Incident Library
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Cases4 cases

1 to 4 of 4

  1. 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.

    Root cause
    Component
    Contributing factor
    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.

  2. Symptoms

    CI jobs failed now and then because a hostname could not be resolved. The failures came from tensorstore, appeared on several different instances and did not repeat consistently. The error read: "./tensorstore/util/result.h:420: Status not ok: status(): UNAVAILABLE: CURL error Could not resolve hostname: Could not resolve host: company-bucket.s3.amazonaws.com".

    Trigger

    An administrative pod running with host networking called sudo in a loop. Each sudo call looked up the node's own hostname, and with ndots:5 and four search domains every lookup became five queries, for both A and AAAA records.

    Root cause
    Component
    Contributing factor
    Detection

    Reports of flaky CI jobs that could not resolve an S3 bucket hostname, followed by i/o timeout errors in the CoreDNS logs.

    Remediation
    • added 'Defaults !fqdn' in /etc/sudoers.d/00-no-fqdn (mode 440) to the container image so sudo stopped looking up the hostname
    Lessons

    Traffic from CoreDNS to the VPC DNS resolver is limited to 1024 packets per second per interface, and drops show up only as UDP timeouts; the linklocal_allowance_exceeded counter in ethtool shows them. Names that are not fully qualified are multiplied by the search domains under ndots:5, and AAAA lookups double them again. sudo looks up the host's own name on every call unless 'Defaults !fqdn' is set.

  3. Symptoms

    After Civis released its new script infrastructure on Kubernetes internally in September 2018, developers reported that kubectl commands were slow, and users intermittently could not start notebooks and services.

    Trigger

    Migrating clients' Python, R and container scripts from the in-house Bocce system to Kubernetes (the project called Croquet) pushed the cluster to about 290 EC2 instances under a large script load.

    Root cause
    Component
    Contributing factor
    Detection

    Developer reports of slow kubectl commands and user reports of notebooks and services failing to start, then high CPU on the master nodes.

    Remediation
    • moved internal users back to the old Bocce infrastructure
    • resized the three master nodes from m4.large to m4.4xlarge
    • changed the Datadog setup so per-node agents no longer polled the API server for service metadata
    • raised max-requests-inflight for non-mutating requests from 400 to 1200
    • grew the etcd EBS volumes from 20 to 100 GB (100 to 300 IOPS)
    • switched client scripts from the ClusterFirst DNS policy to Default
    Action items
    • Upgrade etcd from 2.2.1 to 3.x once Kops supports a safe migration
    • Solve the in-cluster DNS timeouts so client scripts can talk to other workloads in the cluster
    Lessons

    Kubernetes can reach thousands of nodes only after tuning, and third-party DaemonSets are a common cause of control plane load: an agent on every node that polls or watches the API server multiplies with the cluster size. Datadog, kube2iam and the fluentd metadata filter all showed this. A small configuration change often fixes it, but finding it took load testing and the API server access logs.

  4. 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.

    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
    Component
    Contributing factor
    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.

Check your cluster for this

These 4 cases involve CoreDNS and Client-side DNS resolver and cache. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

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