Cases5 cases

1 to 5 of 5

1. IL-0100 PostHog's feature flags pods were packed onto Kubernetes nodes that were too small for their CPU requests, and CPU saturation above 90% caused three outages in ten days through Redis overload, connection pool exhaustion and slow queries

   PostHog2025

   Symptoms

   On 21 October 2025 at 21:45 UTC a change to timeout handling was deployed to the feature flags service, and two minutes later automated monitoring reported higher error rates. The change was rolled back by 21:49, but error rates stayed high: about 38% of flag evaluation requests in the US datacenter returned errors. At 22:30 Redis metrics showed memory exhaustion on ElastiCache, at 22:35 Postgres connections spiked, and at 22:45 the team found a massive data transfer from Postgres into Redis. The main PostHog application was affected as well, because it shares the Redis instance with the flags service.

   Sustained high node CPUHigh error rateHigh latencyContainer crash loopDatabase overload

   Trigger

   On 21 October a deployment meant to reduce timeout errors (PR #39821); on 24 October the deployment of IP-based rate limiting (PR #40074) that saw all traffic as coming from the load balancer's IP; on 28 October an unrelated routine deployment that rolled the feature flag pods in the US region.

   Root cause

   Capacity exhaustion

   Component

   Node poolPod scheduling and placementIn-memory cacheApplication database

   Contributing factor

   No alert on limitRetry stormIneffective rollbackSingle point of failureNo staged rolloutUnrepresentative staging

   Detection

   On 21 October automated monitoring detected higher error rates two minutes after the deploy. On 24 October no alert existed for 429 errors, and the team learned of the incident from customer reports that began at 18:10 UTC, a 62-minute detection delay. On 28 October on-call received a high error count alert. On 29 October the incident was reported with higher error rates and latency. No alert existed for CPU pressure, so the CPU saturation behind all three incidents was not seen directly.

   Remediation

   • rolled back the timeout handling change within 2 minutes on 21 October, which did not stop the errors
   • throttled connections and Redis writes on 21 October
   • halted the rollout on 28 October, scaled the US flags pods to zero until Redis recovered, then scaled them back gradually
   • rolled back hardware changes on 30 October at 00:03 UTC, which removed most errors but not the latency
   • increased the pod resource requests and adjusted the Kubernetes fleet size so fewer pods ran on each node
   • added retry limits to Envoy between the load balancer and the service
   • moved database connection timeouts and other critical settings to runtime configuration and raised the timeout values
   • added per-node and per-pod CPU alerts at 80% sustained for 5 minutes that page on-call

   Action items

   • Form a strike team from the flags, ingestion and infrastructure teams to review the application and infrastructure for remaining bottlenecks
   • Investigate separating the flags Redis instance from the application Redis instance
   • Before re-enabling ArgoCD sync, align the flags service chart configuration with the values in ArgoCD and define the deployment and Redis strategy
   • Make the feature flags team hard code-owners for flags-related code
   • Build a dashboard of key flag metrics with runbook links and update the feature flag runbooks
   • Add missing alerts on existing service and infrastructure metrics
   • Make readiness checks validate dependencies that degrade under load, for example by pinging the database
   • Rate-limit cache population so it cannot overwhelm Redis
   • Monitor connection pools with automatic throttling near exhaustion and limit concurrent connections
   • Build a production-scale load testing framework
   • Build progressive rollout infrastructure to limit blast radius
   • Document Postgres-to-Redis data flows and write runbooks for data transfer storms

   Lessons

   Nodes that are small relative to pod requests let the scheduler pack many pods on each node, and the resulting CPU saturation showed up three different ways: excessive parallelism, connection pool exhaustion and slow queries. Treating each symptom separately let the problem return; right-sizing the pods and nodes fixed all three. CPU must be alerted on from the start. Unbounded Envoy retries and unbounded cache population amplified the overload, and a Redis instance shared with the main application spread a flags problem to the main application. A rollback did not end the incident, because the data transfer and resource exhaustion it had started kept running.

   View case
2. IL-0020 During an EKS 1.30 upgrade the descheduler tried to reschedule every pod on the cordoned nodes at once and production workloads went down

   UK Ministry of Justice2025

   Symptoms

   At 09:36 on 18 March 2025, during the upgrade of the live cluster to EKS 1.30, users reported that their pods were being terminated and were not restarting automatically, in both production and non-production environments. Automated infrastructure alerts fired as well, and production services had outages.

   Pod evictionPods PendingService outageUpgrade stall

   Trigger

   Upgrading the live EKS cluster from 1.29 to 1.30, during which the descheduler attempted to reschedule all pods on the cordoned nodes simultaneously.

