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IL-0268AdGuard DNS resolvers in Amsterdam, Frankfurt and London overloaded and answered with timeouts and SERVFAILAdGuard DNS2025- Symptoms
Between about 15:30 and 18:00 UTC on 25 November 2025 some AdGuard DNS users saw slow or failed DNS resolution, with timeouts and SERVFAIL responses. Alerts fired for increased DNS error rates and server load, and a significant part of the traffic in several European locations, primarily Amsterdam, Frankfurt and London, had intermittent DNS failures for up to 2.5 hours.
- Trigger
A new version of AdGuard DNS, deployed on 25 November to improve how resolvers cache user data, had a bug that saved only the recently changed subset of user data into the local cache file. The first deployment failed partway on a network issue and left about half the servers on the new version; the second attempt made many resolvers start full synchronizations at once.
- Root cause
- Component
- Contributing factor
- Detection
Alerts for increased DNS error rates and server load fired during the second deployment attempt, between 15:30 and 16:00 UTC.
- Remediation
- Rolled back the buggy version on all servers
- Partially rerouted Amsterdam's traffic to other European locations
- Reduced connection concurrency and request pipeline depth in Frankfurt and London, from 5 to 1 in Frankfurt
- Adjusted limits to the actual hardware capacity
- Repaired corrupted cache files and user data snapshots to stop further full synchronizations
- Normalised the traffic distribution once the rollback was complete
- Action items
- Add automated tests that the full user dataset is correctly written to and restored from the local cache file, including detection of empty or truncated caches
- Add integration tests that simulate resolver startup with an empty or corrupted cache under realistic load
- Standardize server hardware profiles per location, or keep separate configurations per hardware class
- Review and adjust connlimit, cache sizes and connection limits for each standard node type
- Reevaluate and lower pipelining depth where needed
- Reevaluate how connections and goroutines are limited so resource use stays bounded with decent throughput after a connection break
- Monitor Go heap size, GC time, GC cycles per second and per-cluster request latency distributions
- Alert on sustained increases in SERVFAIL rate and request duration while upstreams are healthy
- Refine DNS resolver deployment and rollback procedures to avoid simultaneous cache invalidation across a large part of the fleet
- Document and rehearse playbooks for adjusting pipeline depth, connection limits and connlimit during live incidents
- Lessons
How nodes behaved depended on the interaction between available RAM, Go's garbage collector and the connlimit setting. On 64-GiB nodes a high limit let the heap grow large under heavy concurrency and pipelining, and the garbage collector then spent more and more CPU, a known worst case for high-memory applications in which memory is not exhausted but latency rises and throughput falls. On 32-GiB nodes the same configuration reached practical memory limits sooner because it had not been scaled down. Together with high pipelining and client retries, GC pressure, connection saturation and retries formed a feedback loop.
- 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.
- 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
- Component
- Contributing factor
- 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.
These 2 cases involve In-memory cache and CI system. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.
By root cause1
Faulty deployment2 cases
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