Cases5 cases

1 to 5 of 5

1. IL-0380 A compaction change in InfluxDB Cloud us-east-1 dropped data from compacted files, and queries missed data for weeks

   InfluxData2026

   Symptoms

   From 9 February 2026, InfluxDB Cloud customers on the AWS us-east-1 cluster prod101-us-east-1 found that some recent data did not show up in query responses. The first support ticket about data availability came in on 9 February, the day a compaction change was deployed, and more tickets followed on 10 February.

   Data lossResponse quality degradationHigh latency

   Trigger

   On 9 February 2026 InfluxData deployed to prod101-us-east-1 a compaction change that runs several compaction jobs in parallel on one partition, each on a subset of the data chosen by org id prefix.

   Root cause

   Faulty deployment

   Component

   DatabaseDatabase backups and snapshots

   Contributing factor

   Unsafe failure behaviourMissed in reviewGap in automated testsUnrepresentative staging

   Detection

   Tickets about data availability were raised, the first on 9 February and more on 10 February, when the status page reported that some recent data was not showing up in query responses.

   Remediation

   • Rolled back the compaction change
   • Restored recent data first, rebuilt from the Kafka logs
   • Changed the restoration process to make historical data recovery more efficient
   • Restored historical data from the weekly TSM snapshots
   • Kept the cluster online and restored data incrementally so ingest and queries could continue

   Action items

   • Expand test coverage for changes to critical storage components such as the compactor, with scenarios closer to production-scale workloads
   • Strengthen safeguards around compaction so failures are detected and handled safely
   • Review restoration workflows and procedures to reduce elevated TTBR and slow queries during large restores
   • Revisit the backup and restore strategy to speed up cluster-wide restorations

   View case
2. IL-0268 AdGuard DNS resolvers in Amsterdam, Frankfurt and London overloaded and answered with timeouts and SERVFAIL

   AdGuard 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.

   DNS resolution failuresIntermittent timeoutsHigh latencyMemory pressure

   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

   Faulty deployment

   Component

   Recursive DNS resolver serviceLanguage runtime settings

   Contributing factor

   Dependence on a warm cacheRetry stormHarmful manual actionGap in automated tests

   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.

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3. IL-0226 Azure Front Door edge servers crashed worldwide on incompatible configuration metadata, causing timeouts and DNS errors

   Microsoft Azure2025

   Symptoms

   From 15:41 UTC on 29 October 2025, customers and Microsoft services using Azure Front Door (AFD) and Azure Content Delivery Network (CDN) got connection timeout errors and DNS resolution issues. Monitoring alerts started the investigation at 15:48 UTC.

   Intermittent timeoutsDNS resolution failuresService outageHigh latency

   Trigger

   At 15:35 UTC a specific sequence of valid customer configuration changes, made across two different AFD control plane build versions, produced incompatible customer configuration metadata that was deployed to the edge sites.

   Root cause

   Software defect

   Component

   CDN and edge networkAuthoritative DNS servers and records

   Contributing factor

   Gap in automated testsChange with a delayed effectIneffective rollbackExcessive blast radius

   Detection

   The configuration protection system detected the data plane impact and blocked configuration changes at 15:43 UTC, and monitoring alerts started the investigation at 15:48 UTC.

   Remediation

   • The configuration protection system automatically blocked all new and in-flight configuration changes worldwide
   • Removed the problematic customer configurations from the latest LKG snapshot by hand
   • Blocked all customer configuration changes from propagating to the data plane
   • Deployed the updated LKG configuration across the global fleet
   • Moved Azure Portal away from Azure Front Door
   • Rebalanced traffic by hand to a few healthy edge sites, then switched back to automatic traffic management
   • Blocked all AFD customer configuration changes at the Azure Resource Manager level until safeguards were in place

