Incident Library
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  1. Symptoms

    From 17:03 UTC on January 27, 2026, a small subset of Intercom customers, fewer than 1%, saw inconsistent latency and stale data when reading from the platform. At 17:25 UTC an alarm on an excessive message backlog in a worker declared an incident automatically.

    Trigger

    An internal app deletion process, running more concurrent deletion steps per app than usual with no rate limiting, and the ETL data pipeline both put heavy load on the same database shard at the same time.

    Root cause
    Component
    Contributing factor
    Detection

    An alarm on an excessive message backlog in a worker fired at 17:25 UTC and declared an incident automatically.

    Remediation
    • Disabled ETL processing to reduce load on the affected shard
    • Scaled the app deletion workers to zero
    • Re-enabled ETL carefully once monitoring showed stable metrics
    Action items
    • Increase the capacity of shards to leave headroom for unexpected load spikes
    • Add rate limiting and resource controls to the app deletion process so it backs off when database load is high
    • Review how background work such as ETL and maintenance tasks competes with customer-facing operations for database capacity
    • Alert on replication lag earlier and link it to specific workloads
    • Communicate proactively with affected customers in incidents with a limited blast radius
    Lessons

    Because data is spread across many independent shards, the overload stayed on one shard and reached fewer than 1% of customers instead of the whole platform.

Check your cluster for this

This case involves Database and Database replication. 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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