Cases18 cases

3 to 7 of 18

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.

   View case
2. IL-0085 A control plane node of Magic Pages' Kubernetes cluster on Hetzner Cloud ran out of memory, the cluster's own calls then used up the Hetzner API rate limit, and all customer websites were down for about 7.5 hours

   Magic Pages2025

   Symptoms

   At around 09:33 UTC one of the three control plane nodes of the Kubernetes cluster that hosts Magic Pages went offline. Looking at the node showed that it had run out of memory.

   Service outageNode NotReadyCloud API rate limit exhausted

   Trigger

   One of the three control plane nodes ran out of memory and went offline at about 09:33 UTC; the attempts to replace it with hetzner-k3s failed while the cluster's own components were exhausting the Hetzner API rate limit.

   Root cause

   Capacity exhaustion

   Component

   Cloud controller managerPersistent volumes and stateful sets

   Contributing factor

   Unknown limitRetry stormMonitoring blind spotLimited debug accessSingle point of failureNo runbook

   Detection

   The founder found the control plane node offline at about 09:33 UTC. Monitoring alerts about unreachable customer websites started around the time the replacement attempts failed, and about half an hour of investigation identified the exhausted Hetzner API rate limit.

   Remediation

   • powered off all servers of the Kubernetes cluster to stop the API requests so the Hetzner rate limit could recover
   • waited until ratelimit-remaining had built up a buffer before starting recovery
   • restarted the control plane servers
   • started the essential Longhorn components by hand so Longhorn could heal and make storage volumes available
   • restarted the remaining websites by hand

   Action items

   • Monitor the Hetzner API call rate coming from the Kubernetes cluster nodes themselves to get early warning of high usage
   • Get a higher Hetzner API rate limit; Hetzner support was contacted
   • Investigate how to reduce the cluster's reliance on a single API, including alternatives to hetzner-k3s
   • Document and test a procedure for recovering Longhorn after a full cluster shutdown

   Lessons

   The running cluster, not only the deployment tooling, consumed the cloud provider's API quota, and in a failure it consumed it fast enough that the quota never recovered. The default limit of 3600 requests per hour was not enough for failure and recovery scenarios, and API usage from inside the cluster was not monitored. A tool that hides the provider's API errors slows diagnosis. After a full, non-graceful shutdown, Longhorn needed manual intervention to come back, which lengthened the outage.

   View case
3. IL-0103 During an EKS node AMI update, Istio sidecars crash-looped because hundreds of pods restarting at once overloaded the external certificate authority behind istiod

   Tetrate (unnamed U.S. federal customer)2025sev1

   Symptoms

   While the customer was updating the AMIs of its Amazon EKS nodes across more than 30 clusters running Istio, sidecar-injected workloads in one cluster failed to start and showed CrashLoopBackOff.

   Container crash loopProbe failures

   Trigger

   Updating the EKS node AMIs, which started hundreds of sidecar-injected pods at the same time.

   Root cause

   Dependency failure

   Component

   IstioAdmission webhookNode operating system image

   Contributing factor

   Unreviewed defaultsUnknown limit

   Detection

   Sidecar-injected workloads in the updated cluster failed to start and showed CrashLoopBackOff; the customer raised a Severity 1 ticket and a Tetrate engineer was reviewing logs within six minutes.

   Remediation

   • scaled istiod down temporarily from 40 replicas to 1 to slow Istio's internal processes and ease the load on the CA
   • deleted the CertificateSigningRequests stuck in the Approved, Failed state
   • restarted the pods gradually in a controlled order instead of all at once

   Lessons

   When a mesh hands certificate signing to an external CA, every pod start becomes a signing request to that CA, so a mass restart such as a node image rollout can overload it. Sidecars then abort under istiod's 10-second default CA timeout even though the CSRs are eventually signed. Restarting pods in controlled batches keeps the request rate within what the CA can handle. The author's point is that diagnosing such a failure means looking at the whole service mesh ecosystem, including its external dependencies, not only Istio.

   View case
4. IL-0159 Docker Hub rate-limited Linode's IPv6 address space with 429 Too Many Requests, breaking image pulls for LKE and LKE-E clusters

   Linode2025

   Symptoms

   From about 21:38 UTC on 5 February 2025, image pulls from Docker Hub over IPv6 failed with 429 Too Many Requests errors across several Linode regions. Both unauthenticated and authenticated pull requests were refused.

   Image pull failures

   Trigger

   Docker Hub's abuse prevention measures started rate-limiting image pulls coming from Linode's IPv6 address space.

   Root cause

   Dependency failure

   Component

   Container registry and its credentialsVPC DNS resolver

   Contributing factor

   Unknown limit

   Detection

   The rate limiting began at about 21:38 UTC. The first status post, at 22:48 UTC, said Linode was aware of an issue with Docker Hub over IPv6 causing rate limiting and was investigating, and at 01:06 UTC on 6 February the status page said the issue had been identified as rate limiting of image pull requests to Docker Hub over IPv6.

   Remediation

   • changed the DNS resolvers to return an A record instead of an AAAA record so image pulls to Docker Hub used IPv4

   Action items

   • Find long-term solutions so that Docker Hub's abuse prevention measures do not affect Linode services

   Lessons

   A registry's abuse limit that groups IPv6 addresses by their first 64 bits can block a provider's whole address range at once, and authentication does not help. Answering DNS lookups with IPv4 addresses only was the quick way to move image pulls off the blocked range.

   View case
5. IL-0044 Prometheus OOMKilled in a loop while replaying its write-ahead log after a kube-prometheus-stack upgrade

   Saifeddine Rajhi2024

   Symptoms

   After an upgrade of the kube-prometheus-stack Helm chart, several pods stayed not ready. Among them was prometheus-prometheus-operator-prometheus-0, showing 3/4 Running and a recent termination.

   OOMKilledProbe failuresMonitoring outage

   Trigger

   During the kube-prometheus-stack Helm chart upgrade, Prometheus had to replay its write-ahead log on startup, and the replay needed more memory than its limit allowed.

   Root cause

   Capacity exhaustion

   Component

   PrometheusContainer memory limit

   Contributing factor

   Unknown limitNo headroom

   Detection

   The team noticed pods not ready after the Helm chart upgrade and found the OOMKilled termination with kubectl describe pod.

   Remediation

   • ran promtool tsdb analyze inside the Prometheus pod to find high-cardinality metrics such as haproxy_server_http_responses_total

   Action items

   • Verify that the memory overhead during WAL replay stays within acceptable limits, for example 10 to 15%, and do not run Prometheus close to its memory limit
   • Optimise storage, scraping and remote write configuration and upgrade to newer Prometheus releases
   • Recover automatically from OOM crash loops, for example by deleting the WAL on OOM events
   • Enforce scrape limits so Prometheus stops scraping targets when memory use passes a threshold

   Lessons

   Size Prometheus for its restart, not only for steady state: WAL replay can need far more memory than normal running, and a Prometheus that already runs near its limit can be OOM-killed during the replay. Much of Prometheus memory use comes from cardinality, which promtool tsdb analyze can measure even when Prometheus itself is down.

   View case

By root cause5