Quickstart: Deploy an Azure Kubernetes Service (AKS) cluster using Bicep

Azure Kubernetes Service (AKS) is a managed Kubernetes service that lets you quickly deploy and manage clusters. In this quickstart, you:

  • Deploy an AKS cluster using Bicep.
  • Run a sample multi-container application with a group of microservices and web front ends simulating a retail scenario.

Note

To get started with quickly provisioning an AKS cluster, this article includes steps to deploy a cluster with default settings for evaluation purposes only. Before deploying a production-ready cluster, we recommend that you familiarize yourself with our baseline reference architecture to consider how it aligns with your business requirements.

Before you begin

  • This article requires Azure CLI version 2.0.64 or later. If you're using Azure Cloud Shell, the latest version is already installed there.
  • This article requires an existing Azure resource group. If you need to create one, you can use the az group create command.
  • To create an AKS cluster using a Bicep file, you provide an SSH public key. If you need this resource, see the following section. Otherwise, skip to Review the Bicep file.
  • Make sure the identity you use to create your cluster has the appropriate minimum permissions. For more details on access and identity for AKS, see Access and identity options for Azure Kubernetes Service (AKS).
  • To deploy a Bicep file, you need write access on the resources you create and access to all operations on the Microsoft.Resources/deployments resource type. For example, to create a virtual machine, you need Microsoft.Compute/virtualMachines/write and Microsoft.Resources/deployments/* permissions. For a list of roles and permissions, see Azure built-in roles.

Create an SSH key pair

  1. Go to https://shell.azure.com to open Cloud Shell in your browser.

  2. Create an SSH key pair using the az sshkey create Azure CLI command or the ssh-keygen command.

    # Create an SSH key pair using Azure CLI
    az sshkey create --name "mySSHKey" --resource-group "myResourceGroup"
    
    # Create an SSH key pair using ssh-keygen
    ssh-keygen -t rsa -b 4096
    

For more information about creating SSH keys, see Create and manage SSH keys for authentication in Azure.

Review the Bicep file

The Bicep file used in this quickstart is from Azure Quickstart Templates.

@description('The name of the Managed Cluster resource.')
param clusterName string = 'aks101cluster'

@description('The location of the Managed Cluster resource.')
param location string = resourceGroup().location

@description('Optional DNS prefix to use with hosted Kubernetes API server FQDN.')
param dnsPrefix string

@description('Disk size (in GB) to provision for each of the agent pool nodes. This value ranges from 0 to 1023. Specifying 0 will apply the default disk size for that agentVMSize.')
@minValue(0)
@maxValue(1023)
param osDiskSizeGB int = 0

@description('The number of nodes for the cluster.')
@minValue(1)
@maxValue(50)
param agentCount int = 3

@description('The size of the Virtual Machine.')
param agentVMSize string = 'standard_d2s_v3'

@description('User name for the Linux Virtual Machines.')
param linuxAdminUsername string

@description('Configure all linux machines with the SSH RSA public key string. Your key should include three parts, for example \'ssh-rsa AAAAB...snip...UcyupgH azureuser@linuxvm\'')
param sshRSAPublicKey string

resource aks 'Microsoft.ContainerService/managedClusters@2022-05-02-preview' = {
  name: clusterName
  location: location
  identity: {
    type: 'SystemAssigned'
  }
  properties: {
    dnsPrefix: dnsPrefix
    agentPoolProfiles: [
      {
        name: 'agentpool'
        osDiskSizeGB: osDiskSizeGB
        count: agentCount
        vmSize: agentVMSize
        osType: 'Linux'
        mode: 'System'
      }
    ]
    linuxProfile: {
      adminUsername: linuxAdminUsername
      ssh: {
        publicKeys: [
          {
            keyData: sshRSAPublicKey
          }
        ]
      }
    }
  }
}

output controlPlaneFQDN string = aks.properties.fqdn

The resource defined in the Bicep file:

For more AKS samples, see the AKS quickstart templates site.

Deploy the Bicep file

  1. Save the Bicep file as main.bicep to your local computer.

Important

The Bicep file sets the clusterName param to the string aks101cluster. If you want to use a different cluster name, make sure to update the string to your preferred cluster name before saving the file to your computer.

  1. Deploy the Bicep file using either Azure CLI or Azure PowerShell.

    az deployment group create --resource-group myResourceGroup --template-file main.bicep --parameters dnsPrefix=<dns-prefix> linuxAdminUsername=<linux-admin-username> sshRSAPublicKey='<ssh-key>'
    

    Provide the following values in the commands:

    • DNS prefix: Enter a unique DNS prefix for your cluster, such as myakscluster.
    • Linux Admin Username: Enter a username to connect using SSH, such as azureuser.
    • SSH RSA Public Key: Copy and paste the public part of your SSH key pair (by default, the contents of ~/.ssh/id_rsa.pub).

