Cases5 cases

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1. IL-0281 PostHog's Logs ClickHouse cluster silently deleted almost all its S3 data, losing US logs older than 3 days

   PostHog2026

   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.

   Data loss

   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

   Software defect

   Component

   DatabaseDatabase replicationObject storageDatabase backups and snapshots

   Contributing factor

   Monitoring blind spotSingle point of failureUntested backup

   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.

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2. IL-0527 OpenStreetMap.org made no replication diffs for 16 hours as PostgreSQL logical decoding failed on a memory alloc error

   OpenStreetMap Foundation2025

   Symptoms

   At 21:40 UTC on 25 June 2025, Alertmanager alerted the OpenStreetMap operations team: minutely replication feed delayed - 6m 2s. Since 21:33 UTC every run of the osmdbt timer that produces the minutely replication diffs had failed with ERROR: invalid memory alloc request size 1243650064.

   Replication lag

   Root cause

   Software defect

   Component

   Database replicationDatabase

   Contributing factor

   Pages not delivered

   Detection

   Alertmanager sent the first alert at 21:40 UTC, minutely replication feed delayed - 6m 2s, and several more followed, but none was a pager or SMS alert. A community report followed at 00:37 UTC and a social media report at 01:13 UTC.

   Remediation

   • Stopped the osmdbt timer
   • Put OpenStreetMap.org into a read-only maintenance window to suppress edits
   • Ran osmdbt by hand with fakelog to skip ahead of the PostgreSQL replication condition
   • Restarted the osmdbt timer for minutely diffs
   • Ended the read-only maintenance window

   Action items

   • Upgrade PostgreSQL to 17.x
   • Raise an issue with the PostgreSQL developers

   Lessons

   Communication with the osmdbt developer was crucial for working out the workaround, and the operations team's understanding of osmdbt was limited.

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3. IL-0376 A maintenance update set off needless full syncs between Upstash Redis replicas in eu-west-1, slowing the databases

   Upstash2025

   Symptoms

   On 25 February 2025, regional Upstash Redis databases in AWS eu-west-1 slowed down after a maintenance update, with very high CPU load on databases that have a replica in that region. Latency stayed high until about 21:23 UTC, even after the affected databases were unthrottled at 15:06 UTC.

   High latencySustained high node CPU

   Trigger

   A maintenance update to the regional Upstash Redis databases in AWS eu-west-1.

   Root cause

   Software defect

   Component

   In-memory cacheDatabase replication

   Contributing factor

   Automation amplification

   Remediation

   • Added more resources to the regional eu-west-1 cluster
   • Unthrottled the affected databases at 15:06 UTC and enabled more throughput

   Action items

   • Deploy a fix that stops regional databases from starting unnecessary full synchronisations
   • Contact regional database users about migrating to Upstash Global, which does not have this issue

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4. IL-0589 Every Kickstarter MySQL replica on Amazon RDS stopped replicating on a duplicate-key error in the backings table

   Kickstarter2013

   Symptoms

   On Thursday, March 7th, all of Kickstarter's MySQL replicas on Amazon RDS stopped applying updates, not just one replica that could be replaced. Most of Kickstarter's traffic is served from these replicas, so the site ran on stale copies of the database.

   Replication lag

   Trigger

   A later unsafe query on the backings table made MySQL switch to row-based replication for that query, and replaying the full row on the replicas collided with sequence values that already differed from the primary.

   Root cause

   Software defect

   Component

   DatabaseDatabase replication

   Contributing factor

   –

   Detection

   The team saw that the replicas had stopped updating, and SHOW SLAVE STATUS showed the duplicate-key replication error.

   Lessons

   Databases are intricate. In the Rails community they are sometimes treated as simple data stores hidden behind ActiveRecord and ActiveRelation, and Kickstarter's engineers concluded that it pays to learn how the database carries out what it is asked to do.

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5. IL-0303 GitHub's MySQL master failed over to a node with a cold buffer pool during a schema migration and GitHub.com went down

   GitHub2012

   Symptoms

   On Monday, September 10th, 2012, during a schema migration, the health checks on GitHub's MySQL master failed and Percona Replication Manager moved the 'active' role to another node of the new three-node cluster. That node's InnoDB buffer pool was cold and it performed poorly, its health checks failed in turn, and the role failed back to the original server. GitHub.com went down.

   Failover failureDatabase overloadHigh latencyReplication lagSplit brainService outage

   Trigger

   A schema migration on Monday, September 10th put more load on the master database than earlier migrations had, and Percona Replication Manager's health checks on the master failed.

   Root cause

   Software defect

   Component

   DatabaseDatabase failover managerDatabase replication

   Contributing factor

   Automation amplificationDependence on a warm cacheHarmful manual actionOverlapping failures

   Detection

   On the morning of September 11th, a developer told the operations team that the 'standby' node was returning incorrect query results.

   Remediation

   • Enabled Pacemaker's maintenance-mode so no health checks or automatic failovers ran, and let the buffer pool warm
   • Powered off the out-of-date node 'c' to end the partition
   • Stopped Pacemaker and Heartbeat on nodes 'a' and 'b' and started them on node 'a' only
   • Started MySQL on node 'a' in the active role
   • Removed the events leaked to wrong dashboards and audited the wrongly routed repositories
   • Contacted the owners of the exposed private repositories

   Action items

   • Change the Pacemaker configuration so the active database role fails over only when an operations engineer starts it
   • Find a way to warm the InnoDB buffer pool of slave databases so failovers do not hurt performance
   • Audit the Pacemaker and Heartbeat stack, focusing on the code path behind the segfault
   • Run hardware tests on the server where the segfault happened

   Lessons

   GitHub concluded that automated failover is not the right strategy for its primary production database: for every failover in this incident, any member of the operations team would have said it should not happen. Failovers that must happen, in an emergency or during maintenance, should not hurt performance.

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