Cases2 cases

1 to 2 of 2

1. IL-0379 InfluxDB Cloud queries in Azure West Europe slowed and timed out as storage pods fell behind and kept restarting

   InfluxData2026

   Symptoms

   From the evening of 27 September 2026 (UTC), InfluxDB Cloud customers in Azure West Europe saw slow queries and query timeouts, and a query queue depth alert fired at 22:58 UTC. Writes were accepted the whole time, but new data on some partitions could not be queried right away.

   High latencyIntermittent timeoutsReplication lagProbe failures

   Root cause

   Capacity exhaustion

   Component

   DatabaseDatabase replicationLiveness and readiness probesPod scheduling and placement

   Contributing factor

   Monitoring blind spotAutomation amplification

   Detection

   A query queue depth alert fired at 22:58 UTC on 27 September and an incident was opened.

   Remediation

   • Doubled query-layer capacity
   • Restarted stuck storage pods one at a time
   • Raised the readiness probe timeout and rolled it out fleet-wide
   • Doubled recovery concurrency after a restart
   • Raised the cleanup timeout to 24 hours, which stopped the forced restarts
   • Raised each storage pod's resource request so each host held fewer pods, doubling page cache memory and disk throughput per pod

   Action items

   • Add warning alerts for page-fault rate, cache coverage, disk reads on storage volumes and query abandonment
   • List major page faults in the runbooks as a leading indicator of storage performance degradation, with the relief valves that can be tuned
   • Keep tracking down the storage-engine reference leak
   • Move the disk check out of the health path

   Lessons

   Because the storage process uses little memory and the page cache takes what is left, a page cache too small for the data being queried does not show up as memory pressure on the usual memory metrics. InfluxData now treats major page faults as a leading indicator of storage performance degradation and alerts on cache pressure before customers see it.

   View case
2. IL-0413 LLM inference engines timed out or ran out of resources under load in about 60% of 156 severe incidents at Microsoft

   Microsoft2025

   Symptoms

   In Microsoft's cloud LLM inference service, the inference engines themselves failed in 94 of the 156 high-severity incidents from April to June 2025 (≈60%). Requests timed out or ran into resource exhaustion under load, and callers got HTTP 408, 429 and 5xx errors.

   Intermittent timeoutsHigh error rateHigh latency

   Trigger

   Bursty load, traffic with unusually high sampling parameters and long-running requests pushed the inference engines into resource exhaustion and timeouts.

   Root cause

   Capacity exhaustion

   Component

   Model inference server

   Contributing factor

   Monitoring blind spot

   Detection

   Across the 156 incidents, 115 (≈74%) were auto-detected, meaning health probes or DRI pipelines alerted before customers reported the problem; the others were not.

   Remediation

   • Enabled dynamic pushback at the load balancer with caps on pending tokens and requests
   • Tuned request batching and GPU/CPU replica counts
   • Improved gRPC backoff and error mapping
   • Limited sampling parameters and throttled per client
   • Enabled streaming, tuned request batching and capped pending tokens against engine connection timeouts on long-running requests
   • Increased capacity, routed traffic to healthy endpoints and restarted nodes, the actions applied mostly to inference engine failures

   Action items

   • Optimise serving for long-running requests and high sampling regimes, with connection liveness and GPU capacity-aware routing as the main levers
   • Expand health-probe-driven failover and GPU capacity-aware routing so traffic moves off degraded endpoints on its own
   • Forecast token and throughput growth and automate capacity increase decisions
   • Deploy adaptive alerting and expand engine-level telemetry to close detection gaps

   Lessons

   Hotfixes restore correctness but lengthen time to mitigate, while traffic routing and node rebalancing mitigate faster under overload and timeout patterns. Even with AIOps tooling a meaningful share of incidents still needs manual detection or judgement, so health-probe-driven traffic routing, restarts and capacity increases are the decisions to automate first.

   View case

By root cause1