Cases3 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-0298 Wikimedia edits failed for 38 minutes when sessionstore Cassandra nodes in both data centres crashed on full disks

   Wikimedia Foundation2025

   Symptoms

   At 02:58 UTC on 31 March 2025, the sessionstore service in both Wikimedia data centres began returning 500 errors and edits failed. MediaWikiEditFailures (session loss) fired as critical at 03:03, and SessionStoreErrorRateHigh paged at 03:04.

   High error rateService outageNode disk fullData loss

   Trigger

   A rise in session writes, made up of session overwrites at a high rate, filled the storage of the sessionstore Cassandra nodes. The review says the rollout of SUL3 correlates strongly with it.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   No alert on limitUnverified assumption

   Detection

   MediaWikiEditFailures (session loss) fired as critical at 03:03 UTC, and SessionStoreErrorRateHigh, a high 5xx rate alert, paged the batphone at 03:04. Users also reported the problem in task T390512.

   Remediation

   • Wiped the sessionstore storage, removing all sessions, and restarted Cassandra

   Action items

   • Page when the disk utilisation trend becomes unreasonable
   • Increase sessionstore storage capacity
   • Add namespacing to sessionstore
   • Add observability of the sessionstore workload
   • Identify the cause of the high rate of storage growth in sessionstore Cassandra
   • Consider an alert, without paging, on the Cassandra error rate
   • Consider paging or alerting when Cassandra nodes enter a down state

   Lessons

   The storage growth before the node failures made their timing predictable, so an alert on high storage utilisation well in advance would have given a chance to act before users were affected, and an alert on Cassandra node failures would have told responders more than an HTTP error rate alert. A cluster seen as heavily over-provisioned can still grow quickly and unsustainably under an unusual workload.

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3. IL-0503 mybinder.org launches failed after a GKE node ran out of memory and its nginx-ingress proxy pod crash-looped

   Binder Project2017

   Symptoms

   On 30 November 2017 at 16:59 PST, mybinder.org operators noticed that Grafana looked wrong and that launching a new server failed with an error that a temporary user could not be created. Pods that were already running kept running, but no new ones were being created.

   Service outageContainer crash loopProbe failuresOOMKilled

   Trigger

   A build container, which ran without a memory limit, most likely used up all the memory on the node that ran the proxy pod.

   Root cause

   Capacity exhaustion

   Component

   Ingress controllerContainer memory limitJupyterHub

   Contributing factor

   No resource requests or limitsHarmful manual actionNo alert on limit

   Detection

   An operator happened to look at Grafana at 16:59 PST, saw that it looked wrong, and confirmed the error by launching a new server.

   Remediation

   • Cordoned the node that had run out of memory
   • Uncordoned the node once the proxy was working

   Action items

   • Limit the memory available to build pods
   • Change the JupyterHub configuration so user pods are not cleaned up when the jupyterhub pod restarts
   • Nominate one person to drive the response, possibly in a shared terminal, so only one person changes the cluster
   • Alert when something is wrong with the cluster, for example by counting 4xx responses on the jupyterhub pod

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