Cases12 cases

7 to 11 of 12

1. IL-0241 A database shard could not elect a new primary, and the Stripe API failed most requests in two periods on one day

   Stripe2019

   Symptoms

   On 10 July 2019 the Stripe API failed a substantial majority of requests twice, from 16:35 to 17:02 UTC and from 21:14 to 22:47 UTC. In the first period automated monitoring detected within a minute that a database cluster had failed to elect a new primary.

   High error rateFailover failureWrites rejectedIntermittent timeoutsSustained high node CPU

   Trigger

   The primary node of a database cluster failed at 16:35 UTC while one shard had several stalled nodes. The second period was set off by rolling the election protocol back to the previous minor version at 20:42 UTC.

   Root cause

   Software defect

   Component

   DatabaseDatabase failover manager

   Contributing factor

   Change with a delayed effectMonitoring blind spotExcessive blast radiusHarmful manual actionUnverified assumptionOverlapping failures

   Detection

   Automated monitoring detected the failed election within a minute, and the team was alerted at 16:36 UTC. The second period was announced at 21:14 UTC by automated alerts that some shards in the cluster were unavailable.

   Remediation

   • Restarted all nodes of the database cluster, which forced a successful primary election
   • Updated the production configuration and restarted the database cluster's nodes
   • Ramped traffic back up, putting services for user-initiated API requests first

   Action items

   • Alert when nodes stop reporting their replication lag
   • Alert when a shard enters a state that could trigger the election fault
   • Work with the database maintainers on a fix for the underlying fault
   • Add circuit breaking on failed operations to particular database clusters
   • Use fault isolation to contain the impact of a single failed shard
   • Limit the resources used by clients that keep retrying failed requests
   • Add procedures and tooling that make rapid configuration changes during incident response safer

   View case
2. IL-0211 A Mandrill PostgreSQL shard shut down at transaction ID wraparound and email sending was impaired for about 40 hours

   Mailchimp2019

   Symptoms

   From 05:35 UTC on 4 February 2019 Mandrill, Mailchimp's transactional email service, sent only 80% of queued emails, because one of its PostgreSQL shards, shard4, stopped accepting writes. When Mandrill engineers opened an investigation at 11:58 UTC, they saw a sharply elevated load on shard4.

   Writes rejectedThroughput degradationLoad imbalanceNode disk full

   Trigger

   At 05:35 UTC on 4 February the transaction ID counter of shard4, the busiest Mandrill PostgreSQL shard, reached its upper limit and PostgreSQL shut the database down to protect against wraparound.

   Root cause

   Capacity exhaustion

   Component

   DatabasePostgreSQL vacuum

   Contributing factor

   Ignored early signalNo alert on limitSignal buried in noiseRetry stormMonitoring blind spot

   Detection

   The first exception was logged overnight but was buried by unrelated exceptions in the centralized error reporting. Mandrill engineers opened an investigation at 11:58 UTC and, while looking for the source of the rising load on shard4, found the wraparound at 12:56 UTC.

   Remediation

   • Put shard4 into standalone mode and started VACUUM at 14:16 UTC on 4 February
   • Stopped and restarted the vacuum with settings that made it run faster
   • Started a dump and restore of the database without the Search and Url tables while the vacuum ran
   • Replaced storage volumes on the Mandrill app servers whose disks were filling with queued jobs and logs
   • Moved the job-queue tables from shard4 to other live shards
   • Truncated the Search and Url tables on the locked database at 19:12 UTC on 5 February, after which the vacuum finished in about an hour
   • Started sending from the backlog at 21:36 UTC
   • Added alerting for XID wraparound by 22:40 UTC

   Lessons

   Mailchimp named the factors that slowed its response: knowledge of and access to Mandrill systems and monitoring rested with a few people, Mandrill logging needed a locally writable disk, logs of malformed API requests were hard to separate from real application errors, job health was hard to see, and the disk space problem made partial deployments for triage risky. What helped was trained incident commanders with clear roles (incident commanders in 3-hour rotations, scribes, technical liaisons and points of contact between teams), periodic summaries with a list of recent and open decisions, and taking time in a video meeting to reassess, which led to the truncation. Running in the cloud let the team expand storage quickly, and the team was lucky that the largest table holding up the vacuum held data it could delete.

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3. IL-0300 Basecamp 3 went read-only for almost five hours when its events table reached the ceiling of 2,147,483,647

   37signals2018

   Symptoms

   On November 9th, 2018, from 7:21am to 12:11pm CST, Basecamp 3 was in read-only mode. Users could open existing messages, to-do lists and files, but could not add anything new or change anything that was already there.

   Writes rejected

   Trigger

   The busy events table, which records almost every activity in Basecamp 3, reached its ceiling of 2,147,483,647, and new events could not be written.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   No capacity planning

   Remediation

   • Raised the ceiling of the tables from 2,147,483,647 to 9,223,372,036,854,775,807, the same change as the Rails 5.1 default

   Lessons

   The outage was avoidable. The team did not account for how quickly the events table was running out of headroom while it was already working on the problem, and it should have reviewed its own database schema when Rails 5.1 announced the new default in 2017.

   View case
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-0335 A bugcheck in the primary SQL Server put the Stack Exchange network into read-only mode and then offline for 12 minutes

   Stack Exchange2017

   Symptoms

   On January 24, 2017 at 17:53 UTC the Stack Exchange network, including Stack Overflow, went into read-only mode for about 5 minutes and then went offline for 12 minutes. It took the team 2 minutes to notice the problem, 5 minutes to locate its source and 10 minutes to restore service.

   Writes rejectedService outageFailover failureData loss

   Trigger

   At 17:53 UTC the primary SQL Server, NY-SQL02, triggered a bugcheck in the SQL Server process.

   Root cause

   unknown

   Component

   Database

   Contributing factor

   Single point of failure

   Remediation

   • Restarted the SQL service on NY-SQL02, which brought the network back online in read-only mode
   • Put the sites back into read-write mode after a sanity check of SQL health
   • Brought the network online without waiting for the memory dump to complete
   • Took NY-SQL02 out of production for testing, including a memory test, and ran the normal load on NY-SQL01
   • Brought the SQL cluster up to 2016 SP1 CU1

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