Cases6 cases

1 to 5 of 6

1. IL-0490 Records were missing from LootLocker's new MySQL database after a migration cutover and the service degraded for 3 hours

   LootLocker2026sev2

   Symptoms

   About an hour after LootLocker cut over from a managed database provider to its own MySQL deployment on 2026-04-28, data anomalies showed up in production. A small window of writes made shortly before the migration, spanning 2026-04-19 to 2026-04-20, had never reached the new database, and the service stayed degraded for about three hours after the cutover.

   Data loss

   Trigger

   On 2026-04-20 at about 16:20 BST, a routine credential rotation, made as part of an unrelated configuration fix, changed the credentials that the temporary replication process used for its database connection, and the process crashed.

   Root cause

   Certificate or credential expiry

   Component

   DatabaseDatabase replication

   Contributing factor

   Change with a delayed effectMonitoring blind spotUnverified assumptionNo safeguard on destructive change

   Detection

   Data anomalies were detected in production at about 11:30 BST, about an hour after the cutover.

   Remediation

   • Restored normal service behaviour first and deferred the root cause analysis
   • Requested a restore of the deleted source database from the previous provider
   • Replayed the affected records for impacted users from the restored source database

   Action items

   • Perform technical changes of comparable risk in a scheduled maintenance window instead of as live cutovers
   • Require a pre-cutover checklist that verifies replication or sync processes are live and caught up just before cutover
   • Require an explicit downstream impact review for credential rotations of service accounts used by long-running processes
   • Keep source systems until integrity in the target has been independently verified after cutover
   • Publish planned maintenance and migration notices in the in-product News feed as well as Discord, with clear impact statements and more advance notice

   Lessons

   The failure was a silent side effect of an unrelated change made eight days earlier: the replication process recovered and kept working, so there was no signal that data had been dropped, and a pre-cutover integrity check would have caught it. Deleting the source database before validating the new one made recovery much harder. Choosing a live migration to avoid downtime left no clean point to verify integrity, and the resulting degradation and two days of piecemeal recovery far exceeded what a short planned maintenance window would have cost. The announcement on Discord was not clear enough about the impact and came with too little notice.

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2. IL-0308 Most RevenueCat backend systems failed after cutover to a new Aurora PostgreSQL 14 cluster as queries became very slow

   RevenueCat2022

   Symptoms

   At 9:34 UTC on November 23, 2022, RevenueCat started moving traffic from its Aurora PostgreSQL 10.x cluster to a new Aurora PostgreSQL 14.x cluster. By 9:39 UTC all traffic was on the new cluster, latency was bad, and the RevenueCat dashboard and the endpoints used by the RevenueCat SDK stopped working.

   Database overloadHigh latencyService outageConnection pool exhaustion

   Trigger

   At 9:34 UTC production traffic was switched from the Aurora PostgreSQL 10.x cluster to the new Aurora PostgreSQL 14.x cluster, before ANALYZE had run on its largest tables.

   Root cause

   Breaking change in an upgrade

   Component

   DatabasePostgreSQL vacuumDatabase replicationDatabase proxy and connection poolerQuery planner and table statistics

   Contributing factor

   Unrepresentative stagingUnverified assumptionAutomation amplificationMonitoring blind spot

   Detection

   At 9:39 UTC, with all traffic on the new cluster, latency looked bad and the infrastructure team began to investigate.

   Remediation

   • Added a statement timeout to the database user so queries could not hold a connection too long
   • Shed 90% of traffic by returning 503s from the load balancer
   • Dropped indexes that the planner was using wrongly and that were not needed
   • Stopped accepting Apple webhooks for a time
   • Rolled out a release without New Relic to stop its EXPLAIN queries
   • Ran ANALYZE by hand on the biggest tables
   • Raised the values of the affected primary key sequences with a prepared script
   • Stopped the integration event dispatcher by hand
   • Replayed the webhooks that were dispatched but not recorded
   • Restored integration event dispatching, ETLs and other non-web server jobs

   Action items

   • Consider change freezes during business-critical periods
   • Explain better how the SDK behaves during backend outages
   • Tell customers about maintenance and outages in the developer dashboard and by direct email to technical contacts
   • Replay a subset of write traffic in migration dry runs to mimic the cutover
   • Move costly processes to asynchronous work to keep the API fast and stable
   • Re-evaluate table indexes, including unused ones the database may choose for queries
   • Show the load of each endpoint and system in the database dashboards
   • Involve support and customer communications staff during outages

   Lessons

   The new cluster had not been analyzed, and the tests missed it because the test cluster had been up long enough to gather statistics. Dry runs replayed no write traffic, on the belief that writes are simpler than reads, and the team assumed sequence state was replicated with the data. Rolling back at the start would have been better than fixing forward. The migration, timed to finish before Thanksgiving and Black Friday, should have been delayed until the week after, because the US had entered a sensitive period and part of the US support team was already on holiday.

