Incident Library
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Cases2 cases

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  1. Symptoms

    At 03:23 UTC on 27 March 2022, BattleMetrics' primary PostgreSQL database, which holds all account, server and player information, went into read-only mode. Users first saw the website fail to load, and soon RCON updates and queries began to fail as well.

    Trigger

    A corruption of the index on the player identifiers table, which BattleMetrics' logs place at about 26 February 2022, made every vacuum job fail, so transaction IDs kept climbing until Postgres stopped accepting writes on 27 March.

    Root cause
    Component
    Contributing factor
    Detection

    Users failed to load the website and reported it as their API keys expired, then RCON updates and queries began to fail, and the team found that the main database no longer accepted writes.

    Remediation
    • Shut the database down and dropped the corrupted player identifiers index in single-user mode
    • Ran the vacuum again, which succeeded and let the database accept writes
    • Rebuilt the player identifier index, which took about two hours
    • Shut down the query workers and gave RCON updates priority
    • Pointed services at the new Postgres primary
    • Bootstrapped new replicas one after another to take load off the primary
    • Restarted queries with throttling, prioritised by server population and rank, after RCON updates had caught up
    Action items
    • Monitor errors in the Postgres logs
    • Monitor and alert on transaction IDs
    • Set up a logical replica in addition to physical replication
    • Build a faster network to speed up recovery from failure
    Lessons

    BattleMetrics names skipping health checks on transaction ID size as its critical mistake: the index corruption silently broke vacuum for a month, and without monitoring of transaction IDs nobody saw the database approach wraparound. Because the corruption had been replicated to the replicas, failing over did not help, and the backup servers and downloadable backups carried the same corruption. The team also found that, contrary to the error message, a full vacuum on a server in wraparound protection does not need single-user mode, and chose to run a slow, delayed service over no service while replicas were rebuilt.

  2. Symptoms

    On Wednesday, February 9, 2017 at 12:30PM PT, inserts of new entries into the bookmarks table of Instapaper's MySQL database on Amazon RDS started to fail with an error. The bookmarks table, which stores the articles Instapaper users save, had grown past 2TB, and the Instapaper service was down until Thursday, February 10 at 7:30pm PT.

    Trigger

    At 12:30PM PT on February 9 the bookmarks table grew past the 2TB file size limit of the instance's filesystem.

    Root cause
    Component
    Contributing factor
    Remediation
    • Dumped the production database to a 5.5TB RAID-0 disk on an i2.8xlarge instance with Pinterest's SRE team
    • Launched a temporary production instance with limited access to archives
    • Set up an Aurora read replica of the old database as one recovery path
    • Imported the data into a new MySQL RDS instance without secondary indexes, then built the indexes
    • Had an Amazon engineer rsync the data from the ext3 filesystem to an ext4 filesystem mounted on the failed database
    • Synced the new ext4 database with the temporary production database through row-based replication
    • Promoted the new database to master and pointed the application at it
    Action items
    • Define a workflow that escalates system-wide Instapaper outages immediately to Pinterest's SRE team
    • Test MySQL backups every month instead of every three months
    • Put a process in place with Pinterest's SRE team to recover from this type of failure
    Lessons

    A filesystem limit that Instapaper did not know about and could not see made both the production database and all its backups useless, and the only way out was to restore the data onto a new filesystem. Because the hosted database could only be reached through MySQL, filesystem-level fixes such as rsync needed Amazon engineers. Even with a perfect response, rebuilding the database would have taken at least 10 hours. The lack of a disaster recovery plan for this kind of failure, and the estimate of the dump time from row counts, made the downtime and recovery longer than necessary, and the response could have drawn better on Pinterest's and AWS's people. The new escalation workflow and monthly backup tests would not have prevented this failure but would speed up the response to a future one.

Check your cluster for this

These 2 cases involve Database and Database backups and snapshots. 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.

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