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-0213 Roblox was down for 73 hours because key-value writes on its single Consul cluster slowed from under 300 ms to 2 seconds

   Roblox2021

   Symptoms

   On the afternoon of 28 October 2021 (13:37 PST), Vault performance at Roblox degraded and a single Consul server showed high CPU load. Consul cluster metrics showed that writes to its key-value (KV) store had a 50th percentile latency of 2 seconds instead of the usual under 300 ms. Players were not yet affected.

   High latencySustained high node CPUService outageMonitoring outage

   Trigger

   On 27 October at 14:00, one day before the outage, Roblox enabled the Consul streaming feature on the backend service responsible for traffic routing and, to prepare for end-of-year traffic, added 50% more nodes to traffic routing.

   Root cause

   Software defect

   Component

   Coordination and key-value storeService discoveryIn-memory cache

   Contributing factor

   Single point of failureOverlapping failuresMonitoring blind spotHarmful manual actionChange with a delayed effect

   Detection

   Roblox engineers began investigating when Vault performance degraded and a single Consul server had high CPU load, before players were affected. Consul cluster metrics then showed elevated KV write latency.

   Remediation

   • Replaced one Consul cluster node on the suspicion of degraded hardware
   • Replaced all Consul nodes with 128-core machines with faster NVMe SSDs
   • Shut down the Consul cluster and reset its state from a snapshot taken at the start of the outage, with access blocked by iptables
   • Blocked external traffic, disabled all non-essential Consul use, scaled services down and lowered health check frequency from 60 seconds to 10 minutes
   • Moved the Consul cluster back to 64-core servers
   • Disabled the streaming feature for all Consul systems, which brought KV write latency back to 300ms
   • Kept the slow Consul servers from staying elected as leader
   • Redeployed the caching system and fixed its wrong scheduling data and failed deployments
   • Restarted the stopped services at the right capacity levels
   • Let players back in with DNS steering in roughly 10% steps while checking database load, cache performance and stability

   Action items

   • HashiCorp builds laboratory benchmarks and scale tests that reproduce the streaming contention
   • HashiCorp improves the design of streaming so it does not contend under extreme load
   • Compact the BoltDB database with existing BoltDB tooling
   • Remove the circular dependency between the telemetry systems and Consul
   • Extend telemetry with targeted alerts on Consul and BoltDB performance and with visibility into traffic between services and Consul
   • Run the backend in an additional, geographically distinct data center and in multiple availability zones
   • Split critical services into their own dedicated Consul clusters
   • Move data that core services keep directly in the Consul KV store to a more suitable storage system
   • Delete obsolete KV data from Consul
   • Upgrade to a Consul version that replaces BoltDB with bbolt, in Q1
   • Redesign cache deployment so the cache system can be brought up quickly from a standing start
   • Deploy Nomad enhancements that make it easier to turn up large jobs after a long period of unavailability
   • Use mechanisms for faster machine configuration changes
   • Reintroduce streaming only after a new implementation has been tested at Roblox's scale and workload

   Lessons

   Two mostly unrelated problems deep inside Consul, contention in streaming under very high read and write load and BoltDB freelist growth, were hard to diagnose, and that difficulty was the main reason the outage lasted so long. A single Consul cluster serving every workload made the impact larger, and monitoring that depended on Consul hid the cause when it was needed most. Faster hardware did not help and may have hurt, because the contention was worse on NUMA machines. HashiCorp had benchmarked streaming at a similar scale, but the problem needed both a large number of streams and a high churn rate. Restarting a platform from a fully stopped state, including warming caches, took a long time and needed tools built for bootstrapping rather than incremental change.

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3. IL-0430 Mackerel's Redis stopped answering during a replication setup, keepalived failed over by mistake, and Mackerel degraded

   Hatena2018

   Symptoms

   On 26 September 2018 at 10:51 JST the whole Mackerel monitoring service stopped working properly and its alive monitoring stopped. The application servers could not connect to Redis, which stores Mackerel's monitoring data, and could not return correct responses.

   Monitoring outageHigh latencyIntermittent timeouts

   Trigger

   Redis memory use had been growing, so operators set up replication in order to scale Redis up. Building the replication took a long time and Redis stopped answering for a while, and keepalived treated this as a node failure and started a failover nobody intended.

   Root cause

   Configuration change

   Component

   In-memory cacheDatabase replicationDatabase failover manager

   Contributing factor

   Automation amplificationOverlapping failures

   Remediation

   • Switched back to the correct Redis and briefly saw the application return
   • Restarted the application servers one after another, which did not improve the symptoms
   • Detected the organizations posting inappropriate metrics and blocked their requests
   • Adjusted the timeout interval of the application servers
   • Temporarily added capacity to Diamond, the TSDB
   • Increased the number of Kinesis shards and Redis Cluster shards
   • Scaled out the API application servers
   • Put Mackerel in maintenance mode, checked it internally and let outside requests back gradually

   Action items

   • Raise the number of keepalived health checks for Redis so a brief load increase does not trigger a failover
   • Scale Redis up
   • Add more application servers
   • Change the application to store only the monitoring data it needs in Redis, reducing Redis memory use
   • Build a way to detect inappropriate API requests quickly and show them on a dashboard
   • Build a function to block organizations making inappropriate requests quickly
   • Keep improving the accuracy of detecting inappropriate requests and consider adding features such as an API limit
   • Strengthen monitoring of the application servers' internal processing so problems are handled before they become outages

   Lessons

   Hatena did not find why recovery took so long, but expects that keeping failover from reacting to short load spikes and giving the application more performance headroom would prevent an outage this long. It also aims to keep problems contained to a small part of the service when a failure does happen.

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