Cases3 cases

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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 cacheDatabase

   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-0150 A latent race condition in DynamoDB's DNS automation left an empty DNS record for dynamodb.us-east-1.amazonaws.com, and the DynamoDB and EC2 launch failures that followed caused EKS, ECS and Fargate container launch failures and cluster scaling delays in us-east-1

   Amazon Web Services2025

   Symptoms

   At 11:48 PM PDT on 19 October 2025, every system that reached DynamoDB in us-east-1 through the public regional endpoint started getting DNS failures and could not connect. This included customer traffic and internal AWS services that depend on DynamoDB. DynamoDB global table replicas in other regions still answered but fell behind in replication to and from us-east-1.

   DNS resolution failuresHigh error rateScale-up failureIntermittent connection failuresHigh latency

   Trigger

   One DNS Enactor, slowed by unusually high retry delays, applied a much older DNS plan to the regional DynamoDB endpoint just as another Enactor that had applied the newest plan ran its clean-up, which deleted that older plan and with it every IP address of the endpoint.

   Root cause

   Software defect

   Component

   Authoritative DNS servers and recordsDatabaseCloud load balancer

   Contributing factor

   Automation amplificationRetry stormNo runbook

   Detection

   DNS failures to DynamoDB began immediately at 11:48 PM PDT and the engineering teams of affected AWS services were engaged at once; by 12:38 AM they had identified DynamoDB's DNS state as the source. The later NLB health check problem was detected by monitoring at 6:52 AM.

   Remediation

   • applied temporary mitigations by 1:15 AM that let some internal services reach DynamoDB and repaired key internal tooling
   • restored all DynamoDB DNS information by 2:25 AM through manual operator intervention
   • throttled incoming work and selectively restarted DWFM hosts from 4:14 AM so droplet leases could be re-established
   • reduced the load on Network Manager to bring network configuration propagation times back to normal by 10:36 AM
   • disabled automatic health check failover for NLB at 9:36 AM, and re-enabled it at 2:09 PM after EC2 recovered
   • relaxed the EC2 request throttles from 11:23 AM until all EC2 APIs and launches were normal at 1:50 PM

   Action items

   • Disable the DynamoDB DNS Planner and DNS Enactor automation worldwide
   • Fix the race condition and add protections against applying incorrect DNS plans before re-enabling the DNS automation
   • Add a velocity control to NLB that limits how much capacity a single load balancer can remove when health check failures cause availability zone failover
   • Build an additional EC2 test suite that exercises the DWFM recovery workflow at scale
   • Improve throttling in EC2 data propagation systems so incoming work is rate limited by the size of the waiting queue

   Lessons

   A single DNS record that automation can empty is enough to take a widely used regional endpoint off the network, and every service built on it fails in turn. A check made once at the start of a long operation can be stale by the time the operation writes its result. Recovery of the dependency is not recovery of the dependents: systems that lost state during the outage, such as EC2's droplet leases and network propagation backlog, can collapse under their own catch-up work, and container services that rely on new instance launches keep failing to launch and scale until that work is done.

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3. IL-0202 A burst of queries and database writes exhausted connection pools and memory on Honeycomb's ingest hosts, and Kubernetes crash-loop back-off kept failed hosts offline so ingest capacity stayed too low to recover

   Honeycomb2023sev1

   Symptoms

   Standard alerting paged Honeycomb's on-call engineers about the performance and stability of event ingestion, and the automated load-shedding mechanisms tripped and sent further notifications. The status page described a higher than normal error rate and response time on the ingestion service.

   High error rateHigh latencyContainer crash loopService outageDatabase overload

   Trigger

   An abnormally large number of queries arrived from many separate connections within a few minutes.

   Root cause

   Capacity exhaustion

   Component

   DatabaseIn-memory cache

   Contributing factor

   Retry stormAutomation amplification

   Detection

   Standard alerting notified the on-call engineers about ingestion performance and stability, and the automated load-shedding mechanisms added notifications when they tripped. The first status post, about alerts on a higher than normal error rate and response time, went up at 18:28 PST (02:28 UTC), 13 minutes after the 02:15 UTC start.

   Remediation

   • Scaled the ingest fleet up manually and aggressively
   • Cut off most ingest for a few minutes so the ingest hosts could come back with enough capacity
   • Temporarily lowered the rate limits for the most aggressive traffic sources until their traffic subsided

   Action items

   • Keep the ingest fleet scaled up to prevent a repeat while longer-term fixes are worked on
   • Decide on the projects that will address these failure paths

   Lessons

   Load shedding and auto-scaling were in place, yet a burst that arrived faster than the cache could spread its writes still took the ingest hosts down. During recovery, Kubernetes' crash loop back-off worked against the operators: the hosts that had failed stayed offline while the fleet needed every host. Clients that retry whole OTel batches after a partial drop can multiply traffic many times over, so per-source rate limits were what finally stabilised ingest.

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