Incident Library
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Cases4 cases

1 to 4 of 4

  1. Symptoms

    From 04:00 US/Pacific on 8 October 2026, Google Cloud Storage upload and write operations in the us-central1 region became slow and failed. Clients mostly saw HTTP 503 UNAVAILABLE errors on WriteObject operations and client-side timeouts.

    Trigger

    A configuration update to the Cloud Storage load balancing layer turned on a traffic sorting optimization that was meant to deal with local capacity constraints. The recurrence on 9 October came from an incomplete rollback of an upload planner feature.

    Root cause
    Component
    Contributing factor
    Detection

    Google engineering teams monitoring system telemetry detected the problem at 04:50 US/Pacific on 8 October, 50 minutes after it began.

    Remediation
    • Drained traffic from the most overloaded storage backend nodes
    • Deployed a configuration change that disabled the traffic sorting optimization
    • Rolled back a recent update to the metadata database service
    • Redirected traffic and drained affected storage components again during the recurrence
    • Fully reverted the load balancing configuration change, restoring capacity-aware routing
    • Returned the drained nodes to service once resource use was back to normal
    Action items
    • Validate load balancing logic changes in smaller regions before rolling them out to large, highly utilized regions
    • Make the storage load balancing enforce capacity constraints so traffic cannot bypass pre-spilling safeguards
    • Speed up internal data movement within highly utilized storage cells to absorb temporary load spikes
  2. Symptoms

    From about 11:20 UTC on 18 November 2025, Cloudflare's network started failing to deliver core traffic, and Internet users visiting Cloudflare customers' sites got an error page that pointed to a failure inside Cloudflare's network. The volume of HTTP 5xx responses served by the network jumped far above its normal low baseline.

    Trigger

    At 11:05 UTC Cloudflare deployed a ClickHouse access control change that made users' existing implicit access to the underlying tables in the r0 database explicit, so the metadata of those tables became visible in system tables such as system.columns. The change was being rolled out gradually across the cluster.

    Root cause
    Component
    Contributing factor
    Detection

    The first automated test detected the issue at 11:31 UTC, three minutes after the first errors on customer HTTP traffic at 11:28. Manual investigation started at 11:32 and the incident call was created at 11:35.

    Remediation
    • Bypassed the core proxy for Workers KV and Access so they fell back to a prior proxy version, which reduced their impact
    • Stopped the automatic creation and deployment of new Bot Management feature files
    • Inserted a known good feature file into the distribution queue and deployed it globally
    • Forced a restart of the core proxy
    • Scaled control plane concurrency to clear the backlog of dashboard logins
    • Restarted the remaining services that had entered a bad state
    Action items
    • Harden the ingestion of Cloudflare-generated configuration files the same way as user-generated input
    • Enable more global kill switches for features
    • Stop core dumps and other error reports from being able to overwhelm system resources
    • Review the failure modes for error conditions across all core proxy modules
  3. Symptoms

    From 11:45 UTC on 30 July 2024, a subset of customers got intermittent connection errors, timeouts and latency spikes when connecting to Microsoft services that use Azure Front Door (AFD) and Azure Content Delivery Network (CDN). The Azure Portal team saw the first service degradation at 11:47 UTC.

    Trigger

    Around 11:45 UTC the Azure network DDoS protection service was disengaging after a routine DDoS mitigation and returning traffic to its default routing to AFD, but the routes at one site in Europe could not be updated because the network control plane could not reach that site during a local power outage.

    Root cause
    Component
    Contributing factor
    Detection

    The Azure Portal team detected service degradation at 11:47 UTC and began to investigate. At 12:10 UTC network monitoring correlated the Portal incident to a network issue at one site in Europe, and networking engineers joined.

    Remediation
    • Rerouted traffic under the standard congestion playbook, which removed most of the impact by 13:58 UTC
    • Ordered the network control plane to reissue its commands to the misbehaving device, which could not be reached
    • Removed the device that kept attracting traffic from the network and scanned the network for other issues
    Action items
    • Add the missing configuration on network devices so a DDoS mitigation issue in one geography cannot spread to another
    • Improve validation and monitoring in the Azure network to detect invalid configurations
    • Improve monitoring for when the DDoS protection service is unreachable from the control plane but still serving traffic
  4. Symptoms

    On 26 September 2018 at 10:51 JST the whole Mackerel monitoring service stopped working properly and its alive monitoring stopped. The application servers could not connect to Redis, which stores Mackerel's monitoring data, and could not return correct responses.

    Trigger

    Redis memory use had been growing, so operators set up replication in order to scale Redis up. Building the replication took a long time and Redis stopped answering for a while, and keepalived treated this as a node failure and started a failover nobody intended.

    Root cause
    Component
    Contributing factor
    Remediation
    • Switched back to the correct Redis and briefly saw the application return
    • Restarted the application servers one after another, which did not improve the symptoms
    • Detected the organizations posting inappropriate metrics and blocked their requests
    • Adjusted the timeout interval of the application servers
    • Temporarily added capacity to Diamond, the TSDB
    • Increased the number of Kinesis shards and Redis Cluster shards
    • Scaled out the API application servers
    • Put Mackerel in maintenance mode, checked it internally and let outside requests back gradually
    Action items
    • Raise the number of keepalived health checks for Redis so a brief load increase does not trigger a failover
    • Scale Redis up
    • Add more application servers
    • Change the application to store only the monitoring data it needs in Redis, reducing Redis memory use
    • Build a way to detect inappropriate API requests quickly and show them on a dashboard
    • Build a function to block organizations making inappropriate requests quickly
    • Keep improving the accuracy of detecting inappropriate requests and consider adding features such as an API limit
    • Strengthen monitoring of the application servers' internal processing so problems are handled before they become outages
    Lessons

    Hatena did not find why recovery took so long, but expects that keeping failover from reacting to short load spikes and giving the application more performance headroom would prevent an outage this long. It also aims to keep problems contained to a small part of the service when a failure does happen.

Check your cluster for this

These 4 cases involve CDN and edge network and Database. 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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