Cases8 cases

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1. IL-0150 A latent race condition in DynamoDB's DNS automation left an empty DNS record for dynamodb.us-east-1.amazonaws.com, and the DynamoDB and EC2 launch failures that followed caused EKS, ECS and Fargate container launch failures and cluster scaling delays in us-east-1

   Amazon Web Services2025

   Symptoms

   At 11:48 PM PDT on 19 October 2025, every system that reached DynamoDB in us-east-1 through the public regional endpoint started getting DNS failures and could not connect. This included customer traffic and internal AWS services that depend on DynamoDB. DynamoDB global table replicas in other regions still answered but fell behind in replication to and from us-east-1.

   DNS resolution failuresHigh error rateScale-up failureIntermittent connection failuresHigh latency

   Trigger

   One DNS Enactor, slowed by unusually high retry delays, applied a much older DNS plan to the regional DynamoDB endpoint just as another Enactor that had applied the newest plan ran its clean-up, which deleted that older plan and with it every IP address of the endpoint.

   Root cause

   Software defect

   Component

   Authoritative DNS servers and recordsDatabaseCloud load balancer

   Contributing factor

   Automation amplificationRetry stormNo runbook

   Detection

   DNS failures to DynamoDB began immediately at 11:48 PM PDT and the engineering teams of affected AWS services were engaged at once; by 12:38 AM they had identified DynamoDB's DNS state as the source. The later NLB health check problem was detected by monitoring at 6:52 AM.

   Remediation

   • applied temporary mitigations by 1:15 AM that let some internal services reach DynamoDB and repaired key internal tooling
   • restored all DynamoDB DNS information by 2:25 AM through manual operator intervention
   • throttled incoming work and selectively restarted DWFM hosts from 4:14 AM so droplet leases could be re-established
   • reduced the load on Network Manager to bring network configuration propagation times back to normal by 10:36 AM
   • disabled automatic health check failover for NLB at 9:36 AM, and re-enabled it at 2:09 PM after EC2 recovered
   • relaxed the EC2 request throttles from 11:23 AM until all EC2 APIs and launches were normal at 1:50 PM

   Action items

   • Disable the DynamoDB DNS Planner and DNS Enactor automation worldwide
   • Fix the race condition and add protections against applying incorrect DNS plans before re-enabling the DNS automation
   • Add a velocity control to NLB that limits how much capacity a single load balancer can remove when health check failures cause availability zone failover
   • Build an additional EC2 test suite that exercises the DWFM recovery workflow at scale
   • Improve throttling in EC2 data propagation systems so incoming work is rate limited by the size of the waiting queue

   Lessons

   A single DNS record that automation can empty is enough to take a widely used regional endpoint off the network, and every service built on it fails in turn. A check made once at the start of a long operation can be stale by the time the operation writes its result. Recovery of the dependency is not recovery of the dependents: systems that lost state during the outage, such as EC2's droplet leases and network propagation backlog, can collapse under their own catch-up work, and container services that rely on new instance launches keep failing to launch and scale until that work is done.

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2. IL-0495 HubSpot's internal load balancers on Kubernetes were falsely marked unhealthy and the rest collapsed under the traffic

   HubSpot2024

   Symptoms

   Between 14:20 and 15:16 UTC on 27 August 2024, some HubSpot customers could not log in to their accounts, reach their content or use HubSpot tools. Requests were slow or failed because HubSpot's internal load balancers, which run on Kubernetes and spread traffic across the backend services, were overloaded.

   Probe failuresHigh latencyHigh error rate

   Trigger

   Routine operation of a critical component of HubSpot's traffic management system, from about 14:08 UTC, ran path handling updates in the load balancer stack, and an issue in that stack made those updates block health check responses.

   Root cause

   Software defect

   Component

   Ingress controllerLiveness and readiness probes

   Contributing factor

   Automation amplificationNo headroom

   Detection

   Monitoring detected widespread performance degradation across the platform at 14:23 UTC and the incident response team was mobilised. HubSpot says it had identified the issue internally by 14:20 UTC. The status page was not updated until 14:44 UTC, which left customers with outdated information.

   Remediation

   • Tried to increase the capacity of the remaining load balancer instances
   • Brought new load balancer instances online, which relieved the pressure and let traffic return to normal

   Action items

   • Change the health check configuration so health checks do not time out falsely
   • Redesign the health check system so health checks get priority and keep running during operations such as configuration updates
   • Increase the capacity of the load balancers for sudden traffic spikes
   • Change the load balancing architecture to detect traffic surges earlier and distribute load dynamically in real time
   • Update the status page automatically for large-scale outages
   • Integrate the monitoring systems more tightly with the communication tools
   • Expand load testing to simulate high-traffic scenarios
   • Introduce chaos engineering to test platform resilience regularly
   • Add traffic pattern metrics, including the ratio of current connections to the maximum allowed
   • Add auto-scaling that adjusts resource capacity to traffic patterns

   Lessons

   HubSpot concluded that earlier detection of the abnormal traffic surge could have prevented the cascading failure, and that health checks have to keep running during operations such as configuration updates so that working load balancers are not removed together. The late status page update caused confusion for customers.

