Cases8 cases

1 to 5 of 8

1. IL-0058 Argo CD deleted namespaces shared by several applications, leaving about 97 applications stuck Missing and OutOfSync

   Argo CD users2026

   Symptoms

   After a rolling restart of the Argo CD application-controller, namespaces that several applications shared went into the Terminating state. About 97 applications were then stuck in a Missing/OutOfSync deadlock.

   Resources deleted

   Trigger

   A manual bulk sync with force: true from the Argo CD UI, run during the window after an application-controller restart, on an application whose manifests declared a namespace whose app.kubernetes.io/instance label pointed to a different application.

   Root cause

   Software defect

   Component

   Argo CD

   Contributing factor

   No safeguard on destructive changeUnverified assumptionHarmful manual action

   Action items

   • Refile the problem as #29739: a manual force sync hard-replaces shared cluster-scoped resources through a tracking-ID conflict, ignoring Prune=false

   Lessons

   The sync option Prune=false only protects the prune path; it does not stop a force sync from deleting and recreating a resource. When two Argo CD applications declare the same namespace or other cluster-scoped resource, force-syncing the application that does not own the tracking label can delete it at any time.

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2. IL-0171 Karpenter node auto-repair replaced EKS GPU nodes about 10 minutes after they became Ready, in a loop, because the Bottlerocket NVIDIA AMI lacked libdcgm.so and set AcceleratedHardwareReady to False

   Inflab2025

   Symptoms

   GPU nodes for an AI video upscaling job were created normally and reached Ready, and then about 10 to 11 minutes later Karpenter treated each one as unhealthy and replaced it. Every new node went the same way, so the GPU pods kept restarting and the upscaling work could not make progress.

   Node recreation loopGPU unavailable

   Trigger

   Enabling Karpenter's node auto-repair feature gate (nodeRepair: true) and then running GPU pods on nodes from the Bottlerocket NVIDIA AMI.

   Root cause

   Software defect

   Component

   KarpenterNode operating system imageGPU driver

   Contributing factor

   Automation amplificationUnverified assumption

   Detection

   The team noticed GPU nodes being replaced repeatedly after running GPU pods, then found the DCGMError message in the node events.

   Remediation

   • Disabled Karpenter node auto-repair by setting the nodeRepair feature gate to false

   Lessons

   Automatic node repair acts on every unhealthy node condition, including AcceleratedHardwareReady, so an image defect that only breaks GPU health monitoring is enough to have every GPU node terminated and recreated in a loop. Before turning on auto-repair, check which conditions it watches and whether the node image reports them correctly. Disabling auto-repair stops the loop at once but is only a temporary measure; installing the missing library or changing the AMI address the cause but cost more on an immutable OS such as Bottlerocket.

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3. IL-0029 Slow user-facing services from CPU throttling on containers that used far less CPU than their limit

   Buffer2020

   Symptoms

   Services on Buffer's kops-managed cluster on AWS (about 60 nodes and 1500 containers) answered more slowly than they should have. The container throttling metric showed that many containers were being throttled whatever their CPU usage was, including containers that never came close to their CPU limit.

   CPU throttlingHigh latencyNode NotReady

   Trigger

   CPU limits on user-facing services, enforced through CFS quota by a Linux kernel with a bug that throttled containers even when they were well under their limit.

   Root cause

   Software defect

   Component

   Container CPU limitLinux kernelNode operating system image

   Contributing factor

   Unpatched known bugUnverified assumption

   Detection

   While reviewing the container throttling metric, the team noticed many containers being throttled even though their CPU usage stayed far below their limits.

   Remediation

   • removed CPU limits from all services directly or indirectly on the users' critical path
   • moved those services to dedicated tainted nodes and gave them matching tolerations
   • set each service's CPU request to its peak usage over a few months plus a margin of more than 20%, and set memory requests the same way

   Action items

   • Move nodes to a kernel that contains the CFS throttling fix (4.19 or later), which for Buffer means a kops version whose host image is Ubuntu 20.04
   • After the kernel upgrade, check whether throttling is fully gone and update the post

   Lessons

   A CPU limit can slow a service down even when the service uses a small part of it, because of how the kernel enforces CFS quota on older kernels; the throttling metric shows it, the CPU usage graph does not. Upgrading the kernel is the safer fix. Removing limits works for latency, but only together with honest CPU and memory requests, isolation of the unbounded services, and monitoring of node usage, since unbounded containers can starve the kubelet.

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4. IL-0196 Adding a second Redis replica and lowering memory limits in one RedisFailover change restarted the only Redis pod that held data, and Sentinel promoted the empty new replica, erasing the data

   Palark2020

   Symptoms

   Right after an updated RedisFailover resource was rolled out, the large Redis database lost all its data and the service went down; the data had to be restored from a backup taken just before the change.

   Data lossService outage

   Trigger

   A RedisFailover change that raised the number of Redis replicas from one to two and, in the same rollout, lowered the containers' memory requests and limits from 22 GB to 19 GB.

   Root cause

   Software defect

   Component

   Application databasePersistent volumes and stateful setsContainer memory limit

   Contributing factor

   No staged rolloutSingle point of failureUnverified assumption

   Remediation

   • restored the Redis data from the backup prepared before the rollout

   Action items

   • Report the readiness probe flaw to the Redis Operator project and test the developers' fix
   • Reconsider the use of the Spotahome Redis Operator
   • Clean up the Redis databases and fix the application code that created the excess keys
   • Consider Redis built-in clustering and sharding

   Lessons

   Changing the replica count and the pod resources in one rollout turned a seemingly harmless change into a rolling restart of the only pod with data while the new replica was still empty. A readiness probe that only checks that Redis answers a ping does not tell whether a replica has finished syncing. Operators for critical stateful components need to be evaluated for maturity before production use, and the dump.rdb file should be backed up before any change to the Redis cluster.

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5. IL-0062 Intermittent 502 errors behind a GKE Ingress because pods shut down before the load balancer stopped sending them traffic

   Ravelin2019

   Symptoms

   After Ravelin moved its API layer from VMs into its GKE cluster behind a Kubernetes Ingress, the integration tests occasionally received 502 errors.

   Intermittent connection failuresStale endpoint routing

   Trigger

   Pods behind the Ingress being removed by their controller, as happens during rolling deploys and scaling.

   Root cause

   Software defect

   Component

   Ingress controllerPod termination

   Contributing factor

   Unverified assumption

   Detection

   Integration tests occasionally received 502 errors.

   Lessons

   Removing a pod's endpoint and signalling the pod happen at the same time, and an ingress can take a long time to catch up. A pod behind an ingress should keep serving after SIGTERM until its grace period ends, and the grace period should be longer than the load balancer's reprogramming time.

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