Cases13 cases

6 to 10 of 13

1. IL-0077 EKS cluster went read-only for over seven hours after Kyverno ephemeralreports filled the etcd database past its size limit

   Marcin Cuber2024sev1

   Symptoms

   A production EKS cluster on Kubernetes 1.30 went into read-only mode: the API server rejected every request that would change an object, such as creating pods or scaling deployments, and deleting objects to free space did not work either.

   Control plane unresponsiveControl plane lag

   Trigger

   Kyverno 1.12 kept creating ephemeralreports objects that piled up in etcd until the database passed EKS's size limit.

   Root cause

   Software defect

   Component

   etcdKubernetes API serverPolicy engine

   Contributing factor

   No alert on limitLimited debug access

   Detection

   The cluster rejected writes; the audit logs carried the "etcdserver: mvcc: database space exceeded" error.

   Remediation

   • AWS raised the etcd storage limit from 8/10GB to 12GB to unlock writes
   • an AWS engineer deleted the leaked Kyverno ephemeralreports objects directly from etcd

   Action items

   • Monitor the etcd database size with apiserver_storage_size_bytes and never let it reach 8GB
   • Upgrade Kyverno 1.12 to 1.12.4, which fixes the ephemeralreports pile-up, or disable reporting for admission events and tune --aggregationWorkers

   Lessons

   On EKS, once etcd hits its size limit the cluster becomes read-only and only AWS can clean it up, so cleanup scripts must run before the limit is reached. Watch the etcd database size and object counts per resource, especially for controllers that create report objects.

   View case
2. IL-0153 DOKS worker nodes went NotReady at 06:00 UTC on three days because a systemd timer in the Debian image ran apt upgrade and killed it mid kernel upgrade, breaking node networking

   DigitalOcean2023

   Symptoms

   Customers of DigitalOcean Managed Kubernetes (DOKS) saw worker nodes go NotReady between 06:00 and 07:00 UTC and stay unavailable, taking down the services on them, until the nodes were replaced or rebooted; some nodes needed several replacements or reboots. The first sporadic reports on October 8 said replacing the node fixed it. The same thing happened again around 06:00 UTC on October 9, more widely and across many regions, and again on October 10. Only clusters on versions 1.27.6-do.0 and 1.28.2-do.0 were affected.

   Node NotReadyService outage

   Trigger

   A systemd timer in the DigitalOcean Debian image started apt upgrade daily at 06:00 UTC, with a random delay of up to 60 minutes.

   Root cause

   Configuration change

   Component

   Node operating system imageLinux kernelkubelet

   Contributing factor

   Unreviewed defaultsLimited debug accessChange with a delayed effect

   Detection

   Sporadic customer reports of failed worker nodes arrived at 07:43 UTC on October 8; internal incident response started at 07:10 UTC on October 9 after broader impact across regions.

   Remediation

   • Advised customers to replace or reboot affected worker nodes
   • Arranged SSH and console access to worker nodes while they were healthy, and live-debugged an affected node on October 10
   • Released a DaemonSet to all affected clusters that disables the apt upgrade systemd timer on every worker node
   • Released new node images without the auto-update process

   Action items

   • Disable the timer during node provisioning, then remove the DaemonSet from all clusters
   • Add a check to the internal conformance test suite for new cluster versions that the auto-upgrade process and other unwanted timers stay disabled
   • Support logging into worker nodes even when normal access paths are broken

   Lessons

   An operating system auto-update timer left enabled in a node image can change the kernel and systemd underneath Kubernetes, and a timeout that kills the upgrade half-way can leave a node without networking. Failures that start at the same time every day point to a scheduled job on the nodes, and systemd timers belong on that list next to cron jobs. When the failure takes out a node's network, an investigation that depends on logging into the node stalls; access paths that work while the node is broken shorten diagnosis.

   View case
3. IL-0174 After an upgrade to Fluent Bit 2.0, logging sidecars that used the Kafka output plugin kept restarting with SIGSEGV because the plugin read a corrupted record as a map without checking its type

   Hyperconnect2023

   Symptoms

   Hyperconnect upgraded the Fluent Bit sidecar of one service with little user impact to Fluent Bit 2.0 and watched it for several days. Only the containers that used the Kafka output plugin were being restarted, each time on SIGSEGV. The stack trace printed at the crash pointed into the Kafka output plugin, but no core file was left behind, because Fluent Bit ran inside Kubernetes.

