Cases18 cases

8 to 12 of 18

1. IL-0123 A response buffer allocated anew in a loop made OpenAI's routing layer nodes hit their memory limits and fail readiness checks on a morning with more completions than any day before, causing 502 and 503 errors across all models

   OpenAI2023

   Symptoms

   Between 5:42 AM and 7:16 AM PT on 8 November 2023 a large share of requests to OpenAI failed with 502 or 503 errors. Every model and API endpoint saw significant failures.

   High error rateService outageProbe failures

   Trigger

   A morning with dramatically more completions than any previous day pushed the routing layer nodes, which already had a chronic memory problem, past their memory limits.

   Root cause

   Software defect

   Component

   Model request routingContainer memory limit

   Contributing factor

   No alert on limitNo headroom

   Remediation

   • limited incoming traffic
   • redeployed the routing service en masse
   • ramped traffic back up slowly

   Action items

   • Pre-allocate the responseBuffer and reuse it instead of allocating a new one in a loop (3X improvement in memory and CPU usage)
   • Adjust the configured memory limits to an appropriate level so the service has significant headroom
   • Add rate limit controls to shed traffic more gracefully
   • Increase the service's capacity
   • Change alerting to catch the underlying memory behaviour before it escalates into service issues
   • Configure autoscaling for the service, which was not possible before because added capacity could hurt an upstream service

   View case
2. IL-0016 Prometheus on the live cluster ran out of memory and was OOMKilled, and the monitoring node it ran on went NotReady

   UK Ministry of Justice2023

   Symptoms

   At 14:05 on 25 July 2023, a high-priority PagerDuty alert from Pingdom reported that the "Prometheus - live healthcheck" was DOWN. Prometheus on the live cluster was unavailable, and the Cloud Platform lost its monitoring for a while.

   Monitoring outageOOMKilledNode NotReady

   Trigger

   The growing number of namespaces and Prometheus rules raised Prometheus' memory needs beyond what its node instance size could hold.

   Root cause

   Capacity exhaustion

   Component

   PrometheusNode poolContainer memory limit

   Contributing factor

   No alert on limit

   Detection

   A Pingdom health check on Prometheus failed and raised a high-priority PagerDuty alert.

   Remediation

   • drained the monitoring node, moving Prometheus to another monitoring node
   • changed the node instance size to r6i.4xlarge
   • raised the Prometheus memory limit to 100Gi
   • set the Prometheus container limits to 12 CPU cores and 110Gi of memory

   Action items

   • Add an alert on node memory usage and on a pod using up most of a node's memory (issue #4538)

   View case
3. IL-0165 Some LKE users in Frankfurt could not reach their kube-apiservers with kubectl after backend services failed under memory constraints

   Linode2023

   Symptoms

   At 14:11 UTC on May 25, 2023, Linode learned that part of its LKE users in the Frankfurt data center could not reach their kube-apiservers through kubectl. The status page opened at 14:27 UTC with "We are investigating reports of connectivity issues with the LKE service within the Frankfurt Datacenter".

   Control plane unresponsive

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serverContainer memory limit

   Contributing factor

   –

   Detection

   Linode was made aware of the problem at 14:11 UTC; the status page refers to reports of connectivity issues.

   Remediation

   • increased memory limits

   Action items

   • Increase resilience, redundancy and monitoring for the LKE components affected by the incident

   View case
4. 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.

   View case
5. IL-0206 Consul agents in GitLab.com's GKE pods were repeatedly OOM-killed, so the Rails pods lost database replica discovery and sent every query to the primary

   GitLab2022

   Symptoms

   On 2022-04-25 the Sidekiq Apdex (the share of background jobs that finish within a target time) dropped, and an engineer declared an incident in Slack at 15:13 UTC. Users saw intermittent slowness across GitLab.com, and asynchronous work such as pipeline creation and import and export took unusually long.

   High latencyDatabase overloadOOMKilledHigh error rate

   Trigger

   The Consul agent in GitLab.com's GKE pods had memory demand spikes that did not fit in the pods' 500MB memory limit, and the agent was killed again and again.

   Root cause

   Capacity exhaustion

   Component

   Container memory limitService discoveryApplication database

   Contributing factor

   –

   Detection

   An engineer declared the incident in Slack at 15:13 UTC after the Sidekiq Apdex dropped; the second event was found when monitoring detected an elevated error rate at 01:21 UTC on 2022-04-26.

   Remediation

   • planned at 16:13 to raise the per-user connection limit for the gitlab database user and the pgbouncer backend pools for the main database; changes were applied at 17:26 and confirmed at 17:47
   • raised the pod memory limits from 500MB to 1GB so the Consul agents survive memory spikes

   Action items

   • Monitor the Consul agents on GKE pods to see whether the 1GB memory limit keeps them healthy
   • Investigate the Consul memory demand spikes and what can be done beyond raising the hard memory limit

   Lessons

   When the local Consul agent that Rails uses for database service discovery keeps dying, Rails does not fail; it sends every query to the last known primary. What engineers saw was database saturation and slow background jobs, which pointed the investigation first at a recent pgbouncer pool change. The memory limit of the Consul agent in every pod therefore decided the load on the primary database.

   View case

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