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1. IL-0285 Clever Cloud's Pulsar brokers stalled in a minor upgrade, then hypervisors overloaded and crashed with kernel panics

   Clever Cloud2024

   Symptoms

   On 2 August 2024, two minutes after Clever Cloud's data team started a minor rolling upgrade of its Apache Pulsar messaging cluster to release 3.3.1, monitoring showed connection problems with the messaging platform. Producing data into the event bus failed, the backlog grew while no new producers or consumers succeeded, and agents on hypervisors, log forwarders on load balancers and deployments were affected. At 10:20 CEST the Pulsar cluster was declared defective.

   Deploys failingSustained high node CPUMemory pressureNode kernel panicOOMKilledAutoscaler misbehaviourMonitoring outage

   Trigger

   At 10:06 CEST the data team began a minor rolling upgrade of Apache Pulsar to release 3.3.1 that also switched on a new balancer for bundle placement: the extensible load manager (loadManagerClassName=org.apache.pulsar.broker.loadbalance.extensions.ExtensibleLoadManagerImpl) with the TransferShedder load shedding strategy instead of the native OverloadShedder.

   Root cause

   Configuration change

   Component

   Message brokerTelemetry agentLinux kernelAutomated capacity manager

   Contributing factor

   No configuration testsMonitoring blind spotIneffective rollbackUnreviewed defaultsAutomation amplificationRetry stormCircular dependency

   Detection

   Monitoring showed connection issues to the messaging platform at 10:08 CEST, two minutes after the upgrade began, and engineers confirmed at 10:09 that producing data into the event bus failed. Monitoring did not catch the first broker's failure to create the system topic, because that broker stayed up.

   Remediation

   • Rolled back the new unload strategy while keeping Pulsar 3.3.1
   • Shut down the client proxies to release the brokers
   • Switched deployment notification events to RabbitMQ as a workaround
   • Stopped all brokers and started only one, which loaded the conflicting system topic so it could be deleted, then started all brokers
   • Restarted the messaging proxies, after which messages flowed again
   • Stopped the watchdog so it would not reload every failed component, and started components one by one
   • Disabled Metrics API ingestion to protect its storage cluster
   • Stopped the long-term storage for observability metrics (the Warp10/FoundationDB cluster) to free resources
   • Put all monitoring instances in all regions into maintenance so they stopped sending events to the orchestrator
   • Killed the overloaded small VMs one by one
   • Flagged 12 hypervisors to drain workload and shut down some load balancers to free CPU and memory
   • Cleaned zombie VMs to speed up recovery
   • Cleared the monitoring alert queue so queued events would not flood the orchestrator
   • Added parked hypervisors to EU-FR-1 to relieve the overloaded ones
   • Removed an overloaded RabbitMQ node from the cluster used by the orchestrator
   • Rolling-restarted every regional L7 load balancer and every L4 load balancer
   • Restarted all HV agents and the add-on provider APIs
   • Destroyed all dangling VMs stuck in the stopping state
   • Re-enabled Metrics API ingestion with a ramp-up QoS policy

   Action items

   • Track KPIs for Pulsar broker and cluster health, hypervisor load, I/O and kernel panic reboots, zombie VMs, RabbitMQ broker and cluster health and deployment queue consumption, each by time of event, detection, decision and resolution
   • Deal with too many monitoring alerts
   • Look at a backup bastion, because a regional bastion is vulnerable
   • Rework the control plane recovery process
   • Rework the correlation between monitoring and orchestration
   • Revisit fault domain separation against multi-tenancy
   • Rethink runtime images and the role of the hypervisor
   • Isolate waved release upgrades of Pulsar
   • Redesign runtime images with better hypervisor integration
   • Regionalise observability for redundancy
   • Set Vector's buffer.max_events and buffer.max_size for small VMs
   • Switch Vector's buffer.type from memory, the default, to disk
   • Change Vector's buffer.when_full from block, the default, to drop_newest
   • Tune journald verbosity and SystemMaxUse on small VMs
   • Add cgroups restrictions for better process control
   • Add I/O restrictions for the VM manager
   • Detect zombie VMs better

   Lessons

   The simulation process that validates non-minor releases was not applied to minor upgrades or to small configuration changes, and this one went straight to production. Running Clever Cloud on Clever Cloud turned into a chicken-and-egg situation: recovery needed the scheduler to carry out large actions while the control plane was itself slowed and the infrastructure had no free resources. Measuring each key indicator by time of event, detection, decision and resolution showed where the team had lacked visibility or reaction and challenged established rules.

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2. 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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