Cases3 cases

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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

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2. IL-0453 Hakuna API latency spiked every day during the ElastiCache Redis backup as memory filled and the backup went forkless

   Hyperconnect2022

   Symptoms

   The service API latency of Hyperconnect's Hakuna rose sharply at a certain time, periodically. The per-dependency latency analysis in the APM showed that a bottleneck in Redis caused it, but no deployment and no change in traffic pattern could have affected how Redis was used.

   High latencyMemory pressure

   Trigger

   The daily ElastiCache backup on the primary node ran while available memory was insufficient, so ElastiCache used a forkless backup that slowed user requests.

   Root cause

   Capacity exhaustion

   Component

   In-memory cacheDatabase backups and snapshots

   Contributing factor

   No alert on limit

   Detection

   A latency graph showed the service API latency spiking periodically, and the APM's per-dependency latency analysis pointed at Redis.

   Remediation

   • Moved the ElastiCache backup from the primary node to the secondary node
   • Changed the code so data stored without a TTL is stored with one
   • Replaced the Redis Hash CrudRepository usages with a CustomRepository built on RedisTemplate
   • Ran a script that deleted the unneeded data already stored, freeing 58 GB on 3 databases

   Lessons

   The team says its Redis operation and monitoring had gaps, and lists what to watch: use a relational database when data has relationships or must be searched by value; write data that must be durable to on-disk storage rather than Redis; keep available memory at 50% or more when ElastiCache snapshots must be taken; take care that O(n) commands such as DEL on a collection do not stall production Redis; and check regularly for unnecessary I/O between the application and the database. Spring Data Redis repositories, which manage secondary indexes in the framework, suit workloads that need no TTL and need fast queries.

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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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