Cases18 cases

6 to 10 of 18

1. IL-0232 Calico pods in the codfw WikiKube cluster hit their resource limits and crash-looped, taking nodes out one by one

   Wikimedia Foundation2024

   Symptoms

   At 05:01 UTC on 9 October 2024 a page fired for GatewayBackendErrorsHigh, elevated 5xx errors from page-analytics_cluster in codfw, followed by a growing number of KubernetesCalicoDown alerts for WikiKube nodes not running the calico-node pod and by MediaWikiMemcachedHighErrorRate. For about an hour uncached MediaWiki requests, such as those of logged-in users, edits and API calls, saw high latency or errors.

   High error rateHigh latencyContainer crash loopProbe failuresControl plane lag

   Trigger

   An operator used the new mwscript-k8s utility to run a batch of video transcode maintenance, the TimedMediaHandler requeueTranscodes.php script. mwscript-k8s deploys a whole helm release for every script invocation, so the batch created close to 2k Calico NetworkPolicies along with many other resources over several hours.

   Root cause

   Capacity exhaustion

   Component

   CalicoHelmJobs and CronJobs

   Contributing factor

   Change with a delayed effectNo runbook

   Detection

   Automated monitoring detected the issue and paged the on-call SRE: GatewayBackendErrorsHigh at 05:01:36 UTC, 16 KubernetesCalicoDown alerts at 05:03:32 UTC and MediaWikiMemcachedHighErrorRate at 05:04:15 UTC.

   Remediation

   • Raised the resource limits of the Calico and Typha pods and applied the change with helmfile, which redeployed all Calico pods

   Action items

   • Stop mwscript-k8s from creating so many resources

   Lessons

   The incident came close to a full outage. Automated monitoring caught it and many responders found the cause, but end users were affected and responders were unsure whether they could switch over to eqiad. The problem was noticed just before the hand-off from Americas to EMEA SRE shifts, a period with a long-known lack of coverage, and it helped that the transcodes were not queued on a Friday evening in the US.

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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-0044 Prometheus OOMKilled in a loop while replaying its write-ahead log after a kube-prometheus-stack upgrade

   Saifeddine Rajhi2024

   Symptoms

   After an upgrade of the kube-prometheus-stack Helm chart, several pods stayed not ready. Among them was prometheus-prometheus-operator-prometheus-0, showing 3/4 Running and a recent termination.

   OOMKilledProbe failuresMonitoring outage

   Trigger

   During the kube-prometheus-stack Helm chart upgrade, Prometheus had to replay its write-ahead log on startup, and the replay needed more memory than its limit allowed.

   Root cause

   Capacity exhaustion

   Component

   PrometheusContainer memory limit

   Contributing factor

   Unknown limitNo headroom

   Detection

   The team noticed pods not ready after the Helm chart upgrade and found the OOMKilled termination with kubectl describe pod.

   Remediation

   • ran promtool tsdb analyze inside the Prometheus pod to find high-cardinality metrics such as haproxy_server_http_responses_total

   Action items

   • Verify that the memory overhead during WAL replay stays within acceptable limits, for example 10 to 15%, and do not run Prometheus close to its memory limit
   • Optimise storage, scraping and remote write configuration and upgrade to newer Prometheus releases
   • Recover automatically from OOM crash loops, for example by deleting the WAL on OOM events
   • Enforce scrape limits so Prometheus stops scraping targets when memory use passes a threshold

   Lessons

   Size Prometheus for its restart, not only for steady state: WAL replay can need far more memory than normal running, and a Prometheus that already runs near its limit can be OOM-killed during the replay. Much of Prometheus memory use comes from cardinality, which promtool tsdb analyze can measure even when Prometheus itself is down.

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4. IL-0046 Prometheus restarted again and again because its WAL replay took longer than the startup probe allowed

   UK Ministry of Justice2024

   Symptoms

   At 12:32 Prometheus on the Cloud Platform was not available after a planned change, and the Prometheus pod had restarted several times. The platform lost its monitoring and had missing metrics for the period.

   Monitoring outageProbe failures

   Trigger

   A planned change restarted Prometheus at a time when the WAL count was high.

   Root cause

   Capacity exhaustion

   Component

   Prometheus

   Contributing factor

   Unreviewed defaultsUnknown limit

   Detection

   The team saw that the Prometheus pod had restarted several times after the planned change.

   Remediation

   • raised the Prometheus startupProbe threshold to 30 minutes

   Action items

   • Perform planned Prometheus restarts when the WAL count is lower
   • Set the default CPU and memory requests to meet maximum usage
   • Create a test setup that recreates the live WAL count
   • Explore the memory-snapshot-on-shutdown and auto-gomaxprocs feature flags
   • Explore storing WAL files remotely in a different location
   • Look into a blue-green Prometheus to test changes on a live-like setup before applying them to live
   • Spike into Amazon Managed Prometheus

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5. IL-0045 Prometheus on the live cluster restarted several times after readiness probe failures, leaving gaps in metrics

   UK Ministry of Justice2023

   Symptoms

   At 10:15 on 1 November 2023 a Pingdom check raised a PagerDuty high-priority alert that the "Prometheus - live healthcheck is DOWN". The same alert fired for the third time within a few minutes, and the incident was declared at 10:41.

   Monitoring outageProbe failures

   Root cause

   unknown

   Component

   Prometheus

   Contributing factor

   –

   Detection

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

   Remediation

   • increased the Prometheus readiness probe timeout from 3 to 6 seconds

   Action items

   • Inspect more closely to identify this kind of failure earlier
   • Investigate whether the ingestion of data into the database is too big or too long
   • Check whether executing some queries makes Prometheus work harder and stop responding to the readiness probe
   • Check whether any other service probing Prometheus triggers the restart
   • Check whether regular Velero backups disturb EBS reads and writes and cause the restart

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