Incident Library
  • Root cause
  • Component
  • Symptom
  • Contributing factor
  • Case

Cases18 cases

1 to 5 of 18

  1. Symptoms

    The on-call engineer for a high-traffic service was alerted that requests were failing in large batches every 30 seconds.

    Trigger

    DEBUG level logging was on in production when traffic surged, raising log volume to about 100 times normal while the service logged synchronously.

    Root cause
    Component
    Contributing factor
    Detection

    The on-call engineer was alerted that requests were failing in large batches every 30 seconds.

    Action items
    • Replace synchronous logging with TraceAwareAsyncAppender, which captures trace_id and span_id on the calling thread and drops logs instead of blocking when the queue is full (neverBlock=true)
    • Keep the amount of log output at a sustainable level, since an asynchronous queue still fills under sustained overload
    Lessons

    Removing AsyncAppender to keep trace IDs also removed the protection that kept request threads away from stdout back-pressure. A company-wide standard applies the same side effect to dozens of services, so check what a standard rests on and what working behaviour it takes away. ThreadLocal context does not cross into an asynchronous worker, but capturing it on the calling thread before handing the event off keeps both the IDs and the asynchronous output. Under extreme load, dropping log lines is preferable to stopping request handling.

  2. Symptoms

    From about 21:00 UTC on March 4, 2026, customers using Linodes backed by NVIDIA RTX 4000 Ada GPUs saw the GPUs lock up. At first the problem seemed limited to worker nodes of the Linode Kubernetes Engine (LKE); later Linode confirmed that every Linode with this hardware was affected, while Standard Compute and other GPU instances were not.

    Trigger

    Deployment of a repaired legacy GPU monitoring script, fixed during a routine improvement effort to restore a missing metric on internal observability dashboards.

    Root cause
    Component
    Contributing factor
    Detection

    Customers saw the lockups from about 21:00 UTC; the first status post went up at 02:07 UTC on March 5, about 5 hours after the lockups began.

    Remediation
    • Disabled the monitoring script across the GPU fleet
    • Rebooted the affected nodes
    Action items
    • Take improvement and prevention measures to stop a recurrence; the postmortem does not list them
    Lessons

    A monitoring script can take down the hardware it watches. A fix meant only to restore a missing metric brought back a firmware query written for an earlier GPU generation, and on the RTX 4000 Ada architecture that query against an active GPU triggers a lockup. The script ran across the GPU fleet, so every Linode with this GPU was affected in several regions, and the investigation first suspected a regression in the host hypervisor or the GPU firmware.

  3. Symptoms

    During performance testing of Pinterest's search system Manas on its new Kubernetes platform, a small but persistent share of search requests timed out. About one request in a million took 100 times longer than usual.

    Root cause
    Component
    Contributing factor
    Detection

    Performance testing with traffic sent to a test cluster showed a small but persistent percentage of requests timing out.

    Remediation
    • Disabled cAdvisor's working set size estimation on all PinCompute nodes
    Lessons

    CPU shielding does not fully isolate a process from its neighbours. Showing early that the node image was not the cause narrowed the search and avoided dead ends. Simple blackbox debugging, breaking things until the system stops working, works as a way of bisecting the problem.

  4. Symptoms

    Alerts fired at 3:13 PM PST on 11 December 2024, and from 3:16 PM every OpenAI service was significantly degraded or completely unavailable, including ChatGPT, the API and Sora. The status page reported API calls returning errors and difficulties logging in to platform.openai.com and ChatGPT. Customer impact peaked at 3:40 PM.

    Trigger

    At 2:23 PM PST a change introducing a new telemetry service, which collects detailed Kubernetes control plane metrics, was merged and the deployment pipeline started. Between 2:51 PM and 3:20 PM the change was applied to all clusters.

    Root cause
    Component
    Contributing factor
    Detection

    OpenAI's detection tools worked: alerts fired at 3:13 PM PST, a few minutes before customer impact began at 3:16 PM, and the root cause was identified at 3:16 PM.

    Remediation
    • Scaled clusters down to reduce the total Kubernetes API load
    • Blocked network access to the Kubernetes admin APIs so no new expensive requests arrived
    • Scaled up the Kubernetes API servers
    • Removed the telemetry service once enough control was back
    • Moved traffic from impacted clusters to healthy ones
    • Intervened manually in clusters where simultaneous resource downloads saturated resource limits
    Action items
    • Put every infrastructure configuration change through a phased rollout with continuous monitoring of both the service workloads and the clusters, including the Kubernetes control plane
    • Run fault injection tests in which the data plane runs without the control plane for longer, and tests that roll out bad changes on purpose to check detection and rollback
    • Build break-glass mechanisms so engineers can reach the Kubernetes API servers in any circumstances
    • Decouple the Kubernetes data plane from the control plane so that service discovery for critical workloads does not depend on the control plane
    • Speed up recovery with better caching and dynamic rate limiters for resources needed at cluster startup, and regular drills that rapidly replace an entire cluster
    Lessons

    Several systems and processes failed at once and interacted in unexpected ways: tests did not catch the change's effect on the Kubernetes control plane, DNS caching put a delay between the change and the failures, and being locked out of the control plane made remediation very slow. Because service discovery used Kubernetes DNS, the control plane was carrying load for product traffic even though the data plane was meant to be independent of it.

