Cases52 cases

15 to 19 of 52

1. IL-0489 Rejoining a failed node of Magic Pages' three-node MySQL cluster broke its metadata and took customer websites down

   Magic Pages2025

   Symptoms

   On 15 January 2025, from about 09:55 to 11:36 CET, a database outage at Magic Pages took down customer websites. It began when the founder tried to reconnect a failed third node to the high-availability MySQL cluster and the database server reported ambiguous errors about missing metadata information.

   Service outage

   Trigger

   One of the three nodes of the MySQL cluster went down, and reconnecting it failed with errors about missing metadata information.

   Root cause

   Dependency failure

   Component

   DatabasePersistent volumes and stateful sets

   Contributing factor

   Harmful manual actionSingle point of failureMonitoring blind spot

   Remediation

   • Restarted the storage server to fix PVC provisioning
   • Let the MySQL nodes restore and sync with each other for about 45 minutes to rebuild the cluster metadata
   • Cleaned up the corrupted cluster metadata
   • Redeployed MySQL Router

   Action items

   • Monitor the health of the storage system more closely
   • Improve the failover procedures for MySQL Router
   • Investigate and fix the status page so it shows reliable information

   Lessons

   The cluster survived the first node failure as designed and lost no data, but its metadata turned out to be a single point of failure. Better monitoring of PVC health could help prevent similar issues, the 45-minute recovery leaves room for improvement, and the status page has to detect failing customer sites reliably.

   View case
2. IL-0005 A new telemetry service overloaded the Kubernetes API servers of large clusters and broke DNS-based service discovery for all OpenAI services

   OpenAI2024

   Symptoms

   Alerts fired at 3:13 PM PST on 11 December 2024, and from 3:16 PM every OpenAI service was significantly degraded or completely unavailable, including ChatGPT, the API and Sora. The status page reported API calls returning errors and difficulties logging in to platform.openai.com and ChatGPT. Customer impact peaked at 3:40 PM.

   Service outageHigh error rateControl plane unresponsiveDNS resolution failures

   Trigger

   At 2:23 PM PST a change introducing a new telemetry service, which collects detailed Kubernetes control plane metrics, was merged and the deployment pipeline started. Between 2:51 PM and 3:20 PM the change was applied to all clusters.

   Root cause

   Configuration change

   Component

   Kubernetes API serverClient-side DNS resolver and cacheTelemetry agent

   Contributing factor

   Unrepresentative stagingNo staged rolloutCache-masked failureMonitoring blind spotLimited debug access

   Detection

   OpenAI's detection tools worked: alerts fired at 3:13 PM PST, a few minutes before customer impact began at 3:16 PM, and the root cause was identified at 3:16 PM.

   Remediation

   • Scaled clusters down to reduce the total Kubernetes API load
   • Blocked network access to the Kubernetes admin APIs so no new expensive requests arrived
   • Scaled up the Kubernetes API servers
   • Removed the telemetry service once enough control was back
   • Moved traffic from impacted clusters to healthy ones
   • Intervened manually in clusters where simultaneous resource downloads saturated resource limits

   Action items

   • Put every infrastructure configuration change through a phased rollout with continuous monitoring of both the service workloads and the clusters, including the Kubernetes control plane
   • Run fault injection tests in which the data plane runs without the control plane for longer, and tests that roll out bad changes on purpose to check detection and rollback
   • Build break-glass mechanisms so engineers can reach the Kubernetes API servers in any circumstances
   • Decouple the Kubernetes data plane from the control plane so that service discovery for critical workloads does not depend on the control plane
   • Speed up recovery with better caching and dynamic rate limiters for resources needed at cluster startup, and regular drills that rapidly replace an entire cluster

   Lessons

   Several systems and processes failed at once and interacted in unexpected ways: tests did not catch the change's effect on the Kubernetes control plane, DNS caching put a delay between the change and the failures, and being locked out of the control plane made remediation very slow. Because service discovery used Kubernetes DNS, the control plane was carrying load for product traffic even though the data plane was meant to be independent of it.

   View case
3. IL-0203 A routine Kubernetes cluster system update made the AWS Load Balancer Controller fail, so new pods were never registered with the ALB and Honeycomb's EU1 ingest and UI went down once the last old pods were replaced

   Honeycomb2024

   Symptoms

   Event ingestion and the Honeycomb UI in EU1 became unavailable at 22:00:10 UTC on April 9, 2024, and the status page reported that the EU1 instance was down. Both the ingestion service and the service behind the web UI stopped at roughly the same moment.

   Service outageStale endpoint routing

   Trigger

   A routine system update to the Kubernetes cluster, rolled out earlier on April 9, followed by a regular deployment that replaced pods one at a time.

