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Autoscaling in Kubernetes

One of the most power features of orchestration tools such as Kubernetes is the ability to automcatically scale resource allocation in response to real-time changes in resource usage. In the context of continuous deployment, this provides a great deal of stability, with less need for human intervention. In this lesson, you will learn the basics of autoscaling in Kubernetes by creating a simple Horizontal Pod Autoscaler which will create and destroy pod replicas in response to CPU utilization.

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Path Info

Clock icon Intermediate
Clock icon 1h 30m
Clock icon Jul 08, 2018

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Table of Contents

  1. Challenge

    Install the Kubernetes metrics API in the cluster.

    To accomplish this, do the following:

    • Clone the Kubernetes metrics repo.
    git clone
    • Apply the standard configurations to install the Metrics API.
    cd metrics-server/
    git checkout ed0663b3b4ddbfab5afea166dfd68c677930d22e
    kubectl create -f deploy/1.8+/
    • Wait a few seconds for the metrics server pods to start. You can see their status with kubectl get pods -n kube-system.
  2. Challenge

    Configure a Horizontal Pod Autoscaler to autoscale the train schedule app.

    Check the example-solution branch of the source code repo for an example of the code changes needed in the train-schedule-kube.yml file:

    To complete this task, you will need to do the following:

    • Create a fork of the source code at
    • Add a CPU resource request in train-schedule-kube.yml for the pods created by the train-schedule deployment. Check the example solution if you need to need to know where to add this.
        cpu: 200m
    • Define a HorizontalPodAutoscaler in train-schedule-kube.yml to autoscale in response to CPU load.
    apiVersion: autoscaling/v2beta1
    kind: HorizontalPodAutoscaler
      name: train-schedule
      namespace: default
        apiVersion: apps/v1
        kind: Deployment
        name: train-schedule-deployment
      minReplicas: 1
      maxReplicas: 4
      - type: Resource
          name: cpu
          targetAverageUtilization: 50
    • Generate some load on the app to see the autoscaler in action!

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