Cases96 cases

46 to 50 of 96

1. IL-0116 A CNI configuration change incompatible with a hardware variant that exists only in production cut network connectivity in an OpenAI GPU cluster and took text-davinci-003 fully down

   OpenAI2023

   Symptoms

   At 4:53 pm Pacific on 25 January 2023 one of OpenAI's GPU clusters had a major outage, and the text-davinci-003 model went fully down.

   Service outageThroughput degradation

   Trigger

   A configuration change to the CNI (Container Network Interface) plugins, which provide network connectivity to containers, was deployed to a GPU cluster that contained a hardware variant the staging environment did not have.

   Root cause

   Configuration change

   Component

   CNI plugin

   Contributing factor

   Unrepresentative stagingMonitoring blind spot

   Detection

   Engineers immediately identified the problem as network connectivity, but it took nearly an hour to identify CNI as the cause.

   Remediation

   • put a fix for the CNI configuration change in place, which restored 80% of traffic immediately
   • deployed models back onto GPUs over about three more hours until capacity was fully stable

   Action items

   • Make staging environments contain every type of hardware configuration so they better represent production
   • Monitor and alert on the health of the CNI system directly
   • Fix the network and storage bottlenecks that limit how quickly many replicas of a model can be deployed

   Lessons

   A change that had been deployed to other clusters over 24 hours and deemed safe still broke a cluster with different hardware, so staging needs every hardware configuration that production has.

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2. IL-0207 A routine task run from an engineer's local environment switched to production and deleted GitLab.com's Consul release, so Patroni lost Consul and GitLab.com returned 5xx errors to all traffic

   GitLab2022

   Symptoms

   GitLab's monitoring and alerting fired at once. One of the alerts reported a Patroni failover, and users of GitLab.com received 5XX errors; the CDN counted about 13 million requests answered with 503 during the outage.

   Service outageHigh error rateResources deleted

   Trigger

   An engineer ran a routine task with GitLab's tools in a local development environment; during a local deploy or rollback operation the tool's context changed from local development to production, and the change was applied to production.

   Root cause

   Operator error

   Component

   Database

   Contributing factor

   No safeguard on destructive change

   Detection

   GitLab's monitoring and alerting fired immediately; one of the alerts reported a Patroni failover.

   Remediation

   • re-applied the Consul release to the production Kubernetes cluster, with manual steps because the tooling did not support the redeploy directly

   Action items

   • Improve the Kubernetes deployment process to reduce the risk of running harmful deployment operations against production from local environments (https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/16875)
   • Integrate Teleport with the Kubernetes clusters, which could give SREs a read-only role by default (https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/16221)

   Lessons

   A tool that can switch its target from a local environment to production during a deploy or rollback turns routine local work into a production change. GitLab plans to improve its Kubernetes deployment process so that harmful deployment operations are less likely to be run against production from local environments, and to integrate Teleport with its clusters, which could give SREs a read-only role by default. Detection and diagnosis were fast because the alerts led straight from Patroni to its dependency, Consul. Bringing the deleted release back needed manual work, because the tooling did not make the redeploy straightforward, but it was still done relatively quickly.

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3. IL-0096 A GKE node pool pinned to the europe-west2-b zone could not scale for 6.5 hours because Google ran out of resources in that zone (ZONE_RESOURCE_POOL_EXHAUSTED)

   2i2c2022

   Symptoms

   A user of the LIS JupyterHub reported a "backoff after failed scale-up" error, and user servers would not start. An engineer opened incident #36, "LIS hub cannot scale", at 5:40 AM Pacific on 14 November 2022.

   Scale-up failurePods PendingService outage

   Trigger

   At 10 AM UK time on 14 November 2022, the B zone of Google Cloud's europe-west2 region (London) ran out of resources to allocate. The LIS cluster's nodes were restricted to that zone.

   Root cause

   Cloud provider outage

   Component

   Node poolCluster autoscaler

   Contributing factor

   Single point of failurePages not delivered

   Detection

   A user reported the "backoff after failed scale-up" error; an engineer opened the incident at 5:40 AM Pacific, about three hours and forty minutes after the zone ran out of resources.

