Cases3 cases

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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.

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2. 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.

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3. IL-0337 EBS volumes in one US East Availability Zone got stuck after a network change cut EBS nodes off from their replicas

   Amazon Web Services2011

   Symptoms

   From 12:47 AM PDT on 21 April 2011, a subset of Amazon EBS volumes in one Availability Zone of the US East Region could no longer serve reads or writes, and EC2 instances that tried to use them got stuck as well. AWS called these "stuck" volumes. EBS API calls across the whole US East Region also returned high error rates and latencies during two periods on the first day.

   Storage disconnected or stale mountsNetwork unreachableControl plane lagHigh error rateFailover failureData loss

   Trigger

   During a routine network change to upgrade the capacity of the primary EBS network in one Availability Zone, traffic was to be shifted off one of the redundant routers. The shift was executed incorrectly and sent the traffic onto the lower-capacity replication network instead of the other primary router, which could not carry it.

   Root cause

   Operator error

   Component

   Cloud block storageDatabase

   Contributing factor

   Automation amplificationRetry stormNo headroomExcessive blast radiusSignal buried in noise

   Remediation

   • Rolled back the incorrect traffic shift, which restored network connectivity
   • Disabled all new Create Volume requests in the affected Availability Zone at 2:40 AM PDT
   • Disabled all communication between the degraded EBS cluster and the EBS control plane from 8:20 AM PDT
   • Stopped EBS servers in the degraded cluster from futilely contacting other servers for free space
   • Changed the EBS control plane so new EBS-backed instance launches stopped failing
   • Moved spare server capacity physically from across the US East Region into the degraded cluster
   • Adjusted the negotiation throttles so the new servers could join without flooding the old ones
   • Built a separate EBS control plane partitioned to the affected zone and finer-grained throttles, then processed the backlog
   • Restored the remaining volumes from S3 snapshots taken early in the event
   • Restored volumes on failed machines that had no snapshot after forensic work
   • Intervened by hand on the RDS multi-AZ instances that did not fail over automatically

   Action items

   • Audit the change process and add automation to prevent the traffic shift mistake
   • Change capacity planning and alarming to keep the extra safety capacity needed for large recovery events
   • Change the retry logic in EBS nodes to back off harder and reconnect to previous replicas instead of searching for new nodes
   • Test and deploy the fix for the race condition that crashed EBS nodes
   • Fix the RDS bug that kept multi-AZ instances from failing over automatically
   • Improve the EBS control plane timeout logic to prevent thread exhaustion when one zone's cluster is slow
   • Make the EBS control plane aware of Availability Zones so it sheds load when over capacity
   • Move more EBS control plane functions into per-cluster services that run in the same zone as the cluster
   • Offer multiple Availability Zones to Amazon VPC customers
   • Provide better tools for building multi-AZ applications that survive the loss of a zone
   • Host free webinars and publish whitepapers on fault-tolerant architecture
   • Build the fine-grained control and throttling used in recovery into the EBS nodes and automate the recovery models
   • Look at changes that keep volumes working while a cluster is degraded, including the ability to take a snapshot of a stuck volume
   • Send more frequent and detailed customer updates and staff developer support more widely during events
   • Develop tools that show through the APIs whether instances are impaired

   Lessons

   AWS writes that with more spare capacity the cluster would have absorbed the re-mirroring requests without a storm, and that it now understands how much capacity large recovery events need. The event also showed that the EBS control plane let a failure in one Availability Zone affect the others, so more work is needed to make zones behave as if completely independent. AWS adds that some customers had critical components in a single zone, and that multi-AZ deployments need to become easier to design and run.

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