    It takes a few minutes to create the AKS cluster. Wait for the cluster to be successfully deployed before you move on to the next step.

Validate the Bicep deployment

Connect to the cluster

To manage a Kubernetes cluster, use the Kubernetes command-line client, kubectl. kubectl is already installed if you use Azure Cloud Shell.

  1. Install kubectl locally using the az aks install-cli command.

    az aks install-cli
    
  2. Configure kubectl to connect to your Kubernetes cluster using the az aks get-credentials command. This command downloads credentials and configures the Kubernetes CLI to use them.

    az aks get-credentials --resource-group myResourceGroup --name myAKSCluster
    
  3. Verify the connection to your cluster using the kubectl get command. This command returns a list of the cluster nodes.

    kubectl get nodes
    

    The following example output shows the single node created in the previous steps. Make sure the node status is Ready.

    NAME                       STATUS   ROLES   AGE     VERSION
    aks-agentpool-41324942-0   Ready    agent   6m44s   v1.12.6
    aks-agentpool-41324942-1   Ready    agent   6m46s   v1.12.6
    aks-agentpool-41324942-2   Ready    agent   6m45s   v1.12.6
    

Deploy the application

To deploy the application, you use a manifest file to create all the objects required to run the AKS Store application. A Kubernetes manifest file defines a cluster's desired state, such as which container images to run. The manifest includes the following Kubernetes deployments and services:

Screenshot of Azure Store sample architecture.

  • Store front: Web application for customers to view products and place orders.
  • Product service: Shows product information.
  • Order service: Places orders.
  • Rabbit MQ: Message queue for an order queue.

Note

We don't recommend running stateful containers, such as Rabbit MQ, without persistent storage for production. These are used here for simplicity, but we recommend using managed services, such as Azure CosmosDB or Azure Service Bus.

  1. Create a file named aks-store-quickstart.yaml and copy in the following manifest:

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: rabbitmq
    spec:
      replicas: 1
      selector:
        matchLabels:
          app: rabbitmq
      template:
        metadata:
          labels:
            app: rabbitmq
        spec:
          nodeSelector:
            "kubernetes.io/os": linux
          containers:
          - name: rabbitmq
            image: mcr.microsoft.com/mirror/docker/library/rabbitmq:3.10-management-alpine
            ports:
            - containerPort: 5672
              name: rabbitmq-amqp
            - containerPort: 15672
              name: rabbitmq-http
            env:
            - name: RABBITMQ_DEFAULT_USER
              value: "username"
            - name: RABBITMQ_DEFAULT_PASS
              value: "password"
            resources:
              requests:
                cpu: 10m
                memory: 128Mi
              limits:
                cpu: 250m
                memory: 256Mi
            volumeMounts:
            - name: rabbitmq-enabled-plugins
              mountPath: /etc/rabbitmq/enabled_plugins
              subPath: enabled_plugins
          volumes:
          - name: rabbitmq-enabled-plugins
            configMap:
              name: rabbitmq-enabled-plugins
              items:
              - key: rabbitmq_enabled_plugins
                path: enabled_plugins
    ---
    apiVersion: v1
    data:
      rabbitmq_enabled_plugins: |
        [rabbitmq_management,rabbitmq_prometheus,rabbitmq_amqp1_0].
    kind: ConfigMap
    metadata:
      name: rabbitmq-enabled-plugins
    ---
    apiVersion: v1
    kind: Service
    metadata:
      name: rabbitmq
    spec:
      selector:
        app: rabbitmq
      ports:
        - name: rabbitmq-amqp
          port: 5672
          targetPort: 5672
        - name: rabbitmq-http
          port: 15672
          targetPort: 15672
      type: ClusterIP
    ---
    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: order-service
    spec:
      replicas: 1
      selector:
        matchLabels:
          app: order-service
      template:
        metadata:
          labels:
            app: order-service
        spec:
          nodeSelector:
            "kubernetes.io/os": linux
          containers:
          - name: order-service
            image: ghcr.io/azure-samples/aks-store-demo/order-service:latest
            ports:
            - containerPort: 3000
            env:
            - name: ORDER_QUEUE_HOSTNAME
              value: "rabbitmq"
            - name: ORDER_QUEUE_PORT
              value: "5672"
            - name: ORDER_QUEUE_USERNAME
              value: "username"
            - name: ORDER_QUEUE_PASSWORD
              value: "password"
            - name: ORDER_QUEUE_NAME
              value: "orders"
            - name: FASTIFY_ADDRESS
              value: "0.0.0.0"
            resources:
              requests:
                cpu: 1m
                memory: 50Mi
              limits:
                cpu: 75m
                memory: 128Mi
          initContainers:
          - name: wait-for-rabbitmq
            image: busybox
            command: ['sh', '-c', 'until nc -zv rabbitmq 5672; do echo waiting for rabbitmq; sleep 2; done;']
            resources:
              requests:
                cpu: 1m
                memory: 50Mi
              limits:
                cpu: 75m
                memory: 128Mi
    ---
    apiVersion: v1
    kind: Service
    metadata:
      name: order-service
    spec:
      type: ClusterIP
      ports:
      - name: http
        port: 3000
        targetPort: 3000
      selector:
        app: order-service
    ---
    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: product-service
    spec:
      replicas: 1
      selector:
        matchLabels:
          app: product-service
      template:
        metadata:
          labels:
            app: product-service
        spec:
          nodeSelector:
            "kubernetes.io/os": linux
          containers:
          - name: product-service
            image: ghcr.io/azure-samples/aks-store-demo/product-service:latest
            ports:
            - containerPort: 3002
            resources:
              requests:
                cpu: 1m
                memory: 1Mi
              limits:
                cpu: 1m
                memory: 7Mi
    ---
    apiVersion: v1
    kind: Service
    metadata:
      name: product-service
    spec:
      type: ClusterIP
      ports:
      - name: http
        port: 3002
        targetPort: 3002
      selector:
        app: product-service
    ---
    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: store-front
    spec:
      replicas: 1
      selector:
        matchLabels:
          app: store-front
      template:
        metadata:
          labels:
            app: store-front
        spec:
          nodeSelector:
            "kubernetes.io/os": linux
          containers:
          - name: store-front
            image: ghcr.io/azure-samples/aks-store-demo/store-front:latest
            ports:
            - containerPort: 8080
              name: store-front
            env:
            - name: VUE_APP_ORDER_SERVICE_URL
              value: "http://order-service:3000/"
            - name: VUE_APP_PRODUCT_SERVICE_URL
              value: "http://product-service:3002/"
            resources:
              requests:
                cpu: 1m
                memory: 200Mi
              limits:
                cpu: 1000m
                memory: 512Mi
    ---
    apiVersion: v1
    kind: Service
    metadata:
      name: store-front
    spec:
      ports:
      - port: 80
        targetPort: 8080
      selector:
        app: store-front
      type: LoadBalancer
    

