Cases3 cases

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1. IL-0131 A new kind of GenerateContent request made Vertex AI prediction API servers segfault, and once enough of them restarted at the same time the serving fleet could not carry the load

   Google Cloud2024

   Symptoms

   From 12:06 US/Pacific on 12 June 2024, services built on Vertex AI Prediction began reporting failed user requests in eight regions: us-central1, asia-southeast1, europe-west3, europe-west4, us-east1, us-west1, northamerica-northeast1 and us-east4.

   High error rateHigh latencyIntermittent timeouts

   Trigger

   Starting on 8 June 2024, a new form of user request to the GenerateContent API caused intermittent segmentation faults in the Vertex Serving Prediction API servers; on 12 June the rate of such requests increased.

   Root cause

   Software defect

   Component

   Model inference serverCloud load balancer

   Contributing factor

   Partial rolloutNo headroom

   Detection

   Internal production monitoring alerted engineers to the segfaults on 10 June 2024 at 07:35 US/Pacific, before any visible customer impact; on 12 June services built on Vertex Prediction reported failing user requests.

   Remediation

   • confirmed the new failures came from the same segfault found on 10 June
   • accelerated the rollout of the fix that was already in progress, completed at 15:59 US/Pacific

   Action items

   • Ensure early signals of server binary issues are captured in production health analysis and investigated in time
   • Ensure release validation of feature changes and updates in production

   Lessons

   A crash that only some requests trigger can stay invisible while load balancers route around the restarting servers. It becomes an outage when the share of such requests grows until too many servers are restarting at once. Here the cause was known and a fix was rolling out two days before customers were affected, but the progressive rollout had not finished when the triggering traffic increased; Google's follow-up is to investigate early signals of server binary issues in time.

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2. IL-0178 On EKS, HPA scale-in returned 502 errors because containers stopped before their pod IPs left the Service endpoints, and after blue/green switches conntrack kept sending established connections to deleted pods behind an instance-mode ALB

   Lotte E-commerce2022

   Symptoms

   LOTTE ON, Lotte E-commerce's online shopping mall of about 40 microservices on Amazon EKS, returned errors for a period of time whenever the Horizontal Pod Autoscaler (HPA) scaled pods in after load dropped. Requests reached containers that had already been deleted and failed with 502 errors.

   High error rateIntermittent timeoutsStale endpoint routing

   Trigger

   HPA scale-in deleting pods, and the deletion of Blue pods after a blue/green switch of the Service label selector.

   Root cause

   DNS or network fault

   Component

   Horizontal pod autoscalerPod terminationkube-proxyconntrack tableCloud load balancerIngress controller

   Contributing factor

   Unreviewed defaults

   Remediation

   • delayed container shutdown on pod deletion (a wait in the SIGTERM handler or a preStop sleep, capped by terminationGracePeriodSeconds) so containers stop only after the pod has left the Service endpoints
   • switched some workloads' Ingress-created ALBs from instance mode to IP mode so traffic goes straight to pod IPs without iptables and conntrack

   Action items

   • Move all workloads to ALB IP mode gradually

   Lessons

   Removing a pod from Service endpoints and stopping its containers happen at the same time in Kubernetes, so a pod can stop while traffic is still routed to it unless shutdown is delayed with a preStop hook or in the application. With an instance-mode ALB, traffic passes through node iptables and conntrack, and established connections can keep going to a pod IP after its iptables rules are gone. IP mode avoids that path, within the limit of 1000 targets per target group.

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3. 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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