Cases30 cases

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1. IL-0168 Simultaneous cluster auto-upgrades overloaded Scaleway's Kubernetes API, and a node authentication retry loop kept Kapsule and Kosmos nodes NotReady in FR-PAR

   Scaleway2024

   Symptoms

   At 01:04 CET on 13 February 2024, the Kubernetes API behind Scaleway's managed Kubernetes services Kapsule and Kosmos in the Paris region (FR-PAR) spiked in RAM and CPU use and ran out of memory. Nodes in FR-PAR could no longer authenticate to their control plane, and some of them went "Not Ready". Customers on both shared (mutualized) and dedicated environments were affected.

   Node NotReadyControl plane lagOOMKilled

   Trigger

   A new Kubernetes release was made available to all users on 12 February 2024, and during the night of 12 to 13 February many clusters triggered their auto-upgrade at the same time.

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API server

   Contributing factor

   Retry stormAutomation amplificationNo staged rollout

   Detection

   The timeline's first entry, at 01:04 CET, records the Kubernetes API's RAM and CPU spikes and out-of-memory state, with nodes unable to authenticate to their control plane and some becoming "Not Ready". The first recorded action, 42 minutes later at 01:46, was to increase the resources allocated to the API.

   Remediation

   • increased the resources allocated to the Kubernetes API at 01:46 and again at 02:20
   • applied API traffic restrictions at 02:30 to smooth the incoming traffic
   • removed the traffic restrictions progressively in 50-100 batches from 03:20
   • ran a script at 03:40 to replace the failing nodes created during the congestion
   • rolled back to the former node authentication mechanism, first on a subset of clusters from 15:45 and on all clusters by 19:41 on 19 February
   • disabled cluster upgrade and configuration update features until 10:30 on 20 February

   Action items

   • Increase the resources allocated to the Kubernetes API
   • Roll back to the former node authentication mechanism
   • Implement a local authentication cache on nodes
   • Rework the resource allocation strategy to behave better under load spikes
   • Add new request queuing mechanisms for Kubernetes
   • Separate the different components to reduce side effects under high load or failure
   • Monitor selected metrics closely to identify anomalous patterns earlier
   • Refund customers who deployed nodes during the incident but could not use them normally

   Lessons

   Letting many clusters auto-upgrade at the same moment after a release turns routine upgrades into one coordinated load spike on the shared Kubernetes API. A node authentication mechanism that retries without letting up turns a slow API into an unavailable one, and auto-healing that replaces NotReady nodes amplifies the problem. The congestion pattern had already appeared three weeks earlier, and short-term mitigations did not stop it from recurring, so Scaleway rolled back the new authentication mechanism. Scaleway notes that as the number of customer clusters and nodes grows sharply, traffic control mechanisms have to improve to prevent congestion.

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2. IL-0152 An incorrect configuration applied during maintenance made an internal infrastructure cluster automatically delete the control planes of 200 DOKS clusters in FRA1

   DigitalOcean2024

   Symptoms

   At 13:02 UTC on 2024-01-23 DigitalOcean posted that, as of 12:18 UTC, its engineers were investigating an issue with Kubernetes clusters in the FRA1 region and that users might see errors when communicating with their clusters. The control planes of the affected DOKS (DigitalOcean Kubernetes) clusters had become inaccessible.

   Resources deletedControl plane unresponsive

   Trigger

   An incorrect configuration was applied to an internal infrastructure cluster in FRA1 at 12:21 UTC during a maintenance operation.

   Root cause

   Configuration change

   Component

   etcdKubernetes API server

   Contributing factor

   Automation amplificationNo safeguard on destructive changeExcessive blast radius

   Detection

   Engineers were investigating errors with Kubernetes clusters in FRA1 as of 12:18 UTC and identified the incorrect configuration as the cause at 12:36.

   Remediation

   • restored each affected cluster's etcd from its latest twice-daily backup
   • restored the stateless control plane components after etcd
   • patched the recovery mechanism after unexpected problems during recovery
   • found and fixed an additional small bug during recovery
   • contacted affected customers directly with further instructions, since some might need to reconfigure Kubernetes resources

   Action items

   • Prevent automatic deletion of infrastructure cluster resources under any circumstances in the production environment

   Lessons

   When managed control planes run as resources in another cluster, one configuration mistake in that cluster can delete hundreds of control planes at once through ordinary automation. The etcd backups limited the damage, but their twice-daily schedule set the worst case at 12 hours of lost control plane state, and the recovery tooling itself needed patching during the incident.

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3. IL-0202 A burst of queries and database writes exhausted connection pools and memory on Honeycomb's ingest hosts, and Kubernetes crash-loop back-off kept failed hosts offline so ingest capacity stayed too low to recover

   Honeycomb2023sev1

   Symptoms

   Standard alerting paged Honeycomb's on-call engineers about the performance and stability of event ingestion, and the automated load-shedding mechanisms tripped and sent further notifications. The status page described a higher than normal error rate and response time on the ingestion service.

