Cases33 cases

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1. IL-0430 Mackerel's Redis stopped answering during a replication setup, keepalived failed over by mistake, and Mackerel degraded

   Hatena2018

   Symptoms

   On 26 September 2018 at 10:51 JST the whole Mackerel monitoring service stopped working properly and its alive monitoring stopped. The application servers could not connect to Redis, which stores Mackerel's monitoring data, and could not return correct responses.

   Monitoring outageHigh latencyIntermittent timeouts

   Trigger

   Redis memory use had been growing, so operators set up replication in order to scale Redis up. Building the replication took a long time and Redis stopped answering for a while, and keepalived treated this as a node failure and started a failover nobody intended.

   Root cause

   Configuration change

   Component

   In-memory cacheDatabase replicationDatabase failover manager

   Contributing factor

   Automation amplificationOverlapping failures

   Remediation

   • Switched back to the correct Redis and briefly saw the application return
   • Restarted the application servers one after another, which did not improve the symptoms
   • Detected the organizations posting inappropriate metrics and blocked their requests
   • Adjusted the timeout interval of the application servers
   • Temporarily added capacity to Diamond, the TSDB
   • Increased the number of Kinesis shards and Redis Cluster shards
   • Scaled out the API application servers
   • Put Mackerel in maintenance mode, checked it internally and let outside requests back gradually

   Action items

   • Raise the number of keepalived health checks for Redis so a brief load increase does not trigger a failover
   • Scale Redis up
   • Add more application servers
   • Change the application to store only the monitoring data it needs in Redis, reducing Redis memory use
   • Build a way to detect inappropriate API requests quickly and show them on a dashboard
   • Build a function to block organizations making inappropriate requests quickly
   • Keep improving the accuracy of detecting inappropriate requests and consider adding features such as an API limit
   • Strengthen monitoring of the application servers' internal processing so problems are handled before they become outages

   Lessons

   Hatena did not find why recovery took so long, but expects that keeping failover from reacting to short load spikes and giving the application more performance headroom would prevent an outage this long. It also aims to keep problems contained to a small part of the service when a failure does happen.

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2. IL-0431 During Mackerel's move to AWS, PostgreSQL failed over to a replica that had stopped replicating, losing an hour of data

   Hatena2017

   Symptoms

   On 7 August 2017, while checking the application after the maintenance that moved Mackerel from an on-premises data center to AWS, the team found that data updated during a specific time window was missing. Everything written to the PostgreSQL database between 14:30 and 15:30 JST was gone.

   Data lossReplication lagNetwork unreachable

   Trigger

   One hour after the application was switched to the newly promoted primary db001, db001 lost network connectivity and the database failed over to the standby db002.

   Root cause

   Operator error

   Component

   DatabaseDatabase replicationDatabase failover manager

   Contributing factor

   Overlapping failuresUnreviewed defaultsUnrepresentative stagingAutomation amplification

   Detection

   The team found the missing data while checking how the application behaved after the migration work.

   Remediation

   • Tried to restore the lost updates by hand from the table data left on db001 and gave up
   • Tried point-in-time recovery from the base backup and WAL and gave up
   • Built a new replica db003 with db002 as primary to restore redundancy
   • Kept db001 in maintenance state for investigation
   • Watched db002 for some time to confirm the problem did not recur, then completed the migration

   Action items

   • Change the PostgreSQL replica creation script to set recovery_target_timeline = 'latest' in recovery.conf by default
   • Add a check to a server configuration test tool such as Serverspec that recovery.conf contains recovery_target_timeline = 'latest'

   Lessons

   A setting needed only for a rare layout, such as a grandchild replica during a migration, was applied by hand through a script option and was forgotten. Because recovery_target_timeline = 'latest' is harmless on ordinary standbys too, Hatena now sets it by default whenever a replica is created. The staging rehearsal used the same layout and steps but had the setting right, so it could not catch the mistake. Hatena notes that a similar layout may be used again for a future PostgreSQL upgrade or a move to another region.

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3. IL-0325 DigitalOcean's control panel and API were down for 4 hours 56 minutes after the production primary database was deleted

   DigitalOcean2017

   Symptoms

   At 10:24 AM EDT on April 5th, 2017, DigitalOcean began to receive alerts that its public services were not working, and by 10:27 the team had verified that the production database on the primary had been deleted. The control panel and the API were unavailable for four hours and fifty-six minutes: running Droplets kept working, but no new Droplets or other resources could be created or managed.

   Service outage

   Trigger

   A process performing automated testing had been misconfigured with production credentials, and the production primary database was deleted.

