Cases12 cases

9 to 12 of 12

1. 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

   View case
2. IL-0443 A Repro Aurora MySQL reader restarted unexpectedly and writer insert latency rose about sevenfold for a day or more

   Repro2019

   Symptoms

   A reader of Repro's Aurora MySQL cluster restarted for no known reason, and right after that the writer's insert latency rose to about seven times normal and writer performance stayed badly degraded for a long time. This happened more than once, twice within two weeks by 11 November 2019. While the writer was slow, the applications that process data collected by the Repro SDK fell behind, and the team could only watch until the writer recovered.

   High latencyReplication lagThroughput degradation

   Trigger

   Long-running queries on the reader; the author recalls that the cause was a query sent to the reader from Redash.

   Root cause

   Capacity exhaustion

   Component

   DatabaseDatabase replication

   Contributing factor

   Monitoring blind spot

   Remediation

   • Waited for writer performance to return, since the rollback segment release could not be sped up

   Action items

   • Add a monitor on the rollback segment, mysql.innodb.history_list_length in Datadog
   • Kill queries that run long enough to make the reader restart as soon as they are found
   • Set max_execution_time so queries are stopped after a fixed time

   Lessons

   Once the rollback segment has grown large, nothing speeds up its release across the cluster, so the way to avoid the latency is to keep transactions small and stop long-running queries before the rollback segment grows.

   View case
3. IL-0297 Mezmo users could not sign in and log indexing lagged after the auth cache's Redis servers were OOM killed

   Mezmo2019

   Symptoms

   On 15 May 2019, alerts and customer reports showed that the Mezmo web application did not let users sign in, and the status page reported an indexing delay: incoming logs reached live tail and search late.

   Service outageOOMKilledDatabase overloadThroughput degradation

   Trigger

   A traffic spike that the tool tracking batches of logs was not sized to handle.

   Root cause

   Capacity exhaustion

   Component

   In-memory cacheDatabase

   Contributing factor

   Dependence on a warm cache

   Detection

   Alerts and customer reports in the early morning hours triggered the escalation procedure and woke the on-call engineers.

   Remediation

   • Resized specific parts of the cluster to handle a much higher load

   Action items

   • Retool the batch tracking system to reduce its direct impact on the Redis clusters
   • Add observability instrumentation around the batch tracking feature set
   • Improve monitoring for Redis
   • Remove the bottlenecks in the pipeline that were involved in this incident

   Lessons

   Mezmo took from the incident that deep instrumentation, tracing and observability across the platform matter more as features scale up.

   View case
4. 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.

   View case

By root cause4