Cases4 cases

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1. IL-0510 MIT Learn's RDS PostgreSQL ran out of storage after a release and learn.mit.edu returned 503s and 504s for hours

   MIT Open Learning2026

   Symptoms

   On 24 March 2026 learn.mit.edu went down shortly after a regular release. An engineer reported the outage with a linked Sentry issue, and Sentry soon showed many 504 responses from the site.

   Service outageNode disk fullDatabase overloadLock contention or deadlock

   Trigger

   A regular release deployed a query pattern change to the learning resource APIs that put an aggregation inside the main query.

   Root cause

   Faulty deployment

   Component

   Database

   Contributing factor

   Inefficient queryUnrepresentative stagingHarmful manual action

   Detection

   An engineer reported the outage with a linked Sentry issue, and Sentry showed many 504 responses. The RDS FreeStorageSpace metric showed that the database had used up its storage.

   Remediation

   • Rolled back the release's query pattern change
   • Deleted the read replica so the database could flush its buffered WAL files
   • Terminated all active queries running longer than 10 minutes with pg_terminate_backend, after the backup finished and with guidance from AWS Support

   Action items

   • Explore enabling enhanced analytics on the production Learn database
   • Document rollback steps on the platform engineering documentation site
   • Make easy rollback part of the new release management framework
   • Alert on DiskQueueDepth for production databases without making the alert too noisy
   • Alert when API and site response times are more than 30% above normal
   • Run a gameday on an example production database outage
   • Adopt Rootly for incident management, with an automatic channel and an automatic incident transcript
   • Write an outage runbook: start a call, have the on-call engineer watch metrics until they return to baseline, and give one person the communications
   • Put the query that terminates long-running queries, and a version that finds them for selective termination, into a production Postgres runbook
   • Open an issue on redesigning the Learn Resource View Event model, which is only used for counts
   • Use anonymised production data generation tools to bring RC up to production data volume, on separate infrastructure for load testing
   • Change the view count to use a precomputed column

   Lessons

   The change was reviewed by senior engineers and showed no problems locally or on RC, but data scale and load in production are very different, and nothing in the system's health showed that adding a COUNT aggregation would make disk usage explode.

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2. IL-0217 Honeycomb's main MySQL database locked up under cache-miss reads and ran out of connections, a 68-minute total outage

   Honeycomb2023

   Symptoms

   Hours after a routine switch between two clusters of Retriever, Honeycomb's storage and query engine, late on 24 July 2023, the internal SLO for Shepherd, the ingest service, began to burn slowly. The performance was only marginally slow and inconsistent, with no good explanation, so engineers treated it as minor and left it for the morning.

   High latencyDatabase overloadLock contention or deadlockService outage

   Trigger

   Late on 24 July Honeycomb switched between two Retriever clusters to avoid a subtle bug, and the new cluster stopped writing the last-written timestamps the schema cache depends on. On the morning of 25 July a feature flag flip meant to move those writes back stopped the remaining writes entirely.

   Root cause

   Software defect

   Component

   DatabaseIn-memory cache

   Contributing factor

   Dependence on a warm cacheIgnored early signalHarmful manual actionUnverified assumption

   Detection

   Hours after the cluster switch, the internal SLO for the ingest service Shepherd started burning slowly. Engineers left it for the morning, when a chart showed that Retriever's database calls updating the last-written records had stopped.

   Remediation

   • Flipped only the writes back to the other Retriever cluster with a feature flag, which did not fix the problem
   • Set up circuit breakers that denied all Shepherd traffic with a 5xx error to protect the database
   • Failed the hard-locked database over to a replica
   • Restarted all Retriever hosts so they could provide data for the cache
   • Set the last-written timestamp of every schema with traffic in the last day to now, so the schema store reloaded the cache
   • Removed the circuit breaker and restarted the query hosts that failed internal checks

   Action items

   • Finish the Retriever cluster migration and remove all code that could disable writes behind the cache
   • Explore ways to strengthen the cache further
   • Lower the contention put on the database during update storms from new schemas
   • Stabilise the performance costs of some operations in the system
   • Study instrumentation and experimentation that detect and handle such edge cases better

   Lessons

   Locally good decisions combined into the outage: the query engine wrote the timestamps because it already saw all the data, the shared cache helped scale ingest and untangle the main database, the cluster switch was done to avoid a bug, frequent deploys hid the feature flag bug, pausing deploys during the investigation kept hosts from restarting, and past near-misses made the team expect a slow failover. Feature flags, frequent deploys, pausing deploys during incidents and learning from near-misses all contributed, although they are among the practices Honeycomb trusts most. For a rare lockup of this kind a database failover is the fastest recovery. Several recent Honeycomb incidents started from trying to avoid a subtle bug.

