Cases4 cases

1 to 4 of 4

1. 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

   View case
2. IL-0392 MariaDB replication of section x1 between eqiad and codfw broke on a missing heartbeat row during a cookbook run

   Wikimedia Foundation2024

   Symptoms

   On 18 September 2024, while a cookbook was reconfiguring Wikimedia's core MariaDB databases from one-way eqiad -> codfw replication to circular replication ahead of a datacenter switchover, replication in the codfw -> eqiad direction broke on the x1 and es6 sections. Engineers watching the run noticed at 21:28 UTC that the x1 primary was not replicating.

   Replication lag

   Trigger

   Running the cookbook sre.switchdc.databases.prepare, which set up codfw -> eqiad replication and started pt-heartbeat, on the row-based x1 and es sections.

   Root cause

   Configuration change

   Component

   DatabaseDatabase replication

   Contributing factor

   Monitoring blind spotGap in automated testsHarmful manual action

   Detection

   Engineers watching the first run of the automation saw on orchestrator that the x1 primary was not replicating, because monitoring of circular replication was not yet enabled. The break in the eqiad -> codfw direction was reported by Icinga.

   Remediation

   • Skipped replication of the heartbeat tables with Replication_wild_ignore_table='heartbeat.%'
   • Inserted the missing heartbeat row with binary logging off on the es hosts
   • Inserted the missing heartbeat row with binary logging off on the x1 codfw hosts and removed the ignore rule

   Action items

   • Avoid the same issue again, probably by inserting the missing row when it is detected
   • Add a post-maintenance health check that replication works in both directions
   • Add options to run the maintenance in another order or on each section individually
   • Update the Phabricator task for each section rather than only at the end
   • Fix the monitoring gaps for circular replication
   • Made pt-heartbeat-wikimedia use STATEMENT format for its session on the x1 and row-based read-write es hosts, fixed in a later deploy

   Lessons

   Circular replication is not a normal working mode, so the first fix only moved the break to the other side; TRUNCATE would have worked better than DELETE because it always replicates as a statement, but it was not considered because it is more dangerous. Running sql_skip_slave_counter was avoided, since it could have made things worse or lost data. The check of the replication channel should also run once all tasks are complete, not only right after setup. The combination of row-based replication, pt-heartbeat and differing heartbeat rows was not described in earlier maintenance or documentation.

   View case
3. IL-0523 TANF-app production deployments were blocked for two months when a search-index migration ran out of database disk space

   U.S. Department of Health and Human Services2023

   Symptoms

   On 20 September 2023 the sprint 82 production deployment of TANF-app failed with a disk space error while it ran a large search-index migration, search_indexes 0017, against the production PostgreSQL database on cloud.gov. Production could not be deployed again until 9 November.

   Deploys failingNode disk full

   Trigger

   The sprint 82 deployment on 20 September 2023 recreated the deleted search_indexes migration 0016 and ran the large migration search_indexes 0017 against the production database.

   Root cause

   Faulty deployment

   Component

   DatabaseSchema migration

   Contributing factor

   Overlapping failuresGap in automated testsUnrepresentative stagingNo runbook

   Detection

   The sprint 82 production deployment failed with a disk space error, and the large migration search_indexes 0017 was identified as the cause.

   Remediation

   • Increased the production database instance size and re-ran the migrations
   • Created a backup of the production database
   • Truncated all of the search_indexes tables
   • Redeployed the sprint 82 release
   • Fixed the bug that stored empty fields as blank strings

   Action items

   • Write troubleshooting steps for database problems into the technical documentation
   • Write wiki pages and operations playbooks that outline common problems and responses
   • Add a database section and the cf revisions command to the failed-deployment troubleshooting document
   • Keep a GitHub repository of changes made directly to production
   • Save terminal output from production debugging sessions
   • Create Django migration best practices to enforce in code reviews
   • Do not edit migrations that have been merged to develop; write a new migration instead
   • Rename auto-generated migrations to more descriptive names
   • Use one auto-generated migration per model change and regenerate when a pull request makes successive changes
   • Organise a discussion on database seeding and backups for testing

   Lessons

   The work was unplanned and pulled in many developers from a small team, so the team wants documentation and playbooks that let fewer people step away from current work. Large datasets and unexpected edge cases such as rows without space-fill had not been tested, and a copy of the production database with PII scrubbed would allow tests against real-world data. Deploying in-progress code to other environments and changing migrations afterwards had caused conflicts there.

   View case
4. IL-0328 A field type change in the database of CircleCI's job distribution service left all jobs stuck in "not running"

   CircleCI2021

   Symptoms

   From about 18:50 to 19:43 UTC on Monday, November 8, 2021, CircleCI customer jobs on all executors were blocked in a "not running" state. Errors and alerts appeared right after a deploy that changed a field type in the database of the job distribution service.

   Service outageHigh latencyScale-up failureCloud API rate limit exhausted

   Trigger

   At 18:50 UTC a deploy changed the type of a field in the PostgreSQL database of the job distribution service, in a way that was not backwards compatible.

   Root cause

   Faulty deployment

   Component

   DatabaseNode pool

   Contributing factor

   Ineffective rollbackExcessive blast radiusGap in automated tests

   Detection

   Errors and alerts related to the distribution failure appeared immediately after the 18:50 UTC deploy.

   Remediation

   • Rolled back the field type change, which did not clear the errors
   • Wrote, built and manually deployed a change that made the distribution system ignore the field
   • Manually scaled the primary compute cluster for the expected thundering herd of work
   • Monitored until processing was normal and resolved the incident

   Action items

   • Fix the gaps found in the testing strategy
   • Change how the distributor reacts to unexpected data so that this kind of deploy should not affect all jobs
   • Make recovery faster when unforeseen failures occur

   View case

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