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

Cases2 cases

1 to 2 of 2

  1. Symptoms

    On 19 February 2026, a node of the ClickHouse cluster behind PostHog's Logs product crashed, restarted and could not start again because it could not find a large amount of its data. Engineers then found that almost all of the Logs data stored on S3 was gone, so logs collected in the US region more than 3 days earlier were lost. Only the Logs product was affected.

    Trigger

    At 10:54 UTC a routine mutation was run to materialise an index. When it finished at 11:02, it set off a bug in ClickHouse's zero-copy replication.

    Root cause
    Component
    Contributing factor
    Detection

    No automated alarm fired. At 19:40 UTC a node in the cluster crashed, restarted and failed to start because of the missing data, and the engineers who investigated found that most of the S3-backed data was gone.

    Remediation
    • Switched Logs to a new table without zero-copy replication and caught up on recent messages
    • Backfilled the Logs data from the 3 days of Kafka history
    Action items
    • Disable zero-copy replication on all clusters
    • Audit the whole Logs ClickHouse configuration and keep experimental features out of production
    • Enable S3 versioning on the storage buckets so objects deleted by a database bug can be recovered
    • Spot-check configurations and data integrity strategies for single points of failure before a product becomes generally available
    Lessons

    Running Logs in its own cluster kept every other product unaffected, and 3 days of Kafka retention saved the recent data. An experimental setting chosen for an early internal stage was allowed to stay in production. Monitoring covered only incoming and recent data, so petabytes were deleted over 8 hours without an alarm. Relying on database replication alone over shared storage was a single point of failure, and S3 versioning would have let the deleted files be recovered.

  2. Symptoms

    On Friday evening, during scheduled maintenance that started at 5:30 PM PT, the Dropbox service went down. Most functionality came back about three hours later, but core service was not fully restored until 4:40 PM PT on Sunday.

    Trigger

    A planned maintenance on Friday at 5:30 PM PT upgraded the operating system on some machines with a script that was meant to check that a machine held no active data before reinstalling it.

    Root cause
    Component
    Contributing factor
    Remediation
    • Restored the affected databases from backups
    Action items
    • Add a check that makes each machine verify its own state locally before running incoming commands, so machines running critical processes refuse destructive operations
    • Use a new tool that replays binary logs in parallel to recover large MySQL backups faster
    • Open source the parallel binary log replay tool
    Lessons

    Scripts that verify a machine's production state remotely before upgrading or repurposing it can be wrong, so machines need to check their own state before accepting destructive commands. Replicas give redundancy, but when every replica is lost the restore from backup decides the length of the outage, and the standard MySQL restore tool is slow on large data sets.

Check your cluster for this

These 2 cases involve Database and Database backups and snapshots. 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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