  5. Symptoms

    Kibana stopped showing new application logs at 13:45. At 14:27 a user told the Cloud Platform team in the #ask-cloud-platform channel that Kibana had received no logs since then.

    Trigger

    OpenSearch ran out of disk space and index writes were blocked.

    Root cause
    Component
    Contributing factor
    Detection

    A user reported in #ask-cloud-platform at 14:27 that Kibana had received no logs since 13:45. The report records 12:10, when the ClusterIndexWritesBlocked alert first fired, as the first detection.

    Remediation
    • restarted all fluent-bit containers (logs flowed for a few minutes, then the error came back)
    • increased OpenSearch disk space from 8000 to 12000
    • configured fluent-bit to stop sending logs to OpenSearch while the root cause was investigated
    Action items
    • Look into index dating issues in OpenSearch and Elasticsearch
    • Add high-priority alerts for Elasticsearch and OpenSearch
    • Re-introduce OpenSearch into live logging
    • Investigate the fluent-bit "failed to flush chunk" error
Check your cluster for this

These 18 cases involve Telemetry agent and Container runtime. 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.

By root cause6

Configuration change3 cases

  1. IL-0179 With DEBUG logging and synchronous log output, a high-traffic service filled the 64 KB stdout pipe, so request threads blocked in write() while holding database connections and HikariCP ran dry, failing requests in 30-second bursts
  2. IL-0005 A new telemetry service overloaded the Kubernetes API servers of large clusters and broke DNS-based service discovery for all OpenAI services
    OpenAI2024
  3. IL-0109 A retried node rollover on a DFDS EKS cluster set the Auto Scaling group back to 15 instances, and the default termination policy removed every healthy worker node, leaving the cluster with no nodes
    DFDS2023

Faulty deployment3 cases

  1. IL-0160 A repaired legacy GPU monitoring script sent a firmware query that triggers a race condition in the GPU System Processor of NVIDIA RTX 4000 Ada GPUs, locking up GPU Linodes and LKE worker nodes in several regions
    Linode2026
  2. IL-0142 GKE clusters on 1.24 and later wrote unexpected fluentbit error entries such as "Failed to get record: decoder: failed to decode payload: EOF" that used up the Logging API write quota for some customers
  3. IL-0034 A crashing stats agent sidecar marked every pod unready and the service mesh pulled all services out of traffic
    Airbnb2019

Software defect4 cases

  1. IL-0247 One in a million search requests took 100 times longer on Pinterest's Manas leaf nodes after moving to Kubernetes
  2. IL-0174 After an upgrade to Fluent Bit 2.0, logging sidecars that used the Kafka output plugin kept restarting with SIGSEGV because the plugin read a corrupted record as a map without checking its type
  3. IL-0041 fluent-bit silently stopped shipping application logs from one node to Elasticsearch
  4. IL-0051 A third-party log shipping client leaked file descriptors with every new container until nodes failed with too many open files

Capacity exhaustion6 cases

  1. IL-0042 Application logs stopped reaching Elasticsearch and OpenSearch for hours and were lost when OpenSearch ran out of disk space
  2. IL-0110 A Grafana Agent pod's high memory use and crash-looping Hangfire pods drove a Kubernetes node into system OOM and NotReady, twice in one day
    DFDS2024
  3. IL-0043 Application logs missing in Kibana after a newer Fluent Bit version changed its chunk drop strategy
  4. IL-0194 After a kube-apiserver restart, pod LIST requests from every pod of a Vector DaemonSet nearly tripled etcd memory and restarted all master nodes
    Palark2023
  5. IL-0026 Kubernetes nodes hit SystemOOM because fluentd-sumologic log shipper pods had no memory limits
  6. IL-0075 Kubernetes API servers pegged on CPU when the cluster grew to about 290 nodes, traced to Datadog agents polling the API

DNS or network fault1 case

Organisation
  1. IL-0083 Intermittent 5xx errors in a multi-tenant Kubernetes cluster came from internal DNS: the node-local dnsmasq hit its 150 concurrent query limit, did not cache cluster.local answers, and ndots search lookups flooded CoreDNS

Dependency failure1 case

Organisation
  1. IL-0073 Intermittent Kafka connectivity overloaded Docker daemons, set off a node rescheduling cascade and poisoned the Consul gossip mesh
    Target2019