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

Cases2 cases

1 to 2 of 2

  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

    On 7 October 2025 MongoDB reported a delay in the metric ingestion pipeline of MongoDB Atlas and Cloud Manager. Metrics in Atlas dashboards arrived late or went missing, Host Down alerts did not fire, and Atlas cluster management operations such as provisioning and resizing were delayed, while customer clusters themselves stayed healthy and available.

    Trigger

    The rollout of an updated internal software version caused a temporary spike in resource demand on the internal database cluster.

    Root cause
    Component
    Contributing factor
    Remediation
    • Took corrective actions to stabilise the system and monitored their effect
    • Implemented a fix and monitored the results until the incident was resolved
    Action items
    • Shard the collections with concentrated load to spread traffic over more nodes
    • Optimise inefficient queries
    • Provision additional infrastructure capacity for higher traffic
    • Refine capacity planning to anticipate future load spikes, using metrics from this event
    • Redesign software rollouts to account for the predictable rise in resource demand during a rollout
    • Keep monitoring how the sharding and query changes resolve the hotspots
    • Expand the detection of elevated load conditions for faster anomaly detection and response
Check your cluster for this

These 2 cases involve Database and Object storage. 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 cause2