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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-0009 A duplicated generated name made Argo CD replace the InfluxDB Cloud production Application and delete the whole production environment

   InfluxData2022

   Symptoms

   Right after a pull request to the infrastructure-as-code repository was merged, every API endpoint of one InfluxDB Cloud production cluster began returning 404 errors. Writes and reads failed alike: no one could collect data, scheduled tasks failed to run and external queries did not work.

   Service outageResources deletedHigh error rate

   Trigger

   A merged pull request whose rendered YAML added a new Argo CD Application and AppProject that an automation error had named app01-cluster01, the name of the existing production Application, instead of app02-cluster01; reviewers did not notice the app01 and app02 difference.

   Root cause

   Configuration change

   Component

   Argo CDPersistent volumes and stateful setsConfiguration templating

   Contributing factor

   Missed in reviewNo runbookNo safeguard on destructive changeMonitoring blind spotAutomation amplification

   Detection

   Monitoring and alerting did not detect the issue right away, so the team's first response was to try to roll the change back rather than plan a recovery. The write-up does not say how the team found out.

   Remediation

   • stopped the revert of the merged pull request
   • restored the etcd metadata cluster
   • recreated the missing Kubernetes objects so the retained PersistentVolumes became available, then recreated the StatefulSets
   • restored the Zookeeper volumes from an hourly Velero backup
   • rebuilt the Kafka StatefulSet one pod at a time with health and readiness checks disabled
   • accepted writes into the Kafka write-ahead log and scaled up the write path with more replicas and higher memory requests and limits
   • recreated the storage pods, which downloaded data from object storage, reindexed it and replayed the write-ahead log
   • re-enabled Argo CD, queries, dashboards and scheduled tasks, restored replica counts and ran final QA checks

   Action items

   • Write runbooks for restoring state when Kubernetes objects such as PersistentVolumeClaims are deleted but the data on the underlying volumes is preserved
   • Set every volume in every environment to retain data when its PersistentVolumeClaim is deleted
   • Improve the process for handling public-facing incidents
   • Store generated YAML as one object per file, so an overwrite of an existing object shows up as such in review
   • Detect duplicate objects when generating YAML and warn before review, treating objects with the same group, namespace and name as the same even when the apiVersion string differs
   • Add the Argo CD Prune=false annotation to all stateful resources and to Namespace objects managed by Argo CD
   • Set FailOnSharedResource=true on Argo CD Applications so Argo CD fails before changing an object another Application manages or managed

   Lessons

   The biggest gap was that monitoring did not notice the deletion quickly, which pushed the team toward a reflex rollback; in a stateful system a revert can create a new, empty instance instead of bringing the old one back. Retained volumes and clients that retry writes are what kept data loss at zero. Rendering many objects into one file let an overwrite of an existing object look like an addition, so generated configuration needs one object per file and duplicate detection that compares objects the way Kubernetes does. Stateful resources need protection from pruning, and that protection must cover their Namespace too, or deleting the namespace removes everything inside it.

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5. IL-0208 A GitLab chart bump that renamed Kubernetes Service and Deployment ports made the GCP load balancer reconfigure, and GitLab.com slowed down and returned intermittent 50x errors

   GitLab2022

   Symptoms

   At 07:53 UTC on 2022-03-31 the rate of 50x errors on GitLab.com went up, and at 07:54 PagerDuty paged the engineer on call (EOC) about the rising error rate. Users saw slow pages and intermittent 50x error pages. The incident lists the Web, API, Git and Pages services as affected.

   High error rateHigh latencyIntermittent timeouts

   Trigger

   A k8s-workloads/gitlab-com pipeline deployed merge request !1643, a GitLab chart bump that changed the port names of Kubernetes Services and Deployments (related to the Prometheus service and pod monitors), starting with the canary environment gprd-cny at 07:49 UTC.

   Root cause

   Configuration change

   Component

   HelmCloud load balancerHAProxy

   Contributing factor

   Automation amplification

   Detection

   A PagerDuty alert on the rising error rate paged the EOC at 07:54 UTC, one minute after the 50x errors went up. The engineer deploying the change noticed the errors and linked them to it.

   Remediation

   • drained the canary environment and re-enabled it after twenty minutes
   • cancelled the deployment jobs for clusters c and d before they started
   • drained gprd-us-east1-d in HAProxy and re-enabled it five minutes later
   • raised the minimum replica count above 100 (merge request !1650)
   • drained gprd-us-east1-c in HAProxy, deployed the change to it, and re-enabled it
   • reverted the minimum replica increase (merge request !1652)

   Action items

   • Implement graceful draining of a whole regional cluster
   • Create a Chatops command that puts PagerDuty into maintenance mode

   Lessons

   Renaming ports on Kubernetes Services and Deployments behind a GCP load balancer made the load balancer reconfigure, and requests were slow or timed out until the new configuration had fully applied, which took minutes. GitLab notes that alerting worked well. The follow-up work is a way to drain a whole regional cluster gracefully and a Chatops command to put PagerDuty into maintenance mode.

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