Cases46 cases

29 to 33 of 46

1. IL-0032 Mutating admission controller generated broken JSON patches, silently blocking deploys for a week until a production service ran down to two pods

   Airbnb2020

   Symptoms

   A production service at Airbnb showed a high error rate and could not be deployed. The deploy failed with a cryptic Kubernetes error about being unable to access invalid index 5.

   High error rateThroughput degradationDeploys failing

   Trigger

   A change to the mutating admission controller, which had been rolled out within the previous month, had gone in seven days before the incident surfaced; when it had to delete two items from an array in a spec, it generated an invalid JSON patch that removed index 2 and then index 3.

   Root cause

   Software defect

   Component

   Admission webhookHorizontal pod autoscaler

   Contributing factor

   Change with a delayed effectMonitoring blind spotIgnored early signal

   Detection

   The affected service reported a high error rate and its deploy failed; the slow degradation of other services over the previous seven days had gone unnoticed.

   Remediation

   • deleted the mutating admission controller from the affected cluster

   Action items

   • Create alerts from the existing metrics that would have shown the problem earlier

   Lessons

   Keep in mind that a mutating admission controller exists in the cluster; a recently added one is easy to forget during an incident. Search for the exact error message early instead of working on the visible symptoms. Page a teammate when progress stalls.

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2. IL-0146 New AKS clusters with Azure CNI in Central India lost tunnel connectivity because VMs began getting MAC addresses with a 06 prefix, which exposed a dormant bug in bridge-mode Azure CNI

   Microsoft Azure2020

   Symptoms

   In newly created AKS clusters using Azure CNI in Central India, kubectl exec and kubectl logs could time out, and pod-to-pod or pod-to-node traffic could time out. The cluster diagnostics page showed tunnel exec issues. Clusters using kubenet were not affected.

   Intermittent timeouts

   Trigger

   VMs in Central India started being assigned MAC addresses in a much higher range than before (06 prefix rather than the usual 00).

   Root cause

   Software defect

   Component

   Azure CNINode operating system image

   Contributing factor

   –

   Remediation

   • told owners of affected scale-set clusters to run a node image upgrade, or any Kubernetes upgrade such as a patch version, to get the Azure CNI that does not use a bridge
   • told owners of availability-set clusters to scale out and delete the VMs with a high-range MAC address in bridge mode
   • advised recreating affected clusters that were brand new

   Action items

   • Roll out a hotfix that makes transparent mode the default in all regions

   Lessons

   A change in how the platform assigns MAC addresses can wake a long-dormant bug in the network plugin. Knowing which CNI mode the nodes run (bridge or transparent) and keeping node images current decides whether such a bug reaches you.

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3. IL-0041 fluent-bit silently stopped shipping application logs from one node to Elasticsearch

   PrometheusKube2020

   Symptoms

   On a Tuesday morning a developer reported that some production logs were missing. Only the logs from a single node were gone; the other nodes kept shipping normally through fluent-bit, the log shipper the team had run for more than three years.

   Monitoring outage

   Root cause

   Software defect

   Component

   Telemetry agentElasticsearch or OpenSearch

   Contributing factor

   Monitoring blind spotUnpatched known bug

   Detection

   A developer reported missing production logs; no alert fired because fluent-bit's metrics showed no errors.

   Remediation

   • added CPU to the Elasticsearch virtual machines, which cleared the write queue rejections
   • rolled out Fluentd one node at a time, using node affinity to place Fluentd and anti-affinity to remove fluent-bit
   • lowered Fluentd's flush_thread_count to 1 and set flush_interval to 5 seconds so the backlog of old logs did not overflow the buffer
   • enabled Fluentd's Position DB so already-ingested data was skipped

   Action items

   • Build monitoring for Fluentd on its Prometheus integration, including a throughput alert with separate day and night thresholds or a black-box log check

   Lessons

   A log shipper can fail without an error message and with metrics that simply stop moving, so error-based alerts never fire; throughput alerts or end-to-end checks can catch it. Open source software can break in ways nobody expects, and sometimes the way out is to switch to a different shipper fast, node by node. A new shipper that starts from scratch replays every old log on the node and needs to be slowed down.

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4. 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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5. IL-0190 Cortex distributors kept a stuck connection to a dead etcd leader after a Kubernetes upgrade terminated its node, failing writes to Hosted Prometheus for 12 minutes

   Grafana Labs2020

   Symptoms

   About a minute after the etcd leader changed, two replicas of the Cortex distributor started logging "context deadline exceeded" when they tried to read keys from etcd. Writes from some customers that landed on those two replicas failed, while the other distributor replicas kept working, so the service saw an error rate of about 20% on writes to the London region of Hosted Prometheus.

   High error rateIntermittent timeouts

   Trigger

   A scheduled Kubernetes version upgrade abruptly terminated the node that ran the etcd leader at 11:19:00 UTC.

   Root cause

   Software defect

   Component

   etcdNode pool

   Contributing factor

   Unreviewed defaultsNo safeguard on destructive change

   Detection

   An SLO-based alert paged the on-call engineer at 11:31, 11 minutes after the errors started. At a 20% error rate the monthly SLA would have taken about 18 hours to breach.

   Remediation

   • Restarted the two affected Cortex distributor replicas at 11:32, which stopped the errors

   Action items

   • Enable gRPC keepalive probes in the Cortex etcd client (DialKeepAliveTime 10s, DialKeepAliveTimeout, PermitWithoutStream)
   • Make the Kubernetes upgrade process terminate pods and machines gracefully, including the etcd pods managed by the etcd operator

   Lessons

   When a node dies abruptly, clients holding TCP connections to it are not told; without keepalive probes an etcd client can keep sending requests into a dead connection long after a new leader exists. A failover that works on the server side still needs clients that detect the dead peer. Node replacement during cluster upgrades should drain pods gracefully, including pods owned by operators that the upgrade scripts do not know about, and should give instances enough time to shut down.

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