Cases9 cases

1 to 5 of 9

1. IL-0324 Three MediaWiki deployments lost Memcached after a PHP 8.1 rollout and overloaded the External Storage database cluster

   Wikimedia Foundation2025

   Symptoms

   On 2025-03-12 from 10:58 UTC, alerts for high backend response times came in, and the MediaWiki deployments mw-api-int, mw-parsoid and mw-jobrunner, which serve MediaWiki for non-user-facing purposes, showed high latency and database errors. The External Storage cluster, whose hosts store the compressed text of wiki page revisions, had a sharp rise in load and connections. The same happened again on 2025-03-13 from 11:15 UTC.

   Database overloadHigh latencyHigh error rateLog floodMonitoring outage

   Trigger

   At 10:44 UTC on 12 March a Scap deployment of the PHP 8.1 rollout for mw-api-int, mw-parsoid and mw-jobrunner was run with the flag -Dbuild_mw_container_image:False; the rollout was deployed with Scap again at 11:15 UTC on 13 March.

   Root cause

   Faulty deployment

   Component

   DatabaseIn-memory cacheConfigMaps and SecretsHelmCI system

   Contributing factor

   Unverified assumptionDependence on a warm cacheSignal buried in noise

   Detection

   Alerts for high backend response times started at 10:58 UTC on 12 March, 14 minutes after the Scap deployment; 2 on-call engineers were paged. The MediaWikiMemcachedHighErrorRate alert that pointed at the cause fired only briefly because its log pipeline was overloaded.

   Remediation

   • Deployed a patch to cirrus-streaming-updater that reduced SUP parallelism
   • Reduced the concurrency of the categoryMembershipChange job in changeprop-jobqueue
   • On 12 March, reverted 1126607 and 1126650 and re-ran Scap
   • On 13 March, rolling-restarted changeprop-jobqueue
   • Rolling-restarted mw-api-int
   • Disabled the categorymembership job in changeprop-jobqueue
   • Reverted 1127476 and 1127478
   • Redeployed mw-api-int to pick up the revert
   • Increased the number of mw-api-int replicas
   • Redeployed mw-jobrunner to pick up the revert
   • Redeployed mw-parsoid to pick up the revert

   Action items

   • Reduce the number of messages sent through channel:Memcached during failures
   • Add helm rollback functionality to Scap
   • Decide whether Scap should update helmfile-defaults when -Dbuild_mw_container_image:False is set
   • Consider removing envvars.inc from MediaWiki images
   • Consider "Why" dashboards that give an overview of a group of related components together with relevant links to follow
   • Coordinate incident responders better
   • Be able to work with MediaWiki developers during incidents

   Lessons

   The write-up puts the weight not on the technical root cause but on why the team could not see, even after the 12 March incident, that three MediaWiki deployments were running without Memcached. Alert dashboards showed what was happening, such as many busy PHP workers, but not why; dashboards linking components that affect each other, such as SQLBlobStore keys and External Storage, would have helped. The alert that pointed in the right direction did not stay firing and would probably have been lost among 78 others. The incident coordinator could not tell who was working on what or who had joined or left, the channels were noisy, the external impact was judged more critical than it was, and trace.wikimedia.org was not used although it might have helped.

   View case
2. 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.

   View case
3. IL-0270 Discord's ScyllaDB authentication cluster overloaded during an OS upgrade, and logins failed for about 50 minutes

   Discord2023

   Symptoms

   On 6 November 2023 at 10:52 AM PST, when an upgrade script drained all connections from zone [c] of the scylla-authentication ScyllaDB cluster, Discord's availability, measured as the share of HTTP requests that return a 200-level response, fell at once to about 25%. Users could not connect, log in, read or send messages.

   Service outageDatabase overloadHigh latency

   Trigger

   During an OS upgrade that takes one zone of a ScyllaDB cluster offline at a time, the upgrade script drained all connections from zone [c] of scylla-authentication at 10:52 AM, while one node in the just-upgraded zone [b] had a degraded NVMe Local SSD.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   No headroomOverlapping failuresDependence on a warm cacheUnverified assumptionIgnored early signalRetry storm

   Detection

   An incident response call had already started when the first alerts fired at 10:56 AM, four minutes after the start. The time spent by downstream service graph showed the authentication service spending far more time on user requests than normal, and the authentication alerts fired moments later.

