Incident Library
  • Root cause
  • Component
  • Symptom
  • Contributing factor
  • Case

Cases7 cases

6 to 7 of 7

  1. 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.

    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
    Component
    Contributing factor
    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
  2. Symptoms

    At 2:39 AM PDT (9:39 UTC) on 18 July 2013, services that relied on the redis-master of Twilio's billing system began to fail because the load on its host had become extreme. At 2:42 AM PDT monitoring alerted the on-call engineers to a failure in the Redis cluster.

    Trigger

    At 1:35 AM PDT on 18 July a loss of network connectivity disconnected all billing redis-slaves from the redis-master at once, and all of them requested a full synchronization at the same time. On-call engineers then restarted the overloaded redis-master.

    Root cause
    Component
    Contributing factor
    Detection

    At 2:42 AM PDT monitoring alerted the on-call engineers to a failure in the Redis cluster. At 3:28 AM PDT the billing system monitoring reported the anomalous charges, and at 12:36 PM PDT monitoring detected the recurrence.

    Remediation
    • Restarted the redis-master to relieve the high load, which loaded a wrong configuration
    • Took the billing system offline to stop further credit card charges
    • Restored service to all suspended accounts
    • Restored the lost account balances from the independent double-bookkeeping record in the relational datastore
    • Took billing offline again after the recurrence and reactivated the newly suspended accounts
    • Refunded all erroneous credit card charges
    • Gave all affected accounts a credit of 10% of their last 30 days of spend
    • Replaced the original Redis cluster and corrected the redis-master configuration
    • Activated the billing system progressively for groups of customer accounts
    Action items
    • Disable Redis restarts on the redis-master and recover the master by pivoting a slave instead
    • Add fail-safes so the billing system neither suspends accounts nor charges cards when balances are missing or cannot be written
    • Validate the billing system against the double-bookkeeping databases in real time
    Lessons

    Losing the in-flight balances and the ability to write balances at the same time exposed a flaw in the auto-recharge system: it failed in a dangerous way, charging cards and suspending accounts instead of stopping.

Check your cluster for this

These 7 cases involve Database and In-memory cache. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

By root cause5

Capacity exhaustion1 case

Organisation
  1. IL-0432 AutoReserve notifications silently went unsent as Memorystore for Redis 3.2 rejected writes with OOM below its limit

Faulty deployment2 cases

  1. IL-0356 Every domain hosted on Railway returned HTTP 404 to new connections for five minutes because route lookups failed
  2. IL-0380 A compaction change in InfluxDB Cloud us-east-1 dropped data from compacted files, and queries missed data for weeks

Configuration change2 cases

  1. IL-0367 Azure Storage in Central US rejected VM disk I/O, taking down VMs and the SQL Database and Cosmos DB services on them
  2. IL-0215 Twilio's billing Redis master lost all balances and went read-only, and auto-recharge charged customer cards repeatedly
    Twilio2013

Software defect1 case

Organisation
  1. IL-0340 Cloudflare's 1.1.1.1 resolver returned SERVFAIL to some valid DNS queries for four hours

Operator error1 case

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