   Container crash loop

   Trigger

   Upgrading the Fluent Bit logging sidecar to version 2.0 (the 2.0.3 image) on a service with low user impact.

   Root cause

   Software defect

   Component

   Telemetry agent

   Contributing factor

   Limited debug access

   Detection

   The team monitored the upgraded sidecars for several days after the upgrade and saw that the containers using the Kafka output plugin were restarting with SIGSEGV.

   Remediation

   • patched the Fluent Bit Kafka output plugin to skip data that is not a map before calling produce_message()
   • ran a custom build with the patch as the sidecar, which stopped the segmentation faults
   • submitted the patch upstream as fluent-bit pull request 6452, which was merged eight days later

   Lessons

   A segmentation fault in a container can be debugged from its core file, but Kubernetes does not keep one by default: a volume mounted into the pod and a core_pattern pointing into it on the node are needed first. The debug image of the same version, run locally on the same CPU architecture with the matching source checkout, gives a gdb environment that matches production. Core files contain part of the process memory and may hold sensitive data, so they should be shared with care.

   View case
4. IL-0125 A GPU died about once a week during a 176B-parameter training run, and sometimes the job hung instead of exiting, so SLURM never restarted it

   BigScience2022

   Symptoms

   During BigScience's 176B-parameter multilingual training (tr11-176B-ml) on the Jean Zay cluster, launched on 2022-03-11, one of the GPUs died about once a week. Most of the time the SLURM job crashed and restarted automatically from the last checkpoint.

   Training job failureGPU unavailable

   Trigger

   A GPU on one of the training nodes failed during the run.

   Root cause

   Hardware failure

   Component

   NCCLPyTorchSlurm job scheduler

   Contributing factor

   Unreviewed defaultsLimited debug access

   Detection

   There was no automatic detection of a hung job; it was noticed only when someone watching the training saw that it was stuck.

   Remediation

   • raised checkpoint frequency to every 200 iterations and then to every 100 iterations (SAVE_INTERVAL=100)
   • set NCCL_ASYNC_ERROR_HANDLING=1 so NCCL problems crash the job instead of hanging it
   • added the srun options --wait=60 and --kill-on-bad-exit=1 after testing them on 2 nodes

   Lessons

   On a long run across hundreds of GPUs, a dead GPU every week is routine; what costs time is the case where a process crashes and the job hangs instead of exiting, because then nothing restarts it. Making NCCL errors fatal and having the scheduler end the whole step on a bad exit is meant to turn such a hang into a crash that the existing automatic restart handles; only the srun part was tested. Frequent checkpoints bound the loss per crash, at the price of save time for a 2.3TB checkpoint.

   View case
5. IL-0113 The managed GKE control plane of Webhook Relay's main cluster failed because its API server certificates had never been rotated, and a manual credential rotation reported success without taking effect

   Webhook Relay2022

   Symptoms

   At 12:00 EST the health check system reported that the backend APIs, the frontend and the tunnels in the EU region were down. The team first took it for a transient error, such as an interrupted client request or a server evicted for memory or CPU pressure, but the main dashboard was offline as well.

   Service outageControl plane lagControl plane unresponsiveStale or inconsistent API readsCredentials unavailable

   Root cause

   Certificate or credential expiry

   Component

   GKE control planeKubernetes API serverNode poolRBAC and service accounts

   Contributing factor

   Limited debug accessSingle point of failure

   Detection

   The health check system sent a notification that the service was down; the team then found the main dashboard offline.

   Remediation

   • recreated the node pools by hand after the credential rotation failed to recreate them
   • duplicated the main cluster's services into a newly created GKE cluster
   • deleted the Kubernetes Service records in the old cluster so its load balancers could be attached to the new cluster
   • detached the StatefulSet from the old cluster and switched all writes to the main disk
   • exported the data from the temporary ClickHouse instance and inserted it into the main instance

   Action items

   • Change the deployment manifests so services can be switched between clusters quickly without a complicated persistent disk migration

   Lessons

   Recovery of existing infrastructure needs a time limit. Throughout the outage the old cluster looked about to recover, and the reluctance to give up on the salvage effort kept the team from starting over with a new cluster much sooner, which would have been the right call. On older GKE clusters, check the validity of the cluster CA certificate, for example by decoding masterAuth.clusterCaCertificate from gcloud container clusters describe and reading it with openssl x509. GKE's multiple availability zones made no difference in this failure.

   View case

By root cause8