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 https://github.com/kubernetes-incubator/metrics-server.git
   

   • 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: https://github.com/linuxacademy/cicd-pipeline-train-schedule-autoscaling/blob/example-solution/train-schedule-kube.yml.

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

   • Create a fork of the source code at https://github.com/linuxacademy/cicd-pipeline-train-schedule-autoscaling.
   • 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.

   resources:
     requests:
       cpu: 200m
   

   • Define a HorizontalPodAutoscaler in train-schedule-kube.yml to autoscale in response to CPU load.

   ---
   
   apiVersion: autoscaling/v2beta1
   kind: HorizontalPodAutoscaler
   metadata:
     name: train-schedule
     namespace: default
   spec:
     scaleTargetRef:
       apiVersion: apps/v1
       kind: Deployment
       name: train-schedule-deployment
     minReplicas: 1
     maxReplicas: 4
     metrics:
     - type: Resource
       resource:
         name: cpu
         targetAverageUtilization: 50
   

   • Generate some load on the app to see the autoscaler in action!