Cases21 cases

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1. IL-0048 DNS lookups timed out in a large EKS cluster when CoreDNS hit the 1024 packets-per-second link-local limit, driven by sudo hostname lookups

   unknown2025

   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".

   DNS resolution failuresIntermittent timeouts

   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

   Capacity exhaustion

   Component

   CoreDNSClient-side DNS resolver and cacheVPC DNS resolver

   Contributing factor

   Unknown limitUnreviewed defaults

   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.

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2. 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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3. IL-0201 A framework upgrade caused kernel panics in GitHub's Kubernetes infrastructure, and GitHub.com web and API requests, mostly for issues and pull requests, failed intermittently for about 16 hours

   GitHub2025

   Symptoms

   From 18:05 UTC on March 17, 2025, GitHub.com web and API requests failed now and then for a small percentage of users, mostly requests about issues and pull requests. At 18:39 UTC the status page reported degraded performance for Issues.

   High error rateIntermittent timeouts

   Trigger

   A framework upgrade rolled out to GitHub's Kubernetes infrastructure caused kernel panics.

   Root cause

   Breaking change in an upgrade

   Component

   Linux kernel

   Contributing factor

   –

   Detection

   The status page opened the incident at 18:39 UTC, 34 minutes after the failures began, as an investigation of reports of degraded performance for Issues.

   Remediation

   • Downgraded the framework upgrade that caused the kernel panics
   • Disabled the problematic feature of the framework

   Action items

   • Investigate why the upgrade caused the kernel panics
   • Take steps to temporarily prevent the problem from coming back
   • Work out longer-term patch plans
   • Improve observability so that similar classes of problems can be reacted to quickly

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4. IL-0081 API requests timed out during deployment rollouts because the ingress-nginx enable-serial-reloads option held back endpoint updates while a long NGINX reload was running

   Cresta2025

   Symptoms

   During a deployment, traffic into the Kubernetes cluster started hitting dead ends: some API requests timed out and requests were slow and disrupted while the rollout ran.

   Intermittent timeoutsStale endpoint routingHigh latency

   Trigger

   A deployment rollout changed backend endpoints while a long-running NGINX full reload was in progress, with the enable-serial-reloads option enabled on the ingress-nginx controller.

   Root cause

   Software defect

   Component

   Ingress controller

   Contributing factor

   –

   Remediation

   • restarted the NGINX Ingress controller as an attempt to resolve the problem

   Action items

   • Open an issue in kubernetes/ingress-nginx describing how enable-serial-reloads blocks dynamic endpoint updates

   Lessons

   A flag meant only to serialise full reloads also delayed endpoint updates, which opened a window during rollouts in which the ingress routed to pods that no longer existed. Options that change how the ingress controller applies configuration need to be checked against what happens to endpoint updates during a long reload.

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5. IL-0131 A new kind of GenerateContent request made Vertex AI prediction API servers segfault, and once enough of them restarted at the same time the serving fleet could not carry the load

   Google Cloud2024

   Symptoms

   From 12:06 US/Pacific on 12 June 2024, services built on Vertex AI Prediction began reporting failed user requests in eight regions: us-central1, asia-southeast1, europe-west3, europe-west4, us-east1, us-west1, northamerica-northeast1 and us-east4.

   High error rateHigh latencyIntermittent timeouts

   Trigger

   Starting on 8 June 2024, a new form of user request to the GenerateContent API caused intermittent segmentation faults in the Vertex Serving Prediction API servers; on 12 June the rate of such requests increased.

   Root cause

   Software defect

   Component

   Model inference serverCloud load balancer

   Contributing factor

   Partial rolloutNo headroom

   Detection

   Internal production monitoring alerted engineers to the segfaults on 10 June 2024 at 07:35 US/Pacific, before any visible customer impact; on 12 June services built on Vertex Prediction reported failing user requests.

   Remediation

   • confirmed the new failures came from the same segfault found on 10 June
   • accelerated the rollout of the fix that was already in progress, completed at 15:59 US/Pacific

   Action items

   • Ensure early signals of server binary issues are captured in production health analysis and investigated in time
   • Ensure release validation of feature changes and updates in production

   Lessons

   A crash that only some requests trigger can stay invisible while load balancers route around the restarting servers. It becomes an outage when the share of such requests grows until too many servers are restarting at once. Here the cause was known and a fix was rolling out two days before customers were affected, but the progressive rollout had not finished when the triggering traffic increased; Google's follow-up is to investigate early signals of server binary issues in time.

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