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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.

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2. IL-0168 Simultaneous cluster auto-upgrades overloaded Scaleway's Kubernetes API, and a node authentication retry loop kept Kapsule and Kosmos nodes NotReady in FR-PAR

   Scaleway2024

   Symptoms

   At 01:04 CET on 13 February 2024, the Kubernetes API behind Scaleway's managed Kubernetes services Kapsule and Kosmos in the Paris region (FR-PAR) spiked in RAM and CPU use and ran out of memory. Nodes in FR-PAR could no longer authenticate to their control plane, and some of them went "Not Ready". Customers on both shared (mutualized) and dedicated environments were affected.

   Node NotReadyControl plane lagOOMKilled

   Trigger

   A new Kubernetes release was made available to all users on 12 February 2024, and during the night of 12 to 13 February many clusters triggered their auto-upgrade at the same time.

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API server

   Contributing factor

   Retry stormAutomation amplificationNo staged rollout

   Detection

   The timeline's first entry, at 01:04 CET, records the Kubernetes API's RAM and CPU spikes and out-of-memory state, with nodes unable to authenticate to their control plane and some becoming "Not Ready". The first recorded action, 42 minutes later at 01:46, was to increase the resources allocated to the API.

   Remediation

   • increased the resources allocated to the Kubernetes API at 01:46 and again at 02:20
   • applied API traffic restrictions at 02:30 to smooth the incoming traffic
   • removed the traffic restrictions progressively in 50-100 batches from 03:20
   • ran a script at 03:40 to replace the failing nodes created during the congestion
   • rolled back to the former node authentication mechanism, first on a subset of clusters from 15:45 and on all clusters by 19:41 on 19 February
   • disabled cluster upgrade and configuration update features until 10:30 on 20 February

   Action items

   • Increase the resources allocated to the Kubernetes API
   • Roll back to the former node authentication mechanism
   • Implement a local authentication cache on nodes
   • Rework the resource allocation strategy to behave better under load spikes
   • Add new request queuing mechanisms for Kubernetes
   • Separate the different components to reduce side effects under high load or failure
   • Monitor selected metrics closely to identify anomalous patterns earlier
   • Refund customers who deployed nodes during the incident but could not use them normally

   Lessons

   Letting many clusters auto-upgrade at the same moment after a release turns routine upgrades into one coordinated load spike on the shared Kubernetes API. A node authentication mechanism that retries without letting up turns a slow API into an unavailable one, and auto-healing that replaces NotReady nodes amplifies the problem. The congestion pattern had already appeared three weeks earlier, and short-term mitigations did not stop it from recurring, so Scaleway rolled back the new authentication mechanism. Scaleway notes that as the number of customer clusters and nodes grows sharply, traffic control mechanisms have to improve to prevent congestion.

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