Cases2 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

   View case
2. IL-0506 UC Berkeley DataHub logins failed and students got empty home directories after the JupyterHub database was overwritten

   UC Berkeley2017

   Symptoms

   At about 1 AM on 9 February 2017, UC Berkeley's DataHub was reported down: users trying to log in got a proxy error. The hub pod was running, but its log was full of sqlite errors.

   Service outageData lossMisrouted requests

   Trigger

   While debugging, an operator ran `.save jupyterhub.sqlite` in sqlite3 inside the hub pod without having attached the database, which replaced the JupyterHub database file with an empty one.

   Root cause

   Operator error

   Component

   DatabasePersistent volumes and stateful setsJupyterHub

   Contributing factor

   No safeguard on destructive changeUnsafe failure behaviourHarmful manual action

   Detection

   DataHub was reported down at about 1 AM, when users got a proxy error at login. At 1:12 AM a user reported that he could log in but all his notebooks were gone.

   Remediation

   • Scaled the hub to 0 replicas
   • Rebuilt the JupyterHub sqlite database by matching Kubernetes PVC names back to user ids
   • Set cookie_id to null instead of a dummy value and attached the rebuilt database to the hub
   • Changed the cookie secret in the hub pod and restarted it to log all users out
   • Deleted all user pods to log everyone out
   • Ran a script that filled in cookie_id for all users, restarted the hub and deleted all user pods again
   • Dropped the new users with a '.' in their name and renamed the older users from '-' to '.'

   Action items

   • Report upstream that JupyterHub uses only part of the cookie secret and discards the rest
   • Report upstream that the cookie_id column of the users table should have UNIQUE and NOT NULL constraints
   • Ask KubeSpawner to support username hashes in PVC and pod names so the names stay the same when the database is deleted
   • Ask OAuthenticator to support using the id from the Google authenticator as the user id, giving stable user ids
   • Switch to Google Cloud SQL, a hosted and managed MySQL database
   • Take regular and tested backups of the database
   • Start an operational FAQ of things to do and not to do
   • Set up better monitoring and paging
   • Document escalation procedures explicitly

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