dashboards and monitors. The service's logs showed DNS errors stopping it from connecting to the data stores it depends on.

   High error rateDNS resolution failuresHigh latencyOOMKilledPacket lossStale endpoint routing

   Trigger

   Rollouts of the metrics query service, which delete the old pods. The client behaviour behind the failures came from an August 2021 rollout of the metrics query clients that applied round_robin as the new default gRPC load balancing policy.

   Root cause

   Configuration change

   Component

   conntrack tableCiliumLinux kernelCoreDNSClient-side DNS resolver and cacheexternal-dnsHTTP and RPC client library

   Contributing factor

   Change with a delayed effectRetry stormUnknown limitMonitoring blind spot

   Detection

   Errors rose during rollouts of the metrics query service, and the service's logs showed DNS errors when connecting to its data stores.

   Remediation

   • Raised the memory of the node-local-dns pods from 64 MB to 256 MB, which stopped the OOM kills but not the DNS errors
   • Tested larger instance types and confirmed that scaling up the service's infrastructure resolved the errors
   • Removed the custom gRPC reconnect parameters from the clients to restore the defaults

   Action items

   • Contribute a change to Cilium that adds an unreachable route for a deleted pod's IP so clients get an ICMP error

   Lessons

   The errors looked like DNS but were not. ENA metrics and VPC Flow Logs gave the visibility into VPC connection tracking that host metrics did not, and Cilium, Kubernetes and AWS networking interact in ways that can make reverse path filtering drop packets it should allow. DNS propagation time depends on many settings. The causes were changes Datadog had made itself, to work around a dependency bug, to resolve earlier incidents and to override default reconnect parameters, which looked safe at the time and showed their effects much later, when the link was hard to see.

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3. IL-0001 Cluster-wide DNS outage after CoreDNS pods were OOM-killed again and again under a burst of DNS queries

   Zalando2019

   Symptoms

   Customers of the Zalando Fashion Store got a high rate of errors on all web product pages and outfit pages for more than an hour. The errors came from the main data aggregation layer service, which runs in one of Zalando's Kubernetes clusters.

   High error rateDNS resolution failuresOOMKilledMonitoring outage

   Trigger

   Calls from the aggregation layer to a single, not very important downstream service started timing out. The aggregation layer returned 404s for them, clients retried, and the resulting spike in requests became a spike in DNS queries to CoreDNS.

   Root cause

   Capacity exhaustion

   Component

   CoreDNSContainer memory limitClient-side DNS resolver and cache

   Contributing factor

   Unknown limitNo cachingRetry stormUnreviewed defaultsExcessive blast radiusMonitoring blind spot

   Detection

   The on-call engineer for the aggregation layer phoned the on-call Kubernetes engineer. Automatic paging did not happen because the cluster's monitoring and alerting ran inside the cluster and failed along with DNS. The Kubernetes engineer then saw that the CoreDNS pods were being OOM-killed.

   Remediation

   • Raised the CoreDNS memory requests and limits by hand from 100Mi to 2000Mi

   Action items

   • Introduce a more resilient DNS setup: CoreDNS fronted by dnsmasq on every node
   • Improve high-level alerting from outside the clusters, so that a cluster problem is known before or at the same time as applications are affected
   • Improve incident response processes and tooling so that any on-call engineer can escalate directly to other teams when automatic paging fails, for example through a chat bot

   Lessons

   The Kubernetes DNS setup was far from resilient enough: a single application could take down DNS for everything in the cluster. The blast radius of one application sending more DNS queries should be limited to one node at most, ideally to single pods. Known Kubernetes DNS problems, such as Linux kernel races in conntrack handling and the ndots 5 problem, were familiar to the team and partly mitigated, but their combined effect on the DNS infrastructure was worse than the team had understood. Monitoring that runs inside the cluster was not robust enough: when the cluster's DNS broke, it could not page anyone, and recovery took longer than it should have.

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