Deploy the Microservice

Please note: this EKS and Karpenter workshop version is now deprecated since the launch of Karpenter v1beta, and has been updated to a new home on AWS Workshop Studio here: Karpenter: Amazon EKS Best Practice and Cloud Cost Optimization.

This workshop remains here for reference to those who have used this workshop before, or those who want to reference this workshop for running Karpenter on version v1alpha5.

Monte Carlo Pi Microservice

We are now ready to deploy our application. This time around we will deploy both a Service and a Deployment to serve our Monte Carlo microservice. We will apply all the techniques that we’ve seen in the previous Karpenter section.

Let’s create a template configuration file for monte carlo pi application:

cd ~/environment
cat <<EoF > monte-carlo-pi-service.yaml
---
apiVersion: v1 
kind: Service 
metadata: 
  name: monte-carlo-pi-service
  annotations:
    service.beta.kubernetes.io/aws-load-balancer-type: external
    service.beta.kubernetes.io/aws-load-balancer-nlb-target-type: ip
    service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing
    service.beta.kubernetes.io/aws-load-balancer-manage-backend-security-group-rules: "true"
spec: 
  type: LoadBalancer 
  ports: 
    - port: 80 
      targetPort: 8080 
  selector: 
    app: monte-carlo-pi-service 
--- 
apiVersion: apps/v1 
kind: Deployment 
metadata: 
  name: monte-carlo-pi-service 
  labels: 
    app: monte-carlo-pi-service 
spec: 
  replicas: 2 
  selector: 
    matchLabels: 
      app: monte-carlo-pi-service 
  template: 
    metadata: 
      labels: 
        app: monte-carlo-pi-service 
    spec:
      nodeSelector:
        intent: apps
        kubernetes.io/arch: amd64
        karpenter.sh/capacity-type: spot
      containers: 
        - name: monte-carlo-pi-service 
          image: ${MONTE_CARLO_IMAGE}
          resources: 
            requests: 
              memory: "512Mi" 
              cpu: "1024m" 
            limits: 
              memory: "512Mi" 
              cpu: "1024m" 
          securityContext: 
            privileged: false 
            readOnlyRootFilesystem: true 
            allowPrivilegeEscalation: false 
          ports: 
            - containerPort: 8080 
EoF
kubectl apply -f monte-carlo-pi-service.yaml

This should create a monte-carlo-pi-service.yml template file that defines a Service and a Deployment. The configuration instructs the cluster to deploy two replicas of a pod with a single container, that sets up Resource request and limits to a fixed value ~0.5vCPUs and 1024Mi of RAM.

Let’s understand what happens when applying this configuration.

Challenge

You can use Kube-ops-view or just plain kubectl cli to visualize the changes and answer the questions below. In the answers we will provide the CLI commands that will help you check the resposnes. Remember: to get the url of kube-ops-view you can run the following command kubectl get svc kube-ops-view | tail -n 1 | awk '{ print "Kube-ops-view URL = http://"$4 }'

Answer the following questions. You can expand each question to get a detailed answer and validate your understanding.

1) How many replicas would be created and which type of nodes would they be assigned to ?

Click here to show the answer

2) How can you get access to the Service URL ?

Click here to show the answer

Once you completed the questions above you shoudl be ready to move to the next section where we will configure HPA and prepare ourselves for a dynamic scaling exercise.

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