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

Cases2 cases

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  1. Symptoms

    On 2026-06-11 https://gitlab.com returned widespread, intermittent HTTP 503 errors across all major services in two bursts of about 5 to 10 minutes, 06:16 to 06:22 UTC and 07:03 to 07:12 UTC. A Pingdom alert reported the first burst in triage at 06:16:12, about four minutes after impact began at 06:12, and each burst ended on its own.

    Trigger

    A GCP storage incident degraded the IO of a disk in the `us-east1-b` zone that backed GitLab.com's primary PostgreSQL node.

    Root cause
    Component
    Contributing factor
    Detection

    A Pingdom alert on the user-facing 503 errors was reported in triage at 06:16:12 UTC. No alert existed for disk IO saturation, pgBouncer pool saturation or LWLock contention.

    Remediation
    • Escalated to Database Operations experts
    • Paused deployments
    • Opened a GCP support case and engaged the GCP TAM, and GCP confirmed the storage incident
    • Prepared a Patroni primary switchover to node 04 in us-east1-d as a contingency, without executing it
    • Held a 3-hour pause-and-review window and resolved the incident after no recurrence
    Action items
    • Add disk IO saturation and queue-depth alerting for primary Patroni database nodes
    • Write a Patroni primary switchover runbook with explicit trigger thresholds
    • Clarify incident state-transition guidance for intermittent, self-resolving issues
    • Decide whether the primary should fail over automatically during disk IO degradation
    • Find a short-term mitigation that limits the blast radius of a single degraded disk until Cells do so
    • Make application database load balancing fail fast or reroute when the primary stops responding
    • Consider alerting on pgBouncer pool saturation and LWLock contention as leading indicators
    Lessons

    A single degraded disk was enough to take down GitLab.com because all primary database traffic goes through one node and requests waited for the Rack timeout instead of failing fast. The leading indicators, disk IO queue depth, pgBouncer pool saturation and LWLock contention, rose before full impact but had no alerts. Without decision criteria for a switchover, responders spent much of an intermittent incident waiting, and it was hard to decide when to move the incident to Fixing or Monitoring.

  2. Symptoms

    At 7:13 UTC on 20 October 2025, PlanetScale's continuous tests in production started failing because they could not create new database branches. The service that creates, resizes and configures database branches was unavailable, a near-total control plane outage, while existing database branches kept their capacity and connectivity.

    Trigger

    The incident started with a DNS misconfiguration at one of PlanetScale's service providers. An Amazon DynamoDB outage impacted AWS STS, and several hours of capacity constraints and network instability in AWS us-east-1 followed.

    Root cause
    Component
    Contributing factor
    –
    Detection

    PlanetScale's continuous testing in production started failing and alerted engineers at 7:13 UTC. At 10:05 UTC an alert fired for a Kubernetes operator in a large single-tenant customer installation exhausting its resources.

    Remediation
    • Disallowed new databases in us-east-1 and made us-east-2 the default region for new databases
    • Delayed new backups and cancelled pending backups that were waiting for an EC2 instance
    • Advised PlanetScale Managed customers with vtgate autoscaling to shed load
    • Paused draining and terminating EC2 instances older than 30 days
    • Kept vacant EC2 instances for reuse instead of terminating them
    • Bin-packed vtgate processes more tightly for customers with autoscaling
    • Sent manual reparent requests to move primaries to healthier availability zones
    • Restarted the edge load balancer and vtgate processes that did not recover after the partitions healed
    Action items
    • Understand and become resilient to the failure modes of SaaS dependencies for CI/CD, SSO, web application hosting and incident communication
    • Investigate more ambitious ways to reduce runtime dependence on internal and AWS services
    • Look into using all six us-east-1 availability zones to tolerate zonal outages and partitions
    Lessons

    Keeping the control plane separate from the data plane meant the control plane outage did not affect customer databases, and redundancy, automated failover and zonal traffic routing limited the effect of the network partitions. Network partitions are hard to reason about, test and tolerate: with three availability zones a system survives losing one zone only if the other two can still reach each other reliably.

Check your cluster for this

These 2 cases involve Database and Cloud block 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.

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