Cases5 cases

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1. IL-0553 CircleCI's orchestration database filled its transaction log volume, and no workflows or jobs started for 50 minutes

   CircleCI2026

   Symptoms

   On 6 October 2026 CircleCI pipelines were slow to be created and workflows and jobs were slow to start, in short bursts from 13:30 UTC and continuously from 15:30 UTC. From 15:48 to 16:38 UTC no workflows or jobs started at all, and pipelines triggered from 16:02 UTC failed.

   High latencyService outageThroughput degradationNode disk full

   Trigger

   Around 12:40 UTC, on top of the regular workload, anti-wraparound autovacuum runs on several of the largest tables of the orchestration service's database began writing transaction logs faster than the database could archive them.

   Root cause

   Capacity exhaustion

   Component

   DatabasePostgreSQL vacuumCI system

   Contributing factor

   Ignored early signalNo alert on limitSingle point of failure

   Detection

   Monitoring raised short alerts about errors in the orchestration service from 10:02 UTC, each clearing on its own. At 13:35 the alerts came back together with related alerts from several other services, and after they returned around 14:30 and engineers investigated further, an incident was declared at 14:54 UTC.

   Remediation

   • Changed the anti-wraparound autovacuum settings to help it finish faster
   • Moved the transaction logs onto the database's main storage volume, which took the database offline from 15:47 to 16:38 UTC
   • Changed database parameters to stop further maintenance runs from starting
   • Changed a database setting at 18:19 UTC to cut the time each write spent waiting on storage
   • Added database and server capacity to the systems that hand jobs to the execution fleet to clear the backlog faster

   Action items

   • Alert on how full the transaction log volume is and on how far archiving has fallen behind
   • Alert on the database maintenance that drives transaction log growth
   • Review all alerts so that noisy or sensitive alerts do not hide real issues
   • Tune how often and how aggressively autovacuum runs on the database
   • Track the size of the largest tables over time so they stay within safe limits
   • Remove the single transaction log bottleneck by splitting the service across several database instances or moving it to a distributed database
   • Make CircleCI's systems wait for the orchestration service to recover instead of failing jobs after retrying
   • Include specific impact and timing sooner in status page updates during long incidents

   Lessons

   CircleCI says the combination of alerts should have been recognised sooner: brief error alerts that cleared on their own were treated as routine, and nothing alerted on the transaction log volume or on archiving lag. Because every write of the orchestration service went through one database and one transaction log, a backlog in that log slowed every pipeline, workflow and job.

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2. IL-0288 ZooKeeperWait rose to thousands of seconds on three redundant ClickHouse instances, degrading a CDN77 data pipeline

   CDN772026

   Symptoms

   At 13:30 UTC on 9 March 2026, CDN77's data team was alerted to a sudden rise in the ZooKeeperWait metric on the ClickHouse cluster behind one of its large data pipelines. Alerting for this cluster size is set at 10 sec for the low watermark and 20 sec for the high one, and the metric reached thousands of seconds. The same rise appeared on three symmetrically redundant ClickHouse instances in different datacenters, each with its own ZooKeeper instance, and the pipeline stayed degraded for over an hour, with no observable impact on internal users, customers or partners.

   Replication lagThroughput degradation

   Trigger

   From 13:15 UTC two client machines that had been cut off from the network for several months came back and their log shippers sent months of queued, very small batches of health check data with old timestamps, spanning a very large time range.

   Root cause

   Capacity exhaustion

   Component

   DatabaseCoordination and key-value storeDatabase replicationTelemetry agent

   Contributing factor

   Monitoring blind spotUnreviewed defaultsIgnored early signal

   Detection

   An alert on the ZooKeeperWait metric fired at 13:30 UTC, fifteen minutes after the problematic inserts began at 13:15. The cluster's alerting uses a low watermark of 10 sec and a high watermark of 20 sec.

   Remediation

   • Dropped and recreated the one table partitioned by 5 minutes that had not cleared its backlog of parts
   • Broadened that table's partitioning from minutes to hours

   Action items

   • Reject out-of-range timestamps in the root MaterializedView tables
   • Record statistics on rejected rows in a second materialized view
   • Refine the deliberately wide rejection boundaries as data on rejections comes in
   • Keep a minimal test scenario with an insert query whose timestamps span days or months
   • Make a range of observability improvements

   Lessons

   Data structure matters even in a pipeline built around volume: incoherent timestamps can destabilise it as effectively as changes in cardinality. A metric that looks odd and cannot be explained at once should not be set aside under incident pressure; the falling mean insert size was what led to the cause. Means and medians each have their place, and here the mean, not the median, exposed the problem. Partitioning has to be chosen carefully: too few partitions give oversized parts, slow mutations and costly or ineffective TTL, while too many slow column scans, add final merging to aggregating queries and overload the Keeper.

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3. IL-0488 Kit's RDS MySQL write latency rose 100x and running threads jumped from 9 to 900, backing up Sidekiq job queues

   Kit2022

   Symptoms

   At 5:20 am EST on December 24, 2022, Kit's infrastructure team got its first page because the number of MySQL threads running on its RDS MySQL primary in us-east-2 had jumped 100x, from 9 to 900, at a time of day when Kit's traffic is very low. Sidekiq queues were backing up.

   Database overloadThroughput degradation

   Trigger

   An EBS interruption on the standby of the Multi-AZ RDS instance, reported by AWS as an LSE at 02:12:47 UTC on 2022-12-24, started a replaceSecondaryHostAndVolumesTask that replaced the secondary host and volumes.

