Cases12 cases

5 to 9 of 12

1. IL-0552 CircleCI's archival database ran out of storage, slowing workflow and job starts and the display of workflows in the UI

   CircleCI2025

   Symptoms

   At about 23:00 UTC on 21 January 2025, an automated alert reported that the CircleCI database instance holding archived data was almost out of free storage space. From 23:50 UTC customers saw slow starts and cancellations of workflows and jobs, and delays and difficulty viewing workflows in the CircleCI UI, although pipelines for a specific project could still be viewed.

   High latencyIntermittent timeoutsLog floodNode disk fullCloud API rate limit exhausted

   Trigger

   A code change to a function in the archival database on 16 January 2025 unintentionally made it write an excessive number of log messages, and the database filled its storage.

   Root cause

   Faulty deployment

   Component

   DatabaseCI system

   Contributing factor

   Change with a delayed effectExcessive blast radiusOverlapping failures

   Detection

   An automated alert at about 23:00 UTC warned that the archival database was almost out of free storage, before customers were affected. The growing workflows queue was not linked to it at first because a separate alert about request volume fired at about the same time.

   Remediation

   • Scaled up the event consumer that processes the workflows queue at 23:44 UTC
   • Promoted a read replica of the archival database to a standalone primary at 23:55 UTC

   Action items

   • Fix the database function so it no longer writes an excessive number of log messages
   • Add a max duration to the workflows retry policy for archiving so it fails earlier than the default timeout
   • Move workflow archival to an event-based model that decouples it from the workflows service
   • Add forecast and anomaly monitoring for databases that alerts on unusual activity before it reaches critical levels

   Lessons

   The storage alert on the archival database did fire, but the database filled much faster than forecast, so there was little time to act before customers were affected. Because the workflows service called the archival service synchronously, a problem in the archival database slowed the whole workflows service.

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2. IL-0582 Jobs piled up in Kanmu's PostgreSQL-based qg job queue on Aurora as dequeue slowed and dead tuples kept growing

   Kanmu2024

   Symptoms

   A Datadog alert fired because a large number of unprocessed jobs had piled up in Kanmu's job queue and were not being worked off. The jobs were the batch enqueued at about the same time every day, in the usual volume, but the workers took far longer than normal to dequeue and process them.

   Throughput degradationHigh latency

   Trigger

   A heavy ad hoc analysis query from the BI tool Redash ran for a long time on the writer instance, because the Redash data source pointed at the cluster endpoint.

   Root cause

   Operator error

   Component

   DatabasePostgreSQL vacuum

   Contributing factor

   Change with a delayed effectUnreviewed defaults

   Detection

   A Datadog alert on unprocessed jobs piling up in the job queue.

   Remediation

   • Changed the Redash data source to the reader endpoint
   • Ended the long-running Redash query with pg_terminate_backend
   • Ran VACUUM on que_jobs by hand again, which cleaned up the dead tuples

   Action items

   • Make Redash unable to connect to the writer instance even if the writer endpoint is set, by mapping its host name to a TEST-NET-1 address in the Redash container's /etc/hosts
   • Set statement_timeout on PostgreSQL users so that long queries stop automatically
   • Consider improving qg's dequeue algorithm or adopting a job queue such as river that uses SELECT FOR UPDATE SKIP LOCKED

   Lessons

   A long-running query on the same database instance, even one unrelated to que_jobs, can keep VACUUM from removing que_jobs dead tuples, so the table keeps growing and the job queue slows down. qg's design with advisory locks and a recursive CTE is sensitive to dead tuples.

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3. IL-0571 STORES reservation returned bursts of 504 errors when a smart-list query held a database process for over 20 minutes

   STORES2024

   Symptoms

   About 20 minutes into a batch that rewrote the preset smart-list data of STORES 予約 (STORES reservation), an alert fired for a sudden rise in 504 errors (Gateway Timeout). The work had started a little after 03:00 UTC, and the metrics watched during it, such as web server response latency and the load of the writer database, had shown no large change.

