Cases8 cases

1 to 5 of 8

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-0380 A compaction change in InfluxDB Cloud us-east-1 dropped data from compacted files, and queries missed data for weeks

   InfluxData2026

   Symptoms

   From 9 February 2026, InfluxDB Cloud customers on the AWS us-east-1 cluster prod101-us-east-1 found that some recent data did not show up in query responses. The first support ticket about data availability came in on 9 February, the day a compaction change was deployed, and more tickets followed on 10 February.

   Data lossResponse quality degradationHigh latency

   Trigger

   On 9 February 2026 InfluxData deployed to prod101-us-east-1 a compaction change that runs several compaction jobs in parallel on one partition, each on a subset of the data chosen by org id prefix.

   Root cause

   Faulty deployment

   Component

   DatabaseDatabase backups and snapshots

   Contributing factor

   Unsafe failure behaviourMissed in reviewGap in automated testsUnrepresentative staging

   Detection

   Tickets about data availability were raised, the first on 9 February and more on 10 February, when the status page reported that some recent data was not showing up in query responses.

   Remediation

   • Rolled back the compaction change
   • Restored recent data first, rebuilt from the Kafka logs
   • Changed the restoration process to make historical data recovery more efficient
   • Restored historical data from the weekly TSM snapshots
   • Kept the cluster online and restored data incrementally so ingest and queries could continue

   Action items

   • Expand test coverage for changes to critical storage components such as the compactor, with scenarios closer to production-scale workloads
   • Strengthen safeguards around compaction so failures are detected and handled safely
   • Review restoration workflows and procedures to reduce elevated TTBR and slow queries during large restores
   • Revisit the backup and restore strategy to speed up cluster-wide restorations

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3. IL-0242 Both PostgreSQL servers behind matrix.org were lost during a disk expansion, and the homeserver was down for 24 hours

   Matrix.org Foundation2025

   Symptoms

   On 2 September 2025 the matrix.org homeserver went down at 17:45 UTC and stayed down until 18:00 UTC the next day. Both machines of its PostgreSQL cluster were out of action: the original primary had lost its RAID array during a disk expansion, and the data directory of the new primary had been deleted by mistake.

   Service outageData loss

   Trigger

   During maintenance to add drives, an existing drive disappeared from the primary database server's RAID 10 array at 11:17 UTC, which led to a failover and a rebuild of that server. The outage itself began when the data directory of the new primary on db-01 was deleted while clearing a failed restore on db-02.

   Root cause

   Operator error

   Component

   DatabaseDatabase backups and snapshotsDatabase replication

   Contributing factor

   Overlapping failuresHarmful manual actionUnpatched known bugNo safeguard on destructive change

   Detection

   Monitoring fired when the drive disappeared from db-02's RAID array at 11:17 UTC, and Mythic Beasts confirmed the issue. The team realised it had deleted db-01's data directory, and the backend team was then paged, an incident was opened and an emergency was declared.

   Remediation

   • Created a fresh RAID 10 array on db-02
   • Unblocked WAL archiving from the new primary on db-01 to S3
   • Started restoring db-02 from the S3 backups with wal-g
   • Shut down Synapse and the damaged primary database
   • Remounted the data partition of db-01 read-only
   • Copied the WALs missing from S3 from db-01 to db-02
   • Updated wal-g and patched it to continue from the partial restore
   • Configured a restore_command wrapper that fetched WALs from S3 and the local files, and replayed them in standby mode
   • Promoted db-02 to primary and restarted Synapse
   • Ran backups against the primary while no replica existed
   • Rebuilt db-01 as a replica from a base backup of db-02

   Action items

   • Run pg_repack to reclaim free space
   • Take incremental backups more often, since they restore faster than WAL replay
   • Reduce Synapse's storage requirements
   • Consider changing the terminal background colour by the database's role, for example by watching for the standby.signal file
   • Consider wrapper scripts around sensitive commands, such as an alias for rm, and automating operations such as starting a base backup from primary to secondary
   • Shorten the recovery time, with infrastructure from service providers
   • Upgrade wal-g on all servers and repeat disaster recovery testing with it
   • Explore ZFS local snapshots at the next hardware refresh
   • Make status.matrix.org the source of truth during incidents and keep social media updated

   Lessons

   Most of the difficulty came from the size of the database: restoring 51TB and replaying WALs took far longer than hoped, and incremental backups restore much faster than WAL replay. Running a destructive command on the wrong server was the turning point, and Matrix.org looks for better tooling and processes rather than blaming the individual; working as a trio did not catch the mistake, and server names that suggest a role caused confusion because a primary and a secondary swap roles. The recovery lost no data, but the recovery time needs to be shorter, and the wal-g versions on the disaster recovery server and the production servers had drifted apart.