   Root cause

   Breaking change in an upgrade

   Component

   Ingress controllerCloud load balancer

   Contributing factor

   Monitoring blind spotUnrepresentative stagingChange with a delayed effect

   Detection

   The first status post, saying the EU1 instance was down, went up at 15:08 PDT (22:08 UTC), about 8 minutes after the outage began. There was no alerting on the AWS Load Balancer Controller.

   Remediation

   • Restarted the AWS Load Balancer Controller
   • Rolled back the Kubernetes cluster system update

   Action items

   • Find the exact cause of the failure so the system update can be deployed safely
   • Add monitoring and alerting on the AWS Load Balancer Controller so on-call engineers learn of a failure quickly and can pause deployments

   Lessons

   A rolling deployment can look healthy while the component that wires new pods into the load balancer is broken; the failure only shows when the last old pod goes away. Honeycomb now treats the AWS Load Balancer Controller as a critical component that needs its own alerting, so deployments can be paused before they remove the last pods that still receive traffic. Extensive testing in development and staging did not reveal the bad interaction.

   View case
4. IL-0110 A Grafana Agent pod's high memory use and crash-looping Hangfire pods drove a Kubernetes node into system OOM and NotReady, twice in one day

   DFDS2024

   Symptoms

   At 10:01 UTC on 2024-03-13 a Kubernetes node entered NotReady state, with CPU requests at 78% and memory requests at 82%. An alert fired in Slack at 10:03 and was picked up at 10:05. An attempt to SSH into the node at 10:13 failed because it was unresponsive; it started responding again at 10:19.

   Node NotReadyContainer crash loopOOMKilledMonitoring outage

   Trigger

   Pods of a dev container that used Hangfire to trigger jobs failed with OOM and crash-looped on a node where a Grafana Agent pod was already using a lot of memory.

   Root cause

   Capacity exhaustion

   Component

   Telemetry agentContainer memory limitDatabase

   Contributing factor

   Excessive blast radiusNo resource requests or limitsHarmful manual actionMonitoring blind spot

   Detection

   A Slack alert fired two minutes after the node went NotReady and was picked up two minutes later; the second node's NotReady at 15:03 also raised a Slack alert.

   Remediation

   • marked the EC2 instance unhealthy so a new node started
   • cordoned and force-drained the NotReady node and terminated its EC2 instance
   • removed the new LimitRange from the Grafana Agent namespace and restarted the Grafana Agent pod
   • scaled the dev container to 0 replicas, after which EC2 network traffic returned to normal
   • after the second NotReady, scaled the dev container to 0 replicas again and added a new node
   • emailed the team that owns the dev container, which confirmed it was working on a fix

   Action items

   • Move grafana-agent to the monitoring node group
   • Add alerts for when bandwidth across the cluster reaches a certain threshold
   • Investigate whether memory limits can be set on the grafana-agent Helm chart
   • Investigate grafana-agent memory usage
   • Ask Grafana support whether grafana-agent should use a PVC
   • Consider pushing node logs to Loki with limited retention
   • Create a wiki page with common metrics and log queries to check when investigating issues with Kubernetes nodes

   Lessons

   Dev workloads mixed with production let one crash-looping dev container take down nodes that production also used, and a monitoring agent outside its dedicated node group added to the memory pressure. Scaling the suspect container back up before its owners had fixed it brought the failure back on the replacement node. The team also noted that action items from earlier post-mortems need follow-up, and that knowledge of useful CloudWatch metrics had not been shared, which made the diagnosis harder.

   View case
5. IL-0043 Application logs missing in Kibana after a newer Fluent Bit version changed its chunk drop strategy

   UK Ministry of Justice2023

   Symptoms

   At 09:14 on 4 August 2023, users of the Cloud Platform reported in the #ask-cloud-platform Slack channel that Kibana was missing their application logs for long periods.

   Monitoring outage

   Trigger

   The newer Fluent Bit version in use had changed its chunk drop strategy; the fix that followed handled memory buffer overflow.

   Root cause

   Capacity exhaustion

   Component

   Telemetry agent

   Contributing factor

   No alert on limitMonitoring blind spot

   Detection

   Users reported long periods of missing logs in Kibana in the #ask-cloud-platform Slack channel at 09:14; the incident was declared at 10:09.

   Remediation

   • configured Fluent Bit to handle memory buffer overflow by writing buffered logs to the file system
   • made Fluent Bit flush logs in smaller chunks

   Action items

   • Push notifications from the logging clusters to #lower-priority-alerts (#4704)
   • Add an integration test that checks logs are being sent to the logging cluster

   View case

By root cause9