   High error rateDatabase overload

   Trigger

   The preset smart lists 初回予約の顧客 (first-reservation customers) and 要フォロー顧客 (customers needing follow-up), created more than a month earlier, were rewritten with new search parameters after the search specification changed, which switched them onto the new search query.

   Root cause

   Faulty deployment

   Component

   Database

   Contributing factor

   Inefficient queryChange with a delayed effectUnrepresentative stagingMonitoring blind spotUnverified assumption

   Detection

   An alert on a sudden increase in 504 errors (Gateway Timeout), about 20 minutes after the preset rewrite batch started.

   Action items

   • Rework the database design so the inline-view search logic is no longer used
   • Add a test that checks this API's EXPLAIN output for scans larger than expected
   • Provide an environment where the timing of this logic can be measured easily

   Lessons

   Rushing to meet the schedule led to both a failure that affected users and a later release. In a multi-tenant database an EXPLAIN that shows index use can still hide large scans, tests need the case where one query is fast and the next is slow, the metrics to watch during a data change are those of the instance that serves the affected queries, and metrics sent at the end of a request are lost for requests that never finish.

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4. IL-0511 MIT xPro stopped responding after a configuration deploy as pgbouncer logins to RDS failed with 'wrong password type'

   MIT Open Learning2023

   Symptoms

   On 30 October 2023 at 3:51PM, six minutes after a configuration update was deployed to the xPro production Heroku stack, Pingdom alerted the DevOps on-call engineer, who confirmed at 3:52PM that the site was not responding.

   Service outageCredentials unavailable

   Trigger

   A configuration update applied through Salt at 3:45PM found that the Vault lease of the database credentials was about to expire and could not be renewed, so Vault generated new credentials.

   Root cause

   Configuration change

   Component

   DatabaseSecrets store

   Contributing factor

   Unreviewed defaultsChange with a delayed effectConfiguration not in code

   Detection

   Pingdom alerted the DevOps on-call engineer at 3:51PM, and the alert went through OpsGenie; Pingdom closed it automatically at 4:25PM.

   Remediation

   • Created a new database parameter group and applied it to the running RDS instance
   • Cleared the cached credentials and re-applied the Heroku configuration through Salt

   Action items

   • Move the management of xPro resources, including the RDS instance, into Pulumi, where AWS default parameter groups are not used

   Lessons

   Almost none of the configuration for this stack, the RDS instance in particular, was managed as code, and that was one of the main reasons the fix took long. Under Pulumi the instance would not have used an AWS default parameter group, and the fix would have been a change to an attached group.

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5. IL-0483 Replicate's website and API returned slow responses and HTTP 500s for two hours as PostgreSQL connections ran out

   Replicate2023

   Symptoms

   From 05:45 UTC on 11 May 2023, for about two hours, many Replicate customers got slow responses or HTTP 500 errors from the API and the replicate.com website and had trouble running predictions. Monitoring paged an engineer at 05:46 UTC, a minute after the database connection pool became fully saturated.

   High error rateHigh latencyLock contention or deadlock

   Trigger

   A little after 05:00 UTC on 11 May, normal variations in traffic pushed the stacking INSERT queries past their tipping point.

   Root cause

   Faulty deployment

   Component

   Database

   Contributing factor

   Change with a delayed effect

   Detection

   Monitoring detected the saturated database connection pool almost immediately and paged an engineer at 05:46 UTC.

   Remediation

   • Switched off all asynchronous prediction update features with a feature flag at 07:31 UTC, after which the database recovered

   Action items

   • Keep the asynchronous prediction update feature disabled and redesign it so it no longer sends stacked INSERTs
   • Review lock contention and other database hotspots that could cause similar problems
   • Publish detailed reports after major incidents

   Lessons

   The system ran in a degraded state that looked successful until it tipped into a full failure. The same asynchronous update work that triggered the outage also partly protected customers who used webhooks, and the stacked INSERTs came directly from a latency improvement: removing 100-150ms brought two updates of one prediction to the database almost together.

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