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3. IL-0210 GitHub's MySQL clusters failed over to the US West Coast and the site served slow, stale, inconsistent data for 24 hours

   GitHub2018

   Symptoms

   At 22:54 UTC on 21 October 2018 GitHub's internal monitoring started alerting on many faults at once. By 23:02 UTC the first responders saw that the replication topologies of many MySQL clusters were in an unexpected state: the Orchestrator API showed topologies made only of servers in the US West Coast data center. When connectivity came back, the application tier had immediately started sending writes to the new West Coast primaries.

   Split brainFailover failureReplication lagHigh latency

   Trigger

   At 22:52 UTC on 21 October, routine maintenance to replace failing 100G optical equipment cut connectivity between GitHub's US East Coast network hub and its primary US East Coast data center for 43 seconds.

   Root cause

   DNS or network fault

   Component

   DatabaseDatabase failover managerDatabase replicationDatabase backups and snapshotsNIC, cable and switch port link

   Contributing factor

   Automation amplificationUnverified assumption

   Detection

   GitHub's internal monitoring raised alerts on numerous faults at 22:54 UTC, two minutes after the partition. By 23:02 UTC the first responders had found, by querying the Orchestrator API, that many database clusters had a topology with only US West Coast servers.

   Remediation

   • Locked the internal deployment tooling to stop further changes
   • Paused webhook delivery and GitHub Pages builds to stop jobs writing metadata
   • Restored the affected MySQL clusters from backups into the US East Coast data center and let them replicate from the West Coast
   • Found ways to restore directly from the West Coast to get around the throughput limits of downloading from off-site storage
   • Moved all database primaries back to the US East Coast data center
   • Spread read traffic over additional MySQL read replicas in the US East Coast public cloud so replication could catch up
   • Failed over to the original topology once the replicas were in sync
   • Processed the backlog of webhook events and Pages builds, pausing to raise an internal TTL after about 200,000 webhook payloads were dropped

   Action items

   • Reconcile the writes from the captured MySQL binary logs that were not replicated, automatically where possible and with user outreach otherwise
   • Make sure status updates can be published even when GitHub Pages builds are paused
   • Give more accurate public estimates of the time to repair
   • Configure Orchestrator so it cannot promote database primaries across regional boundaries
   • Move to a status reporting mechanism that shows the status of each component of the platform
   • Serve GitHub traffic from multiple data centers in an active/active/active design with N+1 redundancy at the facility level
   • Test assumptions about the system more proactively
   • Validate failure scenarios systematically, with investment in fault injection and chaos engineering tooling

   Lessons

   Orchestrator did what it was configured to do, but the application tier could not work with primaries on the other side of the country: leader election within one region is generally safe, while the sudden cross-country latency was a major contributing factor. The behaviour emerged from a partition larger than any GitHub had seen. Replication caught up along a power decay curve, not a straight line, so the linear estimates of recovery time were wrong. GitHub concluded that tighter operational controls and faster response are not enough on their own and that failure scenarios have to be validated before they happen.

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4. 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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5. IL-0296 Transloadit's us-east Redis cluster lost queued jobs and heartbeats, raising WORKER_JOB_ERROR errors for 81 customers

   Transloadit2016

   Symptoms

   Reports came in of more WORKER_JOB_ERROR errors, which tell Transloadit there is a communication problem between its Uploaders, the machines that receive files and orchestrate Assemblies, and its Drones, the machines that run encoding tasks. Many Assemblies became much slower because retries with exponential back-off kicked in, and a few could not be replayed.

   High error rateHigh latencyLoad imbalance

   Trigger

   A steep increase in HLS traffic, together with more Drones online processing the same volume faster, which put more load on the central Redis nodes.

   Root cause

   Capacity exhaustion

   Component

   In-memory cacheDatabase replication

   Contributing factor

   Monitoring blind spotUnverified assumptionHarmful manual action

   Detection

   Reports of an increased number of WORKER_JOB_ERROR errors.

   Remediation

   • Re-added lost messages and relaxed heartbeat monitoring as a stopgap
   • Upgraded Redis to the latest stable version
   • Doubled the Redis cluster to six nodes and then added three more, which made it worse
   • Brought the number of read replicas down to one, which cut traffic and failure rates
   • Capped how far Drones scale up in each region

   Action items

   • Add logging and alarms for approaching saturation
   • Find ways to decentralise the traffic between the Redis nodes and the Drones and Uploaders
   • Add sharding per Uploader on top of the sharding per region

   Lessons

   To handle more traffic Transloadit had to use fewer Redis nodes, not more: each added replica multiplied the replication traffic from the single leader that takes every write, so scaling up the number of nodes was the wrong call. Bandwidth had not been among the monitored Redis metrics.

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