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4. IL-0331 An RDS MySQL Multi-AZ failover lost 5 seconds of committed data and broke replication on all five read replicas

   Razorpay2020

   Symptoms

   On a Friday evening in December 2019, Razorpay's internal monitoring alerted on an unusually high number of 5xx errors. The SRE team found that the main application, called API, could not connect to its RDS MySQL database, but within about 110 seconds the application recovered by itself and the alerts stopped. The RDS logs showed that the master instance had gone through a Multi-AZ failover.

   High error rateReplication lagData loss

   Trigger

   The master instance of the API application's RDS MySQL database went through a Multi-AZ failover to its standby in another availability zone.

   Root cause

   Configuration change

   Component

   DatabaseDatabase replicationDatabase backups and snapshots

   Contributing factor

   –

   Detection

   An internal monitoring alert on unusually high 5xx errors, then alerts that replication had failed on all replicas. The data loss was found the next day by matching the database against the application trace logs.

   Remediation

   • Moved all read replica traffic to the master instance
   • Recreated all five read replicas after a six-hour full snapshot and moved traffic back to them
   • Recovered the missing queries from the binary logs and corrected the data stored in RDS
   • Changed innodb_flush_log_at_trx_commit on the master from 2 to 1

   Lessons

   With innodb_flush_log_at_trx_commit set to 2, Razorpay had been open to data loss on any failover all along, even though RDS Multi-AZ copies everything on the primary's disk to the standby. The incident also taught the team how RDS runs a Multi-AZ setup and its failovers.

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5. IL-0218 GitLab.com's primary PostgreSQL data was wiped by mistake and, with no working backup, the site was down about 18 hours

   GitLab2017

   Symptoms

   From around 19:00 UTC on 31 January 2017, load on GitLab.com's PostgreSQL database rose, which GitLab suspected was spam, and many users could not post comments on issues and merge requests. Around 23:00 UTC the replication of the PostgreSQL secondary fell behind and then failed, because WAL segments it still needed had already been removed from the primary.

   Database overloadReplication lagData lossService outage

   Trigger

   While trying to restore replication to the secondary, an engineer ran the removal of the PostgreSQL data directory on the primary, db1.cluster.gitlab.com, believing it was the secondary.

   Root cause

   Operator error

   Component

   DatabaseDatabase replicationDatabase backups and snapshotsCloud block storage

   Contributing factor

   Untested backupSingle point of failureNo runbookPages not deliveredUnverified assumptionUnreviewed defaults

   Detection

   The engineer who ran the deletion on the primary noticed the mistake and stopped it a second or two later.

   Remediation

   • Raised max_wal_senders from 3 to 32 and lowered max_connections from 8000 to 2000 so PostgreSQL would restart
   • Stopped the deletion on the primary a second or two after noticing it
   • Restored the database from the LVM snapshot taken about 6 hours before the outage by copying the PostgreSQL data directory from staging
   • Restored the webhooks from a separate database built from the snapshot and imported them as a SQL dump
   • Incremented all database sequences by 100,000 so IDs used before the outage could not be reused
   • Re-enabled GitLab.com gradually
   • Rebuilt projects created after 17:20 UTC in basic form from their Git repositories on the NFS cluster

   Action items

   • Change the shell prompt (PS1) on all hosts to show more clearly which host and environment is in use
   • Monitor backups with Prometheus
   • Set PostgreSQL's max_connections to a sane value
   • Investigate point-in-time recovery and continuous archiving for PostgreSQL
   • Take LVM snapshots of the production databases every hour
   • Take Azure disk snapshots of the production databases
   • Move staging to the ARM environment
   • Recover the production replicas
   • Test recovery of PostgreSQL database backups automatically
   • Improve the PostgreSQL replication documentation and runbooks
   • Investigate pgbarman for PostgreSQL backups
   • Investigate WAL-E for database backup and realtime replication
   • Build a streaming database restore
   • Assign an owner for data durability
   • Set up multiple secondaries and balance load among them with pgpool-II
   • Provide a tool for developers to run and revert Rails migrations on staging
   • Build owned recovery procedures for the rest of the GitLab.com infrastructure, not only the database
   • Publish a backup monitoring dashboard and add more backup data to it
   • Improve the abuse reporting and response system so removal for spam does not hard delete
   • Redact engineers' names in future public incident documents

   Lessons

   GitLab split the incident into two problems: GitLab.com went down because the primary's data directory was removed by mistake during a manual, undocumented replication rebuild, and recovery took over 18 hours because the backups had failed silently, disk snapshots were not enabled and the only copy sat on slow staging disks. The backup procedure was never tested because no one owned it. GitLab chose to focus on fast, regularly practised recovery and on making it obvious which host an engineer is on, rather than on blocking commands, since a safer rm only protects against one of many ways to lose data.

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