Cases8 cases

1 to 5 of 8

1. IL-0561 DigitalOcean's Cloud Control Panel and Public API returned up to 25% errors in two windows as authorization timed out

   DigitalOcean2026

   Symptoms

   From 16:56 UTC on 24 August 2026, requests to DigitalOcean's Cloud Control Panel, Public API and the product management systems that depend on them failed at elevated rates. Automated monitoring flagged abnormal error rates and latency at 16:58. Running Droplets, network routing and block storage Volumes kept working.

   High error rateHigh latency

   Trigger

   A planned migration of the database layer behind DigitalOcean's identity, authentication and authorization services made one critical authorization query run much slower than its historical baseline.

   Root cause

   Configuration change

   Component

   DatabaseIdentity and access service

   Contributing factor

   Retry stormHarmful manual action

   Detection

   Automated monitoring detected abnormal error rates and latency at 16:58 UTC, two minutes after the authorization query slowed down.

   Remediation

   • Rerouted authorization traffic onto a single healthy processing path
   • Temporarily disabled hedged retries for the primary callers
   • Disabled the retry and amplification behaviour across the remaining affected services
   • Added read capacity for the slow authorization query

   Action items

   • Deploy circuit breakers in upstream services that detect a degraded downstream dependency and suppress hedged requests and retries
   • Audit the architecture so retry suppression and circuit breaking are enforced the same way in every control plane service
   • Add fast-fail load shedding so request queues do not saturate while a dependency is slow
   • Add telemetry alerts that spot queues backing up before customers are affected
   • Let critical authorization components run in a degraded mode on asynchronous, eventually consistent database replicas when the primary database is slow

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2. IL-0498 Clerk's Postgres database had load spikes every 15 minutes and requests failed and slowed intermittently for four days

   Clerk2025

   Symptoms

   At 13:08 UTC on 15 September 2025, Clerk's internal monitoring alerted on significant request failures and increased latency. Over the following days the request failures and latency spikes kept coming back while the Postgres database stayed unhealthy, with repeated spikes in database load.

   High error rateHigh latencyDatabase overloadConnection pool exhaustion

   Trigger

   At 05:30 UTC on 14 September 2025 the cloud provider automatically upgraded Clerk's Postgres database to a new minor version, which changed how Postgres handled connection locks.

   Root cause

   Breaking change in an upgrade

   Component

   DatabaseDatabase proxy and connection pooler

   Contributing factor

   Unpinned versionRetry stormMonitoring blind spotOverlapping failuresHarmful manual action

   Detection

   Internal monitoring alerted on significant request failures and increased latency at 13:08 UTC on 15 September. Shortly after the upgrade on 14 September it had already shown an increased but still acceptable database load.

   Remediation

   • Optimised several expensive queries, refined critical indexes and re-indexed the database
   • Blocked abusive traffic more aggressively for a time
   • Performed a manual minor version upgrade and increased database capacity
   • Added jitter to the connection pooling configuration so connections no longer recycled in step

   Action items

   • Evaluate other database providers that give more control over upgrades and downgrades
   • Isolate more services from each other, for example Sign Up and Sign In infrastructure from the rest of the system
   • Keep optimising the most costly database queries
   • Evaluate sharding and multi-tenant infrastructure to isolate one application from others

   Lessons

   Clerk's review found that connection metrics sampled every 60 seconds could not show a recycling pattern that lasted seconds and repeated every 15 minutes, that an attack and background jobs with similar periods pulled attention away from the database layer, that the Backend API looked healthy only because it manages far fewer connections, and that the release freeze removed the staggering of connection creation that normal deploys provide.

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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-0302 Peak load on GitHub's mysql1 MySQL cluster maxed out its database proxy's connections and stopped writes in March 2022

   GitHub2022

   Symptoms

   On March 16, 2022 at 14:09 UTC, write operations across GitHub stopped working, including git operations, webhooks, pull requests, API requests, issues, GitHub Packages, GitHub Codespaces, GitHub Actions and GitHub Pages. Under peak-hour load, the database proxy in front of the shared mysql1 MySQL database had reached its maximum number of connections.

   Writes rejectedDatabase overloadHigh error rateFailover failure

   Trigger

   Increased load on mysql1 during peak hours, combined with poor query performance in specific circumstances, pushed the database proxy to its maximum number of connections.

   Root cause

   Capacity exhaustion

   Component

   DatabaseDatabase proxy and connection pooler

   Contributing factor

   Inefficient queryChange during peak hoursHarmful manual actionExcessive blast radius

   Remediation

   • Failed over mysql1 to a healthy replica on March 16
   • Reset the application connections to the new primary on March 17
   • Added an index that fixed the main query performance problem
   • Performed a primary failover on March 22
   • Performed a primary failover on March 23
   • Throttled webhook traffic to reduce load

   Action items

   • Keep throttling webhook traffic at peak load times
   • Audit the load patterns of mysql1 during peak hours and make performance fixes based on the audits
   • Move traffic to other databases to reduce load and speed up failover
   • Review change management, particularly monitoring and changes during high load in production
   • Shard the databases and scale up hardware

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5. IL-0240 Cloudflare API requests failed and the dashboard slowed for hours after the authentication database lost its replicas

   Cloudflare2020

   Symptoms

   On 2 November 2020 the Cloudflare API and dashboard were degraded for six hours and 33 minutes. The success rate of API queries dropped at times to 75%, and the dashboard was up to 80 times slower than normal, while Cloudflare's edge network kept working.

   High error rateHigh latencyDatabase overloadWrites rejectedPacket loss

   Trigger

   At 14:43 UTC a network switch in a control plane data center partly failed: it kept LACP and BGP running but stopped forwarding some packets, and it recovered on its own six minutes later.

   Root cause

   DNS or network fault

   Component

   NIC, cable and switch port linkCoordination and key-value storeDatabase failover managerDatabaseDatabase replication

   Contributing factor

   Automation amplificationNo headroomHarmful manual action

   Detection

   At 14:43 UTC alerts fired because the misbehaving switch was unreachable to pings. Engineers then saw that the primary authentication database was overwhelmed.

   Remediation

   • Shed load from the primary authentication database by slowing SSL certificate pushes to the edge, emails and other features
   • Moved API traffic that could use read replicas to the secondary data center by hand
   • Sent authentication calls fully back to the primary data center when the dashboard got worse
   • Rebuilt the first database replica, which put itself back into service at 21:20 UTC and returned performance to normal
   • Ramped the turned-down services back up so asynchronous processing could catch up

   Action items

   • Remove the cluster management limitation that requires rebuilding all replicas after a primary is promoted
   • Redesign the user sessions system so dashboard traffic can be steered between data centers
   • Change the auto-remediation configuration so a database replica is not promoted so quickly
   • Tune each system involved to be more resilient to failures of its dependencies

   Lessons

   Every system in the chain had redundancy and none failed completely; each only degraded, which made the chain of events much harder to model and foresee. Automating replica promotion so that it could trigger on a minute's notice had been a point of pride, but for at least one database a fast promotion can do more harm than the failure it answers. Most general-purpose cluster managers use Raft or Paxos rather than Byzantine fault tolerant protocols, accepting a rare failure mode in exchange for a protocol that is simpler to implement correctly and debug.

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