Cases12 cases

9 to 12 of 12

1. IL-0037 Image registry rate-limited a whole region after a crash-looping DaemonSet with imagePullPolicy Always pulled through three NAT instances

   Datadog2019

   Symptoms

   Users reported that their jobs were not starting, with some sign that image pulls were involved. Many pods across the cluster were in error states related to image pulls, while applications that were already running were not affected.

   Image pull failuresService outage

   Trigger

   A change to the permissions on a storage bucket that one application needed to read. The application could no longer start, and its DaemonSet, running on roughly a thousand nodes of the cluster, went into a crash loop with imagePullPolicy: Always, so every restart pulled the image again although it was already on the node.

   Root cause

   Capacity exhaustion

   Component

   Container registry and its credentialsNAT gatewayApplication databaseAdmission webhook

   Contributing factor

   Automation amplificationSingle point of failureUnknown limitHarmful manual actionExcessive blast radius

   Detection

   Users reported that their jobs were not starting; monitoring had not caught it first.

   Remediation

   • replaced the affected NAT instances so registry traffic came from new source IPs
   • reconfigured the static IPs and firewall rules so applications could reach the Cloud SQL database again

   Action items

   • Add an admission webhook that rejects floating image tags, starting with latest
   • Apply the image tag webhook to workload objects only, not to pods, after it blocked pods created by in-cluster controllers

   Lessons

   The speakers' takeaway is to be very careful with DaemonSets in large clusters: they say they had broken everything DaemonSets depend on, including DNS, Vault and the image repository, and that it is very easy to break things when managing them by the thousand. imagePullPolicy: Always made every crash-loop restart pull the image again. Sending all registry traffic through three NAT instances put the whole region behind three source IPs, which the registry rate-limited, and changing those IPs cut the connections to a critical database reached over the internet through them. An admission webhook on pods rejects the pods that controllers in the cluster create, where no user sees the error.

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2. IL-0064 CPU-heavy pods packed onto one Google Cloud node drove it to kernel panics, and the crash moved from node to node until the site was down

   Moonlight2019

   Symptoms

   On Friday, January 18, Moonlight's API began returning occasional errors that looked like connection problems with its Redis database. The API checks Redis on every authenticated request to validate the session. At the same time the Kubernetes monitoring reported some nodes and pods as unresponsive.

   High error rateIntermittent connection failuresNode NotReadyService outageSustained high node CPU

   Trigger

   The Kubernetes scheduler put several CPU-heavy pods on the same node, including all three replicas of the website and the server-side rendering pods.

   Root cause

   Capacity exhaustion

   Component

   Pod scheduling and placementLinux kernelApplication database

   Contributing factor

   Single point of failureUnverified assumptionAutomation amplification

   Detection

   API errors that looked like Redis connectivity problems, and Kubernetes monitoring that showed nodes and pods as unresponsive. Google Cloud Support later found the link between sustained 100% CPU and kernel panics.

   Remediation

   • added anti-affinity rules to all major deployments so their pods spread across nodes

   Action items

   • Keep working with Google Cloud support to find and fix the cause of the node kernel panics

   Lessons

   Three replicas give no fault tolerance if the scheduler places them all on one node; anti-affinity rules make the spread explicit. A node failure that comes from the workload itself moves with the workload when the scheduler reschedules it.

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3. IL-0031 Kubernetes API server OOM-killed, leaving the Skipper ingress without routes; the talk points to a misconfigured CronJob manifest

   Zalando2018

   Symptoms

   Customers were affected, and Zalando's IAM service answered requests with HTTP 404. The talk shows this on slides titled "Customer impact" and "IAM returning 404", followed by a slide on the number of pods in the cluster.

   High error rateControl plane unresponsiveOOMKilled

   Trigger

   A CronJob (apiVersion batch/v2alpha1, schedule "*/15 9-19 * * Mon-Fri") whose concurrencyPolicy: Forbid and job history limits sat inside the pod template spec instead of the CronJob spec. The talk shows it right after the slides on the number of pods and the API server being OOM-killed, and the lessons add a quota on the number of pods.

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serverIngress controller

   Contributing factor

   Single point of failureExcessive blast radius

   Remediation

   • fixed the CronJob manifest: moved concurrencyPolicy: Forbid and the job history limits to the CronJob spec, added activeDeadlineSeconds: 120 and changed the schedule to "7 8-18 * * Mon-Fri"

   Action items

   • Make the ingress stay healthy during API server problems
   • Make the ingress keep the last known set of routes
   • Use a ResourceQuota on the number of pods (the example sets pods: "1500")

   Lessons

   An AWS ALB routes traffic to all hosts when all hosts report unhealthy, so the ingress should stay healthy during API server problems. An ingress controller that reads routes from the API server should keep its last known set of routes. Use a quota for the number of pods.

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4. IL-0040 Parallel Helm installs saturated small gp2 root volumes on a kops cluster and left all three workers NotReady with the kubelet or Docker down

   Saltside2017

   Symptoms

   While running the CI process on a local machine to test an application Helm chart, the SRE lead watched the pods with watch kubectl get pods. All pods reached Running, and then some went back to Pending. Two hours later the problem had grown and some pods showed the phase Unknown.

   Pods PendingImage pull failuresNode NotReadyService outage

   Trigger

   A CI run installed the application's Helm charts for all four markets (Efritin.com, Tonaton.com, Ikman.lk and Bikroy.com) on the nonproduction cluster at the same time.

   Root cause

   Capacity exhaustion

   Component

   Node root diskkubeletContainer runtimeHelm

   Contributing factor

   Unreviewed defaultsMonitoring blind spotIgnored early signalSingle point of failure

   Detection

   The SRE lead saw pods go from Running back to Pending while watching kubectl get pods during the CI run; no monitoring raised an alert, even when all three workers were not ready.

   Remediation

   • terminated the three worker nodes and let the auto scaling group replace them

   Action items

   • Fix the Datadog integration for the nonproduction AWS account and repeat the installs with telemetry
   • Increase the worker root EBS volumes from 20GB to 120GB
   • Keep common base images pulled on every node with a DaemonSet until kops supports user hooks
   • Configure different allocation limits for the different layers in Kubernetes
   • Discuss a highly available Tiller with the Helm team
   • Find a way to throttle the number of newly created pods

   Lessons

   An ordinary deploy of the application could collapse the cluster, because the default 20GB gp2 root volumes could not keep up with parallel image pulls and container starts. Without node metrics and pod phase metrics, three NotReady workers raised no alert. A single Tiller replica can block the deploy that would fix an outage.

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