   Remediation

   • edited the node pool in the GCP console to allow node creation in the A and C zones of europe-west2, which were not exhausted

   Action items

   • Either restrict the node pool back to the B zone after the Black Friday event, or move the cluster and the NFS Filestore to the A zone (infrastructure issue #1944)
   • Investigate an enterprise plan that allows a regional NFS, at higher cost (infrastructure issue #1945)
   • Make sure PagerDuty notifications always get through to engineers (team-compass issue #574)

   Lessons

   A node pool pinned to one zone for storage performance cannot scale when the cloud provider runs out of capacity in that zone. The autoscaler's "backoff after failed scale-up" message usually points to quotas, and raising quotas does nothing here; the ZONE_RESOURCE_POOL_EXHAUSTED error in the logs is what identifies a zone without capacity. The review found that one engineer wearing many hats (incident commander, comms and debugging), with pages not reaching the other responders, made the response harder; a team response would have given more support.

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4. IL-0513 PyPI backends on Kubernetes were unreachable over HTTPS for 20 minutes when the Ingress ELB lost its HTTPS listener

   Python Software Foundation2022

   Symptoms

   From 14:43 to 15:03 UTC on 24 October 2022, during regular maintenance of the Kubernetes cluster that hosts PyPI, PyPI's backends could not be reached over HTTPS. The CDN could not fetch pages, uploads to uploads.pypi.org were impossible, and other services such as the legacy file redirect service were interrupted.

   Service outage

   Trigger

   Regular maintenance did a rolling restart of every node in the cluster to roll out upgrades and new configuration; as the new Kubernetes API server hosts came up, Kubernetes validated and refreshed the Ingress configurations.

   Root cause

   Configuration change

   Component

   Ingress controllerCloud load balancer

   Contributing factor

   Configuration not in codeChange with a delayed effect

   Remediation

   • Pointed the Ingress configuration at the new ACM TLS certificate, and Kubernetes restored the HTTPS listener on the ELB

   Action items

   • Investigate managing the hostnames on the ACM TLS certificates for PyPI's Ingress through Kubernetes resources instead of the AWS console

   Lessons

   When every resource is managed through the Kubernetes API, drift between the desired state and reality is less likely to build up and then surface during later maintenance.

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5. IL-0191 A label selector accidentally added to the Loki distributor Service matched only the new pods of a rollout, which took all write traffic, were OOM-killed and stalled the rollout

   Grafana Labs2022

   Symptoms

   During a rollout of the Grafana Loki distributor Deployment in one Grafana Cloud Logs environment, the write path went down for about 25 minutes. The newly started distributor pods were OOM-killed over and over, never passed their health and readiness checks, and the rollout stopped making progress.

   Service outageOOMKilledLoad imbalanceProbe failures

   Trigger

   Step 1 of moving the Loki ring off a separately deployed key-value store onto embedded memberlist added a new label, gossip_ring_member: 'true', to the distributor pods and Service, together with a small config change, and the change required a rollout of the distributors.

   Root cause

   Configuration change

   Component

   Configuration templatingService discoveryContainer memory limit

   Contributing factor

   Unreviewed defaultsIgnored early signalUnrepresentative staging

   Detection

   The first clue was kubectl get pods and the kube_deployment_status_replicas_updated metric showing that only 16 of 140 distributor pods had been updated.

   Remediation

   • scaled the distributor Deployment out horizontally so the load spread over more pods with the new label and the rollout could continue
   • reverted the migration step to end the outage
   • set service_ignored_labels:: [$._config.gossip_member_label] and passed it to k.util.serviceFor so the label is no longer copied into the Service selector

   Action items

   • Update the function that generates a Service from a Deployment or StatefulSet to handle edge cases, including this one

   Lessons

   A Service whose selector is built by copying every label of a workload changes which pods it matches as soon as a new label is added. During a rollout it then matches only the new pods, which receive all the traffic before there are enough of them. A few errors during a rollout in other environments hid the problem, because those environments gave distributors more memory and could absorb the shift. Manifest diffs kept for every pull request made it possible to see that the Service selector had changed.

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