Cases12 cases

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1. IL-0314 Daily's newly upgraded Aurora MySQL database deadlocked on meeting-event queries and its call servers kept restarting

   Daily2023

   Symptoms

   At 9:47 AM Eastern (14:47 UTC) on 7 February 2023, Daily's database alerts reported a performance problem under normal operational load: a high level of lock contention on a database that had been upgraded the weekend before and had run normally since Monday. Users could get websocket connection errors when joining calls, and REST API requests could time out or return 5xx errors.

   Lock contention or deadlockHigh error rateIntermittent timeouts

   Trigger

   Database maintenance the weekend before had upgraded the database to a MySQL point release with a low-level behaviour change, and on 7 February, under normal load, that change produced the deadlocks Daily saw on Aurora MySQL 2.11.0.

   Root cause

   Breaking change in an upgrade

   Component

   Database

   Contributing factor

   Unrepresentative stagingAutomation amplificationRetry stormChange with a delayed effect

   Detection

   Database alerts at 9:47 AM Eastern reported a performance issue with high lock contention on the newly upgraded database.

   Remediation

   • Disabled MySQL binlog replication to the old database and restarted the database to reduce load
   • The database restarted by itself at 10:47 AM, the SFUs dropped the stale meeting updates without restarting, and the platform recovered
   • Rate-limited the POST /rooms endpoint to the same limit as DELETE /rooms/:name
   • Updated an index on the affected table in production on the Saturday evening to work around the MySQL behaviour change

   Action items

   • Work with AWS so the Aurora MySQL 2.11.0 deadlock issue is documented, understood and fixed in a future release
   • Add more API request patterns to the testing workload used for infrastructure changes
   • Add production monitoring alarms on more fine-grained database metrics

   Lessons

   Daily tests infrastructure changes under production-like workloads, but its synthetic workload did not have the shape that triggers these deadlocks. A deadlock behaviour change in MySQL 5.7 that was already known turned out far more severe in production than either Daily or the AWS Aurora team expected.

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2. IL-0294 CircleCI Docker and machine jobs stalled for nearly 12 hours after a VM cleanup queue lost all its RabbitMQ consumers

   CircleCI2021

   Symptoms

   From 13:30 UTC on 21 May 2021, CircleCI jobs on Docker, the Machine executor (including Windows, Mac, Arm and GPU) and remote Docker were delayed or blocked. Engineers had been paged at 13:00 for excessive delays in starting jobs, and the alerts showed CircleCI could not provision some types and classes of VM because it was hitting its CPU quotas in GCP.

   Throughput degradationCloud API rate limit exhaustedDatabase overload

   Trigger

   The upgrade of the RabbitMQ cluster from 3.8.9 to 3.8.16 on 20 May 2021, which brought in a 15 minute consumer acknowledgement timeout for all queue types.

   Root cause

   Breaking change in an upgrade

   Component

   Message brokerDatabase

   Contributing factor

   Change with a delayed effectIgnored early signalRetry stormExcessive blast radiusOverlapping failures

   Detection

   Engineers were paged at 13:00 UTC on 21 May for excessive delays in starting jobs. Earlier, a 02:31 notification that a VM destroyer queue had no consumers was handled by restarting pods, and a 10:11 alert that a VM destroy queue was backing up notified once at its threshold and did not page.

   Remediation

   • Blocked Linux machine jobs from entering the work queue
   • Had GCP raise the CPU quotas
   • Deleted old VMs by hand
   • Cancelled some workflows to free resources
   • Reset the VM services deployment so the VM destroy queues had consumers again
   • Scaled up the RDS database instance, at 18:20 and again at 19:15
   • Turned down VM provisioning and blocked remote-docker jobs to shed load
   • Scaled down all pods that created and destroyed VMs
   • Deployed a revised distribution backend that kept remote-docker jobs out of the provisioning queues
   • Brought provisioning and distribution back one component at a time and reversed the mitigation code changes

   Action items

   • Make VM provisioning more robust to changes in load so it can recover from thundering herds
   • Make VM provisioning independent so its failure does not affect Docker jobs
   • Review and improve observability of RabbitMQ and the production queues
   • Batch requests to avoid hitting GCP API rate limits
   • Improve incident response for long-running incidents

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3. 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

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