   High error rateHigh latencyContainer crash loopService outageDatabase overload

   Trigger

   An abnormally large number of queries arrived from many separate connections within a few minutes.

   Root cause

   Capacity exhaustion

   Component

   DatabaseIn-memory cache

   Contributing factor

   Retry stormAutomation amplification

   Detection

   Standard alerting notified the on-call engineers about ingestion performance and stability, and the automated load-shedding mechanisms added notifications when they tripped. The first status post, about alerts on a higher than normal error rate and response time, went up at 18:28 PST (02:28 UTC), 13 minutes after the 02:15 UTC start.

   Remediation

   • Scaled the ingest fleet up manually and aggressively
   • Cut off most ingest for a few minutes so the ingest hosts could come back with enough capacity
   • Temporarily lowered the rate limits for the most aggressive traffic sources until their traffic subsided

   Action items

   • Keep the ingest fleet scaled up to prevent a repeat while longer-term fixes are worked on
   • Decide on the projects that will address these failure paths

   Lessons

   Load shedding and auto-scaling were in place, yet a burst that arrived faster than the cache could spread its writes still took the ingest hosts down. During recovery, Kubernetes' crash loop back-off worked against the operators: the hosts that had failed stayed offline while the fleet needed every host. Clients that retry whole OTel batches after a partial drop can multiply traffic many times over, so per-source rate limits were what finally stabilised ingest.

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4. IL-0437 Old pods from an unfinished Argo Rollouts canary could not start with a Helm-updated ConfigMap and died in a cascade

   Gunosy2023

   Symptoms

   During a traffic surge from a breaking news alert, pods of a Gunosy service on Kubernetes could not start because the containers could not read their settings from the ConfigMap. The pods that kept running took the extra load and were restarted, failed to start again, and pod after pod died.

   Container crash loop

   Trigger

   A breaking news alert increased traffic and pods were scaled out automatically while a release that removed an old database's settings from the ConfigMap had been rolled out to only one canary pod.

   Root cause

   Faulty deployment

   Component

   ConfigMaps and SecretsHelmProgressive delivery controller

   Contributing factor

   Partial rolloutAutomation amplification

   Action items

   • Configure Argo Rollouts to roll out automatically, with setWeight 1, a one-hour pause and then setWeight 100
   • Add the "helm.sh/resource-policy": keep annotation so Helm keeps past versions of the ConfigMap
   • Build and use a Helm plugin, helm-clear, that keeps only the last few ConfigMap versions by the revision number in their names, set to keep 5 in CD

   Lessons

   Gunosy holds that a review that only resolves to be careful next time would soon be followed by the same failure, because human attention cannot be relied on, so each incident gets a permanent fix built into the system. Keeping past ConfigMap versions also made it possible to roll back quickly without going through CI/CD, which a ConfigMap change had previously prevented.

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5. IL-0198 An automatic Ubuntu security update restarted systemd-networkd, which flushed the IP rules Cilium relied on and cut more than 60 percent of Datadog's Kubernetes nodes off the network across regions and cloud providers

   Datadog2023

   Symptoms

   Between 06:00 and 07:00 UTC on 8 March 2023, Kubernetes nodes in several Datadog regions lost network connectivity at the same time, on AWS, Google Cloud and Azure. All services across multiple regions were affected, including the regions that host Datadog's CI/CD and automation tools. Datadog's regions are not connected to each other and changes are rolled out one region at a time, so a simultaneous failure on several cloud providers looked implausible at first.

   Node NotReadyService outageThroughput degradation

   Trigger

   On 7 March 2023 Ubuntu published a systemd patch for a CVE (249.11-0ubuntu3.7). Ubuntu's default unattended upgrades installed it on the nodes between 06:00 and 07:00 UTC on 8 March, and installing it restarted systemd-networkd.

   Root cause

   Breaking change in an upgrade

   Component

   systemd-networkdCiliumNode operating system image

   Contributing factor

   Unreviewed defaultsChange with a delayed effectNo staged rolloutAutomation amplification

   Remediation

   • restarted thousands of disconnected instances on Google Cloud and Azure through the cloud APIs, keeping their local disks
   • rebuilt the data lost with terminated AWS instances' local disks through replication, reprocessing and restores from backup

   Lessons

   A change in a base operating system component can stay dormant for months until something restarts that component. Here the trigger was a security patch, and the change reached every region at once because automatic OS updates ran outside the progressive, region-by-region process used for every other change. Cloud providers react differently to an instance that loses its network. Google Cloud and Azure left the instances running, so a restart recovered them with their disks; AWS replaced them quickly, which brought stateless services back fast but destroyed the local-disk data the stateful services needed.

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