Cases3 cases

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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.

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2. IL-0581 29CM product display servers lost pods and saturated Netty event loops on the first day of the Igu Week sale

   MUSINSA2025

   Symptoms

   Shortly after 29CM's Igu Week sale began on 3 November 2025, the servers for the display domain, which serve search results and product listings, showed anomalies. Some pods went down and stopped taking traffic, and the remaining pods received more traffic than they could handle and saturated their Netty event loops.

   Probe failuresIntermittent connection failuresHigh latency

   Trigger

   Igu Week, 29CM's largest Black Friday sale and its busiest traffic of the year, raised Redis network traffic to about four times normal.

   Root cause

   Capacity exhaustion

   Component

   In-memory cacheLiveness and readiness probes

   Contributing factor

   Unknown limitMonitoring blind spotHealth check tied to a dependency

   Detection

   Anomalies on the display servers, pods going down and Netty event loops saturating, were the first sign. The Redis cause was found from Redis health check failure logs and then the Redis network metrics.

   Remediation

   • Scaled the Redis nodes up from cache.r7g.large to cache.r7g.2xlarge, about twice the baseline network bandwidth

   Action items

   • Extended the monitoring dashboard to show in real time whether network in and out bandwidth exceeds the baseline, with metrics such as aws.elasticache.network_bandwidth_out_allowance_exceeded and aws.elasticache.traffic_management_active
   • Added Datadog alerts that fire when network bandwidth crosses a threshold
   • Added a Caffeine local cache in front of Redis for data that changes rarely
   • Look into compressing cached data with Snappy to reduce Redis network bandwidth
   • Look into storing cached data as Protocol Buffers instead of JSON; measured on the cached Item Document data, Snappy and protobuf together are expected to cut the size by about 73%
   • Keep improving the architecture for traffic growth, building on the cache restructuring, stronger monitoring and read separation already applied

   Lessons

   CPU and memory metrics alone did not show the problem, so Redis needs network monitoring against the node's bandwidth baseline. Burst credits kept the nodes working for about two hours after traffic passed the baseline, which delayed the failure and made its cause slower to find.

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3. IL-0333 A downsized RDS PostgreSQL database maxed out its CPU at US peak and failing ELB health checks took buildkite.com down

   Buildkite2016

   Symptoms

   On 22 August 2016 at 17:21 UTC buildkite.com went offline: nobody could log in, view build logs or read the documentation, although builds kept running and GitHub and Bitbucket pull request statuses kept updating. The Buildkite team in Melbourne was asleep and woke at 21:00 UTC, almost 4 hours later, to emails, tweets and Slack messages saying Buildkite was down.

   Service outageDatabase overloadProbe failuresCloud API rate limit exhausted

   Trigger

   Over a weekend maintenance window, Buildkite downgraded its m4.10xlarge Multi-AZ RDS PostgreSQL database to an r3.2xlarge Multi-AZ instance to cut its AWS bill, and moved to a centralised pgbouncer to pool connections. The first US weekday peak after the change pushed the new instance's CPU to its maximum at about 16:00 UTC.

   Root cause

   Capacity exhaustion

   Component

   DatabaseCloud load balancerLiveness and readiness probesNode launch template

   Contributing factor

   Unverified assumptionHealth check tied to a dependencyAutomation amplificationCircular dependencyPages not deliveredOverlapping failures

   Detection

   New Relic, Pingdom, Datadog, Calibre and CloudWatch raised PagerDuty alerts, but the on-call phone call never came and only silent push notifications arrived. The team noticed at 21:00 UTC when they woke to emails, tweets and Slack messages from users.

   Remediation

   • Moved servers from the Agent API to the dashboard ELB to bring buildkite.com back online
   • Switched the Auto Scaling Groups to EC2 health checks and locked in desired counts
   • Upgraded the database to an instance type that could handle the load, using the aws-cli
   • Built a new AMI that did not depend on buildkite.com/_secret/version and rolled out servers that could self-deploy
   • Removed the centralised pgbouncer and went back to pgbouncer on each host

   Action items

   • Plan load testing after significant infrastructure changes without affecting other users of the system
   • Make the ELB health check return OK when the app answers, without checking database connectivity
   • Consider a separate health check endpoint for Pingdom that checks the database and other dependencies
   • Store the latest deployed revision as a file on S3 for newly launched servers to self-deploy from
   • Test by hand that PagerDuty calls reach every on-call team member
   • Investigate regular company-wide tests of everyone's paging setup
   • Ask PagerDuty about calling members when they go on call or requiring an immediate phone rule

   Lessons

   Buildkite listed six lessons. Watch AWS credits, because billing reports show $0 while credits are spent, so the real cost is hidden and big changes end up rushed. Load test after significant infrastructure changes. Think about what a health check should do and what happens when every server behind an ELB fails it. Do not make new servers look up the deployed revision from the service they are replacing. Make sure every on-call member has the right PagerDuty and phone settings before going on call. When servers churn in AWS, stop health checks and autoscaling, for example with the Auto Scaling feature to suspend and resume processes, so the EC2 request limit is not used up.

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