Cases69 cases

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1. IL-0465 IONOS Container Registry image pushes and pulls failed with 504 Gateway Timeout

   IONOS Cloud2026

   Symptoms

   On 4 March 2026 customers of the IONOS Container Registry got 504 Gateway Timeout errors when pushing or pulling container images, and deployments that relied on the registry were blocked. The status page first reported an increased error count on the registry at 09:23 UTC, saying customers might be unable to pull images.

   Image pull failuresHigh error rate

   Trigger

   A migration combined with a temporary capacity constraint placed two critical control plane components on the same proxy instance, despite anti-affinity rules; the shared proxy then reached its concurrent connection limit.

   Root cause

   Capacity exhaustion

   Component

   Container registry and its credentialsManaged Kubernetes control plane and provider APIPod scheduling and placement

   Contributing factor

   No headroomSingle point of failureNo alert on limit

   Remediation

   • Rolled back a component of the Kubernetes control plane to an earlier version
   • Provisioned additional proxy capacity
   • Moved the affected control plane components onto separate proxy instances

   Action items

   • Redesign registry-to-API connectivity so each node uses its own local proxy
   • Adjust alert thresholds to warn before proxy connection use approaches capacity
   • Deploy more infrastructure clusters and redistribute registries so no cluster exceeds safe capacity, with automation that keeps balancing load

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2. IL-0350 Hosts in an IONOS TXL cluster lost connectivity when an InfiniBand fabric resweep failed on a full multicast table

   IONOS Cloud2026

   Symptoms

   On 3 March 2026, during a scheduled maintenance to replace a faulty switch in a cluster in IONOS Cloud's TXL data center, monitoring alerts fired and the hosts in that cluster lost network connectivity. Customers saw intermittent network degradation or outages, and the Data Center Designer (DCD) web frontend and the AI Model Hub became unreachable.

   Network unreachableLink flappingService outage

   Trigger

   During a scheduled maintenance to replace a faulty switch, Topology 2 of 2 in the affected cluster was disabled as planned, and a link flap on a switch in Topology 1 then flooded the InfiniBand control plane with alerts and started a fabric resweep.

   Root cause

   Capacity exhaustion

   Component

   InfiniBand fabricNetwork control plane (SDN)BGP routingNIC, cable and switch port linkModel hubDatabase

   Contributing factor

   Automation amplificationExcessive blast radius

   Detection

   IONOS began investigating monitoring alerts in TXL; the first status update was posted at 11:36 UTC.

   Remediation

   • Switched from the affected control plane to its standby
   • Rebooted the locked-up gateways
   • Recovered Topology 1 from the scheduled maintenance
   • Paused provisioning in the cluster that had a backlog of queued jobs
   • Re-enabled provisioning once network connectivity improved and watched job execution
   • Re-established connectivity for the systems that support the DCD

   Action items

   • Adjusted the control-plane configuration to use less aggressive failover timers and reduced heavy sweep activity, lowering the load on the forwarding table
   • Reduced the number of multicast group memberships to relieve the forwarding table
   • Set up continuous monitoring of forwarding table use and control-plane memory with alerts before capacity limits are reached
   • Move the control plane to dedicated high-performance servers, separate from data-plane traffic
   • Audit IPv6 configurations and IPoIB driver settings
   • Modernise the interconnect fabric, including switches, gateways and drivers
   • Hardened IAM maintenance protocols so that critical identity services keep working when the primary Cloud APIs are temporarily unavailable
   • Move IAM to a dedicated platform without a dependency on the standard managed Kubernetes clusters
   • Introduce a dedicated provisioning provider for DBaaS and Managed Kubernetes
   • Add automated circuit breakers to the provisioning engine against overload and queue buildup from self-healing services
   • Improve visibility and control over job execution and prioritisation in the provisioning queue
   • Change the provisioning queue architecture to decouple services and reduce spillover

   Lessons

   IONOS identified the linking of services through the shared provisioning queue as a key reason the degradation lasted as long as it did. Alongside making each service more robust, it is decoupling the provisioning queue and addressing dependencies between services to reduce the blast radius of future incidents.

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3. IL-0181 A traffic spike from an external community post pushed product API pods into CPU throttling under a 1-core limit, and an e-commerce product page slowed down while every pod stayed Running

   Kevin2026

   Symptoms

   An e-commerce platform suddenly became slow to respond, worst on the product lookup page, and users began reporting that the page would not open.

   CPU throttlingHigh latency

   Trigger

   A post on the external community Ppomppu linked to the platform and brought a large number of users in a short time, who called the product lookup API heavily.

   Root cause

   Capacity exhaustion

   Component

   Container CPU limitHorizontal pod autoscaler

   Contributing factor

   Monitoring blind spot

   Detection

   Users reported that the product page would not open. The dashboard showed all pods Running, and the namespace CPU usage near 100% and the CPU usage metrics pointed to throttling.

   Remediation

   • raised the HPA minReplicas from 5 to 10
   • raised the container CPU limit from 1 core to 2 cores

   Action items

   • Keep adjusting the CPU limit to a suitable level through monitoring

   Lessons

   CPU throttling is hard to notice: pods stay Running, nothing restarts and the logs show no clear error, so a pod list does not reveal it and CPU usage metrics do. Settings that had handled expected event traffic became the bottleneck as soon as one external post multiplied the traffic. The author concludes that the team needs more preparation for traffic pattern changes and more thought about resource settings.

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4. IL-0581 29CM product display servers lost pods and saturated Netty event loops on the first day of the Igu Week sale

   MUSINSA2025

   Symptoms

   Shortly after 29CM's Igu Week sale began on 3 November 2025, the servers for the display domain, which serve search results and product listings, showed anomalies. Some pods went down and stopped taking traffic, and the remaining pods received more traffic than they could handle and saturated their Netty event loops.

   Probe failuresIntermittent connection failuresHigh latency

   Trigger

   Igu Week, 29CM's largest Black Friday sale and its busiest traffic of the year, raised Redis network traffic to about four times normal.

   Root cause

   Capacity exhaustion

   Component

   In-memory cacheLiveness and readiness probes

   Contributing factor

   Unknown limitMonitoring blind spotHealth check tied to a dependency

   Detection

   Anomalies on the display servers, pods going down and Netty event loops saturating, were the first sign. The Redis cause was found from Redis health check failure logs and then the Redis network metrics.

   Remediation

   • Scaled the Redis nodes up from cache.r7g.large to cache.r7g.2xlarge, about twice the baseline network bandwidth

   Action items

   • Extended the monitoring dashboard to show in real time whether network in and out bandwidth exceeds the baseline, with metrics such as aws.elasticache.network_bandwidth_out_allowance_exceeded and aws.elasticache.traffic_management_active
   • Added Datadog alerts that fire when network bandwidth crosses a threshold
   • Added a Caffeine local cache in front of Redis for data that changes rarely
   • Look into compressing cached data with Snappy to reduce Redis network bandwidth
   • Look into storing cached data as Protocol Buffers instead of JSON; measured on the cached Item Document data, Snappy and protobuf together are expected to cut the size by about 73%
   • Keep improving the architecture for traffic growth, building on the cache restructuring, stronger monitoring and read separation already applied

   Lessons

   CPU and memory metrics alone did not show the problem, so Redis needs network monitoring against the node's bandwidth baseline. Burst credits kept the nodes working for about two hours after traffic passed the baseline, which delayed the failure and made its cause slower to find.

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5. 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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