   Root cause

   Configuration change

   Component

   DatabaseDatabase replicationDatabase backups and snapshots

   Contributing factor

   No safeguard on destructive change

   Detection

   Alerts at 10:24 AM EDT that public services were not functioning; within three minutes the team found that the primary database had been deleted.

   Remediation

   • Began recovery from a time-delayed database replica
   • Took a backup of the time-delayed replica
   • Copied the backup to the primary and recovered it
   • Copied the backups to the replicas until all systems were restored

   Action items

   • Drastically reduce access to the primary system for certain actions
   • Upgrade network connectivity between database servers
   • Update hardware to speed up recovery
   • Assess further safeguards against developer error
   • Improve the processes around data recovery
   • Explore ways to provide better real-time information to customers during future customer-impacting events

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4. IL-0334 Instapaper's RDS MySQL bookmarks table hit the 2TB ext3 file size limit, inserts failed and the service went down

   Instapaper2017

   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.

   Writes rejectedService outage

   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

   Capacity exhaustion

   Component

   DatabaseDatabase backups and snapshotsDatabase replication

   Contributing factor

   Unknown limitMonitoring blind spotUntested backupNo runbookLimited debug access

   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.

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5. IL-0218 GitLab.com's primary PostgreSQL data was wiped by mistake and, with no working backup, the site was down about 18 hours

   GitLab2017

   Symptoms

   From around 19:00 UTC on 31 January 2017, load on GitLab.com's PostgreSQL database rose, which GitLab suspected was spam, and many users could not post comments on issues and merge requests. Around 23:00 UTC the replication of the PostgreSQL secondary fell behind and then failed, because WAL segments it still needed had already been removed from the primary.

   Database overloadReplication lagData lossService outage

   Trigger

   While trying to restore replication to the secondary, an engineer ran the removal of the PostgreSQL data directory on the primary, db1.cluster.gitlab.com, believing it was the secondary.

   Root cause

   Operator error

   Component

   DatabaseDatabase replicationDatabase backups and snapshotsCloud block storage

   Contributing factor

   Untested backupSingle point of failureNo runbookPages not deliveredUnverified assumptionUnreviewed defaults

   Detection

   The engineer who ran the deletion on the primary noticed the mistake and stopped it a second or two later.

   Remediation

   • Raised max_wal_senders from 3 to 32 and lowered max_connections from 8000 to 2000 so PostgreSQL would restart
   • Stopped the deletion on the primary a second or two after noticing it
   • Restored the database from the LVM snapshot taken about 6 hours before the outage by copying the PostgreSQL data directory from staging
   • Restored the webhooks from a separate database built from the snapshot and imported them as a SQL dump
   • Incremented all database sequences by 100,000 so IDs used before the outage could not be reused
   • Re-enabled GitLab.com gradually
   • Rebuilt projects created after 17:20 UTC in basic form from their Git repositories on the NFS cluster

   Action items

   • Change the shell prompt (PS1) on all hosts to show more clearly which host and environment is in use
   • Monitor backups with Prometheus
   • Set PostgreSQL's max_connections to a sane value
   • Investigate point-in-time recovery and continuous archiving for PostgreSQL
   • Take LVM snapshots of the production databases every hour
   • Take Azure disk snapshots of the production databases
   • Move staging to the ARM environment
   • Recover the production replicas
   • Test recovery of PostgreSQL database backups automatically
   • Improve the PostgreSQL replication documentation and runbooks
   • Investigate pgbarman for PostgreSQL backups
   • Investigate WAL-E for database backup and realtime replication
   • Build a streaming database restore
   • Assign an owner for data durability
   • Set up multiple secondaries and balance load among them with pgpool-II
   • Provide a tool for developers to run and revert Rails migrations on staging
   • Build owned recovery procedures for the rest of the GitLab.com infrastructure, not only the database
   • Publish a backup monitoring dashboard and add more backup data to it
   • Improve the abuse reporting and response system so removal for spam does not hard delete
   • Redact engineers' names in future public incident documents

   Lessons

   GitLab split the incident into two problems: GitLab.com went down because the primary's data directory was removed by mistake during a manual, undocumented replication rebuild, and recovery took over 18 hours because the backups had failed silently, disk snapshots were not enabled and the only copy sat on slow staging disks. The backup procedure was never tested because no one owned it. GitLab chose to focus on fast, regularly practised recovery and on making it obvious which host an engineer is on, rather than on blocking commands, since a safer rm only protects against one of many ways to lose data.

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