   Root cause

   Faulty deployment

   Component

   DeschedulerNode poolCluster autoscaler

   Contributing factor

   No headroomNo alert on limitUnreviewed defaults

   Detection

   Users reported at 09:36 that pods were being terminated and not restarting, and automated infrastructure alerts fired.

   Remediation

   • Removed the cordon annotation so pods could be rescheduled
   • Deleted stuck 1.30 nodes to force their recreation
   • Added a new node group of 20 nodes running 1.30 by hand
   • Cordoned and drained all old 1.29 nodes onto the new 1.30 nodes

   Action items

   • Raise an AWS Production System Down support case (Case ID: 174229643100496)
   • Get guidance from AWS support on querying control-plane logs to find the root cause of similar issues
   • Document a runbook for emergency scaling of a new node group in case of unexpected capacity shortages
   • Alert on an imbalance between the autoscaling group and the Kubernetes nodes
   • Review the descheduler configuration to understand why it throws pods off cordoned nodes, and fix it where possible
   • Alert on spikes in the pending pod count

   Lessons

   The write-up lists review actions rather than lessons. They show the team had no documented way to add an emergency node group, no alert on a mismatch between the autoscaling group and the nodes Kubernetes sees, no alert on a spike in pending pods, and no clear understanding of how the descheduler treats cordoned nodes.

   View case
3. IL-0019 An EKS Terraform module upgrade shrank node root disks from 100 GB to 20 GB and image pulls failed with no space left on device

   UK Ministry of Justice2023

   Symptoms

   At 13:42 the RootVolUtilisation-Critical alert fired in the high-priority alerts channel. At 14:03 a user reported image pull errors with the message "no space left on device".

   Node disk fullImage pull failuresPod evictionPods stuck in ContainerCreatingIP address exhaustion

   Trigger

   Upgrading the EKS Terraform module to version 18, which changed the node launch template, and draining workloads onto the new nodes it created.

   Root cause

   Configuration change

   Component

   Node root diskNode launch templateNode poolAWS VPC CNICoreDNS

   Contributing factor

   Missed in reviewNo configuration testsNo alert on limit

   Detection

   The RootVolUtilisation-Critical alert fired in the high-priority alerts channel at 13:42, and a user reported "no space left on device" image pull errors at 14:03.

   Remediation

   • Uncordoned the old default node group and drained the new nodes to move workloads back
   • Identified the launch template change that cut the root volume to 20 GB and applied a fix on the manager and live clusters
   • Drained the old node group onto the fixed nodes

   Action items

   • Update the runbook to compare launch template changes during an EKS module upgrade
   • Create a test setup that pulls images of different sizes, like live
   • Update the RootVolUtilisation alert runbook to check the disk space configuration
   • Scale CoreDNS dynamically based on the number of nodes
   • Investigate whether IPv6 can solve the IP prefix starvation problem
   • Add drift testing to identify when a terraform plan shows a change to the launch template
   • Set up logging for the CNI and ipamd logs and alerts on errors related to IP prefix starvation

   Lessons

   The write-up lists review actions rather than lessons. They aim at two gaps: the launch template changes brought by a module upgrade were not compared or drift-tested before rollout, and there were no CNI or ipamd logs or alerts to show the IP prefix starvation the team suspected during the drain.

   View case
4. 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
5. IL-0013 The conntrack table filled up on downsized GKE nodes and a high-traffic aggregation proxy saw climbing connection resets

   loveholidays2020

   Symptoms

   Shortly after a round of changes, loveholidays saw connectivity problems and raised error rates on its aggregation service. The service is a proxy between two other services of more than 100 instances each, handles over 10,000 requests per second continuously and can spike to four times that.

   Connection resetsHigh error rateIntermittent connection failures

   Trigger

   Three changes together: a change in service architecture, halving the RAM of the nodes that run the aggregation service, and sharding a downstream service, which doubled the number of connections to track.

   Root cause

   Capacity exhaustion

   Component

   conntrack tablekube-proxyNode poolHAProxy

   Contributing factor

   Unknown limitNo capacity planningNo alert on limit

   Detection

   Rising error rates and climbing connection reset errors in HAProxy, then node_exporter conntrack metrics compared with their limit.

   Remediation

   • moved the aggregation service to nodes with more RAM
   • limited the number of aggregation service pods per node

   Action items

   • Alert when conntrack table use goes above 75%, with (node_nf_conntrack_entries / on (pod) node_nf_conntrack_entries_limit / on (pod) group_right kube_pod_info) > 0.75

   Lessons

   The conntrack limit is tied to node memory, so resizing a node pool silently resizes the connection table, and on GKE the ratio did not match the commonly cited formula. A high-traffic proxy on small nodes can exhaust it, and the symptoms are connection resets and timeouts, so conntrack use needs its own metric and alert.

   View case

By root cause3