   Remediation

   • Turned off sending typing events and marking messages as read to shed load from the authentication cluster
   • Waited for the zone [c] RAID rebuild instead of restarting nodes, and the two zone [c] nodes came back online at 11:41 AM
   • Restarted the two zone [b] nodes one at a time

   Action items

   • Double the size of the scylla-authentication cluster so each node takes half as many requests
   • Rebuild scylla-authentication-b-2 with new, non-degraded disks
   • Build a way to detect disk degradation that is too subtle to affect overall latency but still threatens reliability
   • Move upgrade operations into a more automatic control plane for the Scylla clusters that analyses cluster state, such as cache hit rate
   • Increase the redundancy of the scylla-authentication cluster; the options being explored, not yet decided, are a higher replication factor or a hot-standby cluster in another region

   Lessons

   Taking a whole zone out of a three-zone cluster leaves no margin, so one additional slow node in another zone was enough to overload it. For some Scylla clusters the row cache hit rate of restarted nodes must reach a safe level before the next zone goes down, and a short zonal test beforehand gave false confidence. The small but critical authentication cluster needs to be run differently from Discord's much larger Scylla clusters, with more redundancy.

   View case
4. 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.

   View case
5. IL-0308 Most RevenueCat backend systems failed after cutover to a new Aurora PostgreSQL 14 cluster as queries became very slow

   RevenueCat2022

   Symptoms

   At 9:34 UTC on November 23, 2022, RevenueCat started moving traffic from its Aurora PostgreSQL 10.x cluster to a new Aurora PostgreSQL 14.x cluster. By 9:39 UTC all traffic was on the new cluster, latency was bad, and the RevenueCat dashboard and the endpoints used by the RevenueCat SDK stopped working.

   Database overloadHigh latencyService outageConnection pool exhaustion

   Trigger

   At 9:34 UTC production traffic was switched from the Aurora PostgreSQL 10.x cluster to the new Aurora PostgreSQL 14.x cluster, before ANALYZE had run on its largest tables.

   Root cause

   Breaking change in an upgrade

   Component

   DatabasePostgreSQL vacuumDatabase replicationDatabase proxy and connection poolerQuery planner and table statistics

   Contributing factor

   Unrepresentative stagingUnverified assumptionAutomation amplificationMonitoring blind spot

   Detection

   At 9:39 UTC, with all traffic on the new cluster, latency looked bad and the infrastructure team began to investigate.

   Remediation

   • Added a statement timeout to the database user so queries could not hold a connection too long
   • Shed 90% of traffic by returning 503s from the load balancer
   • Dropped indexes that the planner was using wrongly and that were not needed
   • Stopped accepting Apple webhooks for a time
   • Rolled out a release without New Relic to stop its EXPLAIN queries
   • Ran ANALYZE by hand on the biggest tables
   • Raised the values of the affected primary key sequences with a prepared script
   • Stopped the integration event dispatcher by hand
   • Replayed the webhooks that were dispatched but not recorded
   • Restored integration event dispatching, ETLs and other non-web server jobs

   Action items

   • Consider change freezes during business-critical periods
   • Explain better how the SDK behaves during backend outages
   • Tell customers about maintenance and outages in the developer dashboard and by direct email to technical contacts
   • Replay a subset of write traffic in migration dry runs to mimic the cutover
   • Move costly processes to asynchronous work to keep the API fast and stable
   • Re-evaluate table indexes, including unused ones the database may choose for queries
   • Show the load of each endpoint and system in the database dashboards
   • Involve support and customer communications staff during outages

   Lessons

   The new cluster had not been analyzed, and the tests missed it because the test cluster had been up long enough to gather statistics. Dry runs replayed no write traffic, on the belief that writes are simpler than reads, and the team assumed sequence state was replicated with the data. Rolling back at the start would have been better than fixing forward. The migration, timed to finish before Thanksgiving and Black Friday, should have been delayed until the week after, because the US had entered a sensitive period and part of the US support team was already on holiday.

   View case

By root cause5