   Action items

   • Fix the control plane incompatibility and the data plane bug
   • Process each customer configuration completely and synchronously before production stages
   • Add stages to the phased configuration rollout, with longer bake time between them
   • Move configuration processing on data plane servers into isolated worker processes
   • Reduce configuration propagation time from 45 minutes to about 15 minutes
   • Test backwards compatibility with configurations generated by earlier control plane builds
   • Move critical first-party infrastructure to an active-active setup with fail away
   • Isolate customer configuration and traffic with micro cell segmentation of the AFD data plane
   • Restore customer configurations within one hour by using the local configuration cache
   • Cut data plane recovery further, to about 10 minutes
   • Remove delays in Azure Service Health alerts by raising resource thresholds
   • Extend automated Azure Service Health alerts to similar classes of service degradation
   • Fail over support request systems faster so Premier and Unified customers can always open requests

   Lessons

   Microsoft describes configuration propagation risk as inherent to a global Content Delivery Network, in which route, WAF and origin changes must be deployed quickly worldwide. Both this incident and the 9 October AFD incident came from that risk. This time the incompatible metadata passed a protection system that advances only on healthy data plane signals, because the crash came later from delayed asynchronous processing. Some learnings and repair items from the earlier incident applied here as well. Microsoft advises customers to build a resilient global HTTP ingress layer that stays available during regional or network disruptions, to manage resources through the REST API or PowerShell when management portals are unavailable, and to configure Azure Service Health alerts so the right people hear about service issues.

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4. IL-0328 A field type change in the database of CircleCI's job distribution service left all jobs stuck in "not running"

   CircleCI2021

   Symptoms

   From about 18:50 to 19:43 UTC on Monday, November 8, 2021, CircleCI customer jobs on all executors were blocked in a "not running" state. Errors and alerts appeared right after a deploy that changed a field type in the database of the job distribution service.

   Service outageHigh latencyScale-up failureCloud API rate limit exhausted

   Trigger

   At 18:50 UTC a deploy changed the type of a field in the PostgreSQL database of the job distribution service, in a way that was not backwards compatible.

   Root cause

   Faulty deployment

   Component

   DatabaseNode pool

   Contributing factor

   Ineffective rollbackExcessive blast radiusGap in automated tests

   Detection

   Errors and alerts related to the distribution failure appeared immediately after the 18:50 UTC deploy.

   Remediation

   • Rolled back the field type change, which did not clear the errors
   • Wrote, built and manually deployed a change that made the distribution system ignore the field
   • Manually scaled the primary compute cluster for the expected thundering herd of work
   • Monitored until processing was normal and resolved the incident

   Action items

   • Fix the gaps found in the testing strategy
   • Change how the distributor reacts to unexpected data so that this kind of deploy should not affect all jobs
   • Make recovery faster when unforeseen failures occur

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5. IL-0338 Google Cloud HTTP(S) and TCP/SSL Proxy load balancers returned 502s or reset connections on 33% to 87% of requests

   Google Cloud2018

   Symptoms

   From 12:17 to 12:49 PDT on 17 July 2018, Google Cloud HTTP(S) Load Balancers answered a share of requests with 502 return codes, and connections to TCP/SSL Proxy Load Balancers were reset. The share of failed requests moved between 33% and 87% during those 32 minutes, and automated monitoring alerted Google engineers at 12:19.

   High error rateConnection resetsHigh latency

   Trigger

   A configuration change in the production environment intermittently set off a bug in a new feature of the second-layer Google Front Ends.

   Root cause

   Software defect

   Component

   Cloud load balancerCDN and edge network

   Contributing factor

   Gap in automated testsExcessive blast radius

   Detection

   Automated monitoring alerted Google's engineering team at 12:19 PDT, two minutes after the 502s began.

   Remediation

   • Reverted the configuration change at 12:44 PDT, which stopped the GFE restarts

   Action items

   • Add safeguards that disable features not yet in service
   • Harden the GFE testing stack against latent bugs in production binaries that make a task restart
   • Isolate the shards of GFE pools further from each other to reduce the scope of failures
   • Build a consolidated dashboard of all configuration changes for GFE pools

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