    For a breakdown of YAML manifest files, see Deployments and YAML manifests.

    If you create and save the YAML file locally, then you can upload the manifest file to your default directory in CloudShell by selecting the Upload/Download files button and selecting the file from your local file system.

  2. Deploy the application using the kubectl apply command and specify the name of your YAML manifest.

    kubectl apply -f aks-store-quickstart.yaml
    

    The following example output shows the deployments and services:

    deployment.apps/rabbitmq created
    service/rabbitmq created
    deployment.apps/order-service created
    service/order-service created
    deployment.apps/product-service created
    service/product-service created
    deployment.apps/store-front created
    service/store-front created
    

Test the application

When the application runs, a Kubernetes service exposes the application front end to the internet. This process can take a few minutes to complete.

  1. Check the status of the deployed pods using the kubectl get pods command. Make all pods are Running before proceeding.

    kubectl get pods
    
  2. Check for a public IP address for the store-front application. Monitor progress using the kubectl get service command with the --watch argument.

    kubectl get service store-front --watch
    

    The EXTERNAL-IP output for the store-front service initially shows as pending:

    NAME          TYPE           CLUSTER-IP    EXTERNAL-IP   PORT(S)        AGE
    store-front   LoadBalancer   10.0.100.10   <pending>     80:30025/TCP   4h4m
    
  3. Once the EXTERNAL-IP address changes from pending to an actual public IP address, use CTRL-C to stop the kubectl watch process.

    The following example output shows a valid public IP address assigned to the service:

    NAME          TYPE           CLUSTER-IP    EXTERNAL-IP    PORT(S)        AGE
    store-front   LoadBalancer   10.0.100.10   20.62.159.19   80:30025/TCP   4h5m
    
  4. Open a web browser to the external IP address of your service to see the Azure Store app in action.

    Screenshot of AKS Store sample application.

Delete the cluster

If you don't plan on going through the AKS tutorial, clean up unnecessary resources to avoid Azure charges.

  • Remove the resource group, container service, and all related resources using the az group delete command.

    az group delete --name myResourceGroup --yes --no-wait
    

Note

The AKS cluster was created with a system-assigned managed identity, which is the default identity option used in this quickstart. The platform manages this identity so you don't need to manually remove it.

Next steps

In this quickstart, you deployed a Kubernetes cluster and then deployed a simple multi-container application to it. This sample application is for demo purposes only and doesn't represent all the best practices for Kubernetes applications. For guidance on creating full solutions with AKS for production, see AKS solution guidance.

To learn more about AKS and walk through a complete code-to-deployment example, continue to the Kubernetes cluster tutorial.