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3. IL-0301 Basecamp 3's primary database went into total gridlock at the top of the hour and Basecamp 3 went down twice

   37signals2022

   Symptoms

   At 13:00 UTC on Monday, June 27, 2022, Basecamp 3 became extremely slow, then showed error pages, and then stopped working altogether. Basecamp 2 and Basecamp Classic kept working normally.

   Lock contention or deadlockDatabase overloadHigh latencyHigh error rateService outage

   Trigger

   At 13:00 UTC, when Basecamp 3 sends scheduled notifications in massive batches, a rush of demanding queries reached the primary database and started a cascade of heavy lock contention.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   No capacity planningHarmful manual actionMonitoring blind spot

   Detection

   The team saw the Basecamp 3 primary database become completely congested within 30 seconds of 13:00 UTC and responded immediately.

   Remediation

   • Paused and disabled parts of Basecamp 3, such as delaying progress report emails
   • Failed over from the primary database to a replica, which brought Basecamp 3 back until it tipped over again
   • Put Basecamp 3 into maintenance mode
   • Set hard limits on database query time
   • Brought features back online step by step with the query limits in place

   Action items

   • Keep hard limits on database query time so slow queries fail instead of piling up
   • Tune the limits on concurrency
   • Treat top-of-the-hour database load spikes from scheduled batches as unacceptable
   • Reduce the number of parallel workers for some scheduled jobs
   • Let some scheduled activities run over several minutes instead of all at once
   • Move bursty queries from the primary database to read-only replicas
   • Fix the quiet performance regressions found in a review of the past year of database metrics
   • Find and spread out other bursty activities that concentrate load on the database
   • Tune the database for more predictable behaviour under heavy load and high concurrency
   • Add observability into the database's internal locking and transaction states
   • Build tools to switch off high-load Basecamp 3 features selectively during an incident
   • Run operational fire drills for a wedged database
   • Evaluate upgrading the database software and hardware
   • Set service level objectives for the components behind Basecamp 3
   • Hold quarterly performance and load reviews to catch slow, subtle changes

   Lessons

   A system running just past its limits can flip from stable to unstable without any particular trigger, and steady growth plus small regressions that raised no alarm had brought Basecamp 3's primary database to that point. Under lock contention, terminating queries does not relieve the database, because rolling back their transactions waits on the same locks. The team struggled to respond with confidence because there was no single clear cause and little visibility into the database's internals.

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4. IL-0486 Figma's PostgreSQL 9 database on RDS slowed under high write IOPS and lock contention until the site was unavailable

   Figma2020

   Symptoms

   On January 22, 2020 at 9:45 AM PST, Figma's database metrics showed rising write IOPS and lock contention, while database CPU was lower than normal. An automated alert followed at 10:00 AM, and by 11:42 AM enough API requests had queued up behind the database that Figma became unavailable for some users.

   Lock contention or deadlockDatabase overloadService outage

   Trigger

   An automatic ANALYZE on one of the tables in the query finished at exactly the moment the incident began. The new statistics pushed the planner's row estimate for an inner subquery past 200 rows, and the plan for the whole query switched to an estimate of over 20 million rows.

   Root cause

   Software defect

   Component

   DatabasePostgreSQL vacuum

   Contributing factor

   Overlapping failuresHarmful manual action

   Detection

   Database metrics showed higher write IOPS and lock contention at 9:45 AM PST, and an automated alert at 10:00 AM started the investigation.

   Remediation

   • Cancelled several inessential queries and increased the allocated IOPS
   • Cancelled autovacuum statements running on the largest tables
   • Changed autovacuum_freeze_max_age and restarted the database to stop aggressive autovacuuming
   • Took the service down for emergency maintenance and upgraded the database to PostgreSQL 11

   Action items

   • Revisit transaction timeouts and limits so long-running and expensive queries are terminated
   • Improve metrics, monitoring and database settings for autovacuuming, locking and query performance
   • Audit queries for unnecessary or excessive joins and subqueries that could trip up the query planner
   • Bring the performance tools of the upgraded PostgreSQL into the incident response plans
   • Audit the structure and access patterns of data stored in PostgreSQL and consider moving some of it to datastores suited to those patterns
   • Improve in-app messaging about unexpected maintenance windows
   • Move towards online database upgrades that do not need downtime
   • Let users keep working on already open files safely while offline, even after closing the browser

   Lessons

   Having upgraded staging to PostgreSQL 11, tested it for months and written a production upgrade plan let the team upgrade under poor conditions with confidence. A routine statistics refresh can flip a query to a catastrophic plan, and the same query's plan can differ widely over time, so comparing plans from snapshots taken at different dates exposed the cause.

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