Cases2 cases

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1. IL-0108 An Alcide admission controller applied to the production cluster during a workshop made create and update requests on Deployments time out

   DFDS2020

   Symptoms

   At 14:41 CET on 4 November 2020 users reported timeouts when deploying to Hellman, the production Kubernetes cluster. The timeouts were confirmed at 15:04.

   Deploys failingControl plane lag

   Trigger

   During an Alcide workshop that began at 13:00 CET, an Alcide admission controller was applied to the production cluster at 13:13 in the belief that it would not disrupt anything.

   Root cause

   Configuration change

   Component

   Admission webhookKubernetes API server

   Contributing factor

   No configuration testsUnverified assumptionMonitoring blind spot

   Detection

   Users reported timeouts when deploying to the Hellman cluster at 14:41 CET; the timeouts were verified at 15:04.

   Remediation

   • toggled the Alcide admission controller to verify that it caused the timeouts
   • permanently deleted the Alcide admission controller

   Action items

   • Monitor Kubernetes deployments for timeouts
   • Monitor 5xx status codes from the Kubernetes API server

   Lessons

   New components are applied in sandbox clusters, not in production. The team needs enough Alcide knowledge to maintain the component until it is fully deprecated.

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2. IL-0075 Kubernetes API servers pegged on CPU when the cluster grew to about 290 nodes, traced to Datadog agents polling the API

   Civis Analytics2019

   Symptoms

   After Civis released its new script infrastructure on Kubernetes internally in September 2018, developers reported that kubectl commands were slow, and users intermittently could not start notebooks and services.

   Control plane lagControl plane unresponsiveHigh latencyHigh error rateDNS resolution failures

   Trigger

   Migrating clients' Python, R and container scripts from the in-house Bocce system to Kubernetes (the project called Croquet) pushed the cluster to about 290 EC2 instances under a large script load.

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serverTelemetry agentetcd

   Contributing factor

   Unknown limitUnverified assumptionMonitoring blind spotNo capacity planning

   Detection

   Developer reports of slow kubectl commands and user reports of notebooks and services failing to start, then high CPU on the master nodes.

   Remediation

   • moved internal users back to the old Bocce infrastructure
   • resized the three master nodes from m4.large to m4.4xlarge
   • changed the Datadog setup so per-node agents no longer polled the API server for service metadata
   • raised max-requests-inflight for non-mutating requests from 400 to 1200
   • grew the etcd EBS volumes from 20 to 100 GB (100 to 300 IOPS)
   • switched client scripts from the ClusterFirst DNS policy to Default

   Action items

   • Upgrade etcd from 2.2.1 to 3.x once Kops supports a safe migration
   • Solve the in-cluster DNS timeouts so client scripts can talk to other workloads in the cluster

   Lessons

   Kubernetes can reach thousands of nodes only after tuning, and third-party DaemonSets are a common cause of control plane load: an agent on every node that polls or watches the API server multiplies with the cluster size. Datadog, kube2iam and the fluentd metadata filter all showed this. A small configuration change often fixes it, but finding it took load testing and the API server access logs.

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