   Root cause

   Cloud provider outage

   Component

   DatabaseCloud block storageDatabase replication

   Contributing factor

   Ignored early signal

   Detection

   A page fired at 5:20 am EST because MySQL threads running jumped from 9 to 900. An AWS RSS feed alert in Slack about degraded EC2 and EBS in use2-az1 had arrived the evening before and was not acted on.

   Remediation

   • Scaled the RDS storage from 12.5 TB to 13.75 TB, which put the instance through "optimizing storage" and brought write latency back to 1-2 ms

   Lessons

   Multi-AZ RDS keeps data consistent and durable, but synchronous replication means each write also waits for the standby, so a degraded EBS volume on the standby while it is being replaced slows writes on the primary. Large databases recover more slowly from such events than small ones. A single-AZ instance would not have been hit by a standby problem, but would have suffered hours of downtime if its own zone failed, and that trade-off has to be weighed when deploying production RDS instances.

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4. IL-0294 CircleCI Docker and machine jobs stalled for nearly 12 hours after a VM cleanup queue lost all its RabbitMQ consumers

   CircleCI2021

   Symptoms

   From 13:30 UTC on 21 May 2021, CircleCI jobs on Docker, the Machine executor (including Windows, Mac, Arm and GPU) and remote Docker were delayed or blocked. Engineers had been paged at 13:00 for excessive delays in starting jobs, and the alerts showed CircleCI could not provision some types and classes of VM because it was hitting its CPU quotas in GCP.

   Throughput degradationCloud API rate limit exhaustedDatabase overload

   Trigger

   The upgrade of the RabbitMQ cluster from 3.8.9 to 3.8.16 on 20 May 2021, which brought in a 15 minute consumer acknowledgement timeout for all queue types.

   Root cause

   Breaking change in an upgrade

   Component

   Message brokerDatabase

   Contributing factor

   Change with a delayed effectIgnored early signalRetry stormExcessive blast radiusOverlapping failures

   Detection

   Engineers were paged at 13:00 UTC on 21 May for excessive delays in starting jobs. Earlier, a 02:31 notification that a VM destroyer queue had no consumers was handled by restarting pods, and a 10:11 alert that a VM destroy queue was backing up notified once at its threshold and did not page.

   Remediation

   • Blocked Linux machine jobs from entering the work queue
   • Had GCP raise the CPU quotas
   • Deleted old VMs by hand
   • Cancelled some workflows to free resources
   • Reset the VM services deployment so the VM destroy queues had consumers again
   • Scaled up the RDS database instance, at 18:20 and again at 19:15
   • Turned down VM provisioning and blocked remote-docker jobs to shed load
   • Scaled down all pods that created and destroyed VMs
   • Deployed a revised distribution backend that kept remote-docker jobs out of the provisioning queues
   • Brought provisioning and distribution back one component at a time and reversed the mitigation code changes

   Action items

   • Make VM provisioning more robust to changes in load so it can recover from thundering herds
   • Make VM provisioning independent so its failure does not affect Docker jobs
   • Review and improve observability of RabbitMQ and the production queues
   • Batch requests to avoid hitting GCP API rate limits
   • Improve incident response for long-running incidents

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5. IL-0211 A Mandrill PostgreSQL shard shut down at transaction ID wraparound and email sending was impaired for about 40 hours

   Mailchimp2019

   Symptoms

   From 05:35 UTC on 4 February 2019 Mandrill, Mailchimp's transactional email service, sent only 80% of queued emails, because one of its PostgreSQL shards, shard4, stopped accepting writes. When Mandrill engineers opened an investigation at 11:58 UTC, they saw a sharply elevated load on shard4.

   Writes rejectedThroughput degradationLoad imbalanceNode disk full

   Trigger

   At 05:35 UTC on 4 February the transaction ID counter of shard4, the busiest Mandrill PostgreSQL shard, reached its upper limit and PostgreSQL shut the database down to protect against wraparound.

   Root cause

   Capacity exhaustion

   Component

   DatabasePostgreSQL vacuum

   Contributing factor

   Ignored early signalNo alert on limitSignal buried in noiseRetry stormMonitoring blind spot

   Detection

   The first exception was logged overnight but was buried by unrelated exceptions in the centralized error reporting. Mandrill engineers opened an investigation at 11:58 UTC and, while looking for the source of the rising load on shard4, found the wraparound at 12:56 UTC.

   Remediation

   • Put shard4 into standalone mode and started VACUUM at 14:16 UTC on 4 February
   • Stopped and restarted the vacuum with settings that made it run faster
   • Started a dump and restore of the database without the Search and Url tables while the vacuum ran
   • Replaced storage volumes on the Mandrill app servers whose disks were filling with queued jobs and logs
   • Moved the job-queue tables from shard4 to other live shards
   • Truncated the Search and Url tables on the locked database at 19:12 UTC on 5 February, after which the vacuum finished in about an hour
   • Started sending from the backlog at 21:36 UTC
   • Added alerting for XID wraparound by 22:40 UTC

   Lessons

   Mailchimp named the factors that slowed its response: knowledge of and access to Mandrill systems and monitoring rested with a few people, Mandrill logging needed a locally writable disk, logs of malformed API requests were hard to separate from real application errors, job health was hard to see, and the disk space problem made partial deployments for triage risky. What helped was trained incident commanders with clear roles (incident commanders in 3-hour rotations, scribes, technical liaisons and points of contact between teams), periodic summaries with a list of recent and open decisions, and taking time in a video meeting to reassess, which led to the truncation. Running in the cloud let the team expand storage quickly, and the team was lucky that the largest table holding up the vacuum held data it could delete.

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