Cases2 cases

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1. IL-0290 GitLab.com's Redis Sidekiq cluster ran out of memory and lost quorum, causing 500 errors and a 12 million job backlog

   GitLab2026sev1

   Symptoms

   From 17:12 UTC on 12 May 2026, error rates rose at the same time across GitLab.com's web, Git, API and CI runner services. Users got frequent 500 errors, failed or stuck CI pipelines, authentication failures and unreliable Git access, and a Prometheus Alertmanager alert opened the incident at 17:15.

   High error rateMemory pressureThroughput degradation

   Trigger

   Earlier on 12 May, as a mitigation for a previous incident, INC-10084, a Sidekiq queueing apdex SLO violation on shard catchall-b, jobs of AuditEvents::AuditEventStreamingWorker were deferred. The deferred audit event jobs then piled up in the Redis Sidekiq cluster.

   Root cause

   Capacity exhaustion

   Component

   In-memory cache

   Contributing factor

   Change with a delayed effectPages not deliveredLimited debug access

   Detection

   A Prometheus Alertmanager alert reported the incident at 17:15:10 UTC, three minutes after impact began. A follow-up asks why the redis-sidekiq memory alert did not page, and the review says some signals did not reach the right on-call people.

   Remediation

   • Restarted and force-reset the two unresponsive Redis Sidekiq nodes
   • Stopped each Redis Sidekiq VM in turn and resized it to c2-standard-16, doubling memory and CPU
   • Re-enabled AuditEventStreamingWorker instead of deferring it

   Action items

   • Investigate why the redis-sidekiq memory alert did not page
   • Establish the root cause, failure mode and remediation status of the WebHookWorker poison pill
   • Give mitigations that disable safety limits an explicit owner and a deadline for re-evaluating them
   • Stop AuditEvents::AuditEventStreamingWorker from causing recurring incidents
   • Create a runbook for AuditEventStreamingWorker
   • Address PG connection pool saturation

   Lessons

   Broad degradation across web, Git, authentication, CI runners, Sidekiq and the internal API made it hard to settle on one cause and explain it clearly. The customer symptom of pipelines stuck in created was hard to measure in real time, and the dashboards that would have answered it were not obvious to on-call responders. Some alerts did not reach the right on-call people, and hosts that hung on login slowed diagnosis and made changes riskier. Redis-sidekiq under memory saturation forces choices between availability and data loss that are hard to make during an incident and call for guardrails and design changes.

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2. 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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