Cases4 cases

1 to 4 of 4

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-0308 Most RevenueCat backend systems failed after cutover to a new Aurora PostgreSQL 14 cluster as queries became very slow

   RevenueCat2022

   Symptoms

   At 9:34 UTC on November 23, 2022, RevenueCat started moving traffic from its Aurora PostgreSQL 10.x cluster to a new Aurora PostgreSQL 14.x cluster. By 9:39 UTC all traffic was on the new cluster, latency was bad, and the RevenueCat dashboard and the endpoints used by the RevenueCat SDK stopped working.

   Database overloadHigh latencyService outageConnection pool exhaustion

   Trigger

   At 9:34 UTC production traffic was switched from the Aurora PostgreSQL 10.x cluster to the new Aurora PostgreSQL 14.x cluster, before ANALYZE had run on its largest tables.

   Root cause

   Breaking change in an upgrade

   Component

   DatabasePostgreSQL vacuumDatabase replicationDatabase proxy and connection poolerQuery planner and table statistics

   Contributing factor

   Unrepresentative stagingUnverified assumptionAutomation amplificationMonitoring blind spot

   Detection

   At 9:39 UTC, with all traffic on the new cluster, latency looked bad and the infrastructure team began to investigate.

   Remediation

   • Added a statement timeout to the database user so queries could not hold a connection too long
   • Shed 90% of traffic by returning 503s from the load balancer
   • Dropped indexes that the planner was using wrongly and that were not needed
   • Stopped accepting Apple webhooks for a time
   • Rolled out a release without New Relic to stop its EXPLAIN queries
   • Ran ANALYZE by hand on the biggest tables
   • Raised the values of the affected primary key sequences with a prepared script
   • Stopped the integration event dispatcher by hand
   • Replayed the webhooks that were dispatched but not recorded
   • Restored integration event dispatching, ETLs and other non-web server jobs

   Action items

   • Consider change freezes during business-critical periods
   • Explain better how the SDK behaves during backend outages
   • Tell customers about maintenance and outages in the developer dashboard and by direct email to technical contacts
   • Replay a subset of write traffic in migration dry runs to mimic the cutover
   • Move costly processes to asynchronous work to keep the API fast and stable
   • Re-evaluate table indexes, including unused ones the database may choose for queries
   • Show the load of each endpoint and system in the database dashboards
   • Involve support and customer communications staff during outages

   Lessons

   The new cluster had not been analyzed, and the tests missed it because the test cluster had been up long enough to gather statistics. Dry runs replayed no write traffic, on the belief that writes are simpler than reads, and the team assumed sequence state was replicated with the data. Rolling back at the start would have been better than fixing forward. The migration, timed to finish before Thanksgiving and Black Friday, should have been delayed until the week after, because the US had entered a sensitive period and part of the US support team was already on holiday.

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3. IL-0443 A Repro Aurora MySQL reader restarted unexpectedly and writer insert latency rose about sevenfold for a day or more

   Repro2019

   Symptoms

   A reader of Repro's Aurora MySQL cluster restarted for no known reason, and right after that the writer's insert latency rose to about seven times normal and writer performance stayed badly degraded for a long time. This happened more than once, twice within two weeks by 11 November 2019. While the writer was slow, the applications that process data collected by the Repro SDK fell behind, and the team could only watch until the writer recovered.

   High latencyReplication lagThroughput degradation

   Trigger

   Long-running queries on the reader; the author recalls that the cause was a query sent to the reader from Redash.

   Root cause

   Capacity exhaustion

   Component

   DatabaseDatabase replication

   Contributing factor

   Monitoring blind spot

   Remediation

   • Waited for writer performance to return, since the rollback segment release could not be sped up

   Action items

   • Add a monitor on the rollback segment, mysql.innodb.history_list_length in Datadog
   • Kill queries that run long enough to make the reader restart as soon as they are found
   • Set max_execution_time so queries are stopped after a fixed time

   Lessons

   Once the rollback segment has grown large, nothing speeds up its release across the cluster, so the way to avoid the latency is to keep transactions small and stop long-running queries before the rollback segment grows.

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4. IL-0296 Transloadit's us-east Redis cluster lost queued jobs and heartbeats, raising WORKER_JOB_ERROR errors for 81 customers

   Transloadit2016

   Symptoms

   Reports came in of more WORKER_JOB_ERROR errors, which tell Transloadit there is a communication problem between its Uploaders, the machines that receive files and orchestrate Assemblies, and its Drones, the machines that run encoding tasks. Many Assemblies became much slower because retries with exponential back-off kicked in, and a few could not be replayed.

   High error rateHigh latencyLoad imbalance

   Trigger

   A steep increase in HLS traffic, together with more Drones online processing the same volume faster, which put more load on the central Redis nodes.

   Root cause

   Capacity exhaustion

   Component

   In-memory cacheDatabase replication

   Contributing factor

   Monitoring blind spotUnverified assumptionHarmful manual action

   Detection

   Reports of an increased number of WORKER_JOB_ERROR errors.

   Remediation

   • Re-added lost messages and relaxed heartbeat monitoring as a stopgap
   • Upgraded Redis to the latest stable version
   • Doubled the Redis cluster to six nodes and then added three more, which made it worse
   • Brought the number of read replicas down to one, which cut traffic and failure rates
   • Capped how far Drones scale up in each region

   Action items

   • Add logging and alarms for approaching saturation
   • Find ways to decentralise the traffic between the Redis nodes and the Drones and Uploaders
   • Add sharding per Uploader on top of the sharding per region

   Lessons

   To handle more traffic Transloadit had to use fewer Redis nodes, not more: each added replica multiplied the replication traffic from the single leader that takes every write, so scaling up the number of nodes was the wrong call. Bandwidth had not been among the monitored Redis metrics.

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