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

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

  • Deploy an AKS cluster with default settings using the Azure CLI.
  • Deploy a sample multi-container application with a group of microservices and web front ends that simulate a retail scenario.

Note

This article includes steps to deploy a cluster for evaluation purposes only. Before you deploy a production-ready cluster, we recommend that you familiarize yourself with our baseline reference architecture to consider how it aligns with your business requirements.

If you only want to deploy an Azure Kubernetes Service cluster, select Deploy to Azure to open your browser in the Azure portal and select Run all steps.

Deploy to Azure

Before you begin

This quickstart assumes a basic understanding of Kubernetes concepts. For more information, see Kubernetes core concepts for Azure Kubernetes Service (AKS).

  • If you don't have an Azure account, create a free account before you begin.

Register resource providers

You might need to register resource providers in your Azure subscription. For example, Microsoft.ContainerService is required.

Run the following command to check the registration status.

az provider show --namespace Microsoft.ContainerService --query registrationState

If necessary, register the resource provider.

az provider register --namespace Microsoft.ContainerService

Define environment variables

Define the following environment variables for use throughout this quickstart.

export RANDOM_STRING=$(printf '%05d%05d' "$RANDOM" "$RANDOM")
export RESOURCE_GROUP="myAKSResourceGroup$RANDOM_STRING"
export CLUSTER_NAME="myAKSCluster$RANDOM_STRING"
export USER_NP='userpool1'
export LOCATION="westus"

The RANDOM_STRING variable stores a random 10-digit string. The RESOURCE_GROUP and CLUSTER_NAME variable values are concatenated with the RANDOM_STRING value to create unique names. The USER_NP stores a name for a user mode node pool. The LOCATION variable has the value westus2. You can use these variable values or create your own. Use the echo command to view variable values like echo $RANDOM_STRING.

Create a resource group

An Azure resource group is a logical group in which Azure resources are deployed and managed. When you create a resource group, you're prompted to specify a location. This location is the storage location of your resource group metadata and where your resources run in Azure if you don't specify another region during resource creation.

Create a resource group using the az group create command.

az group create --name $RESOURCE_GROUP --location $LOCATION

The result looks like the following example.

{
  "id": "/subscriptions/aaaa0a0a-bb1b-cc2c-dd3d-eeeeee4e4e4e/resourceGroups/myAKSResourceGroup<randomStringValue>",
  "location": "westus",
  "managedBy": null,
  "name": "myAKSResourceGroup<randomStringValue>",
  "properties": {
    "provisioningState": "Succeeded"
  },
  "tags": null,
  "type": "Microsoft.Resources/resourceGroups"
}

Create an AKS cluster

Create an AKS cluster using the az aks create command. The following example creates a cluster with one node and enables a system-assigned managed identity.

az aks create \
  --resource-group $RESOURCE_GROUP \
  --name $CLUSTER_NAME \
  --node-count 1 \
  --generate-ssh-keys

When you create a new cluster, AKS automatically creates a second resource group to store the AKS resources. For more information, see Why are two resource groups created with AKS?

The cluster in this example specifies a node count of one to save time and resources. In a production environment, the recommendation is a node count of threee or more nodes. The az aks create defaults to three nodes if you don't specify a node count.

Add a user mode node pool

Applications should run on user mode node pools instead of the default system mode node pool. User node pools provide better isolation and flexibility for application workloads. Add a user node pool to your cluster using the az aks nodepool add command.

az aks nodepool add \
  --resource-group $RESOURCE_GROUP \
  --cluster-name $CLUSTER_NAME \
  --name $USER_NP \
  --node-count 1 \
  --mode User

Like the cluster, we specified one node, but the default is three nodes if you don't specify a node count.

After the user node pool is created, you can verify that your cluster has a system node pool and a user node pool using the az aks nodepool list command.

az aks nodepool list \
  --resource-group $RESOURCE_GROUP \
  --cluster-name $CLUSTER_NAME \
  --query "[].{Count:count, Mode:mode, NodePool:name, ResourceGroup:resourceGroup}"
[
  {
    "Count": 1,
    "Mode": "System",
    "NodePool": "nodepool1",
    "ResourceGroup": "myAKSResourceGroup1234554321"
  },
  {
    "Count": 1,
    "Mode": "User",
    "NodePool": "userpool1",
    "ResourceGroup": "myAKSResourceGroup1234554321"
  }
]

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. To install kubectl locally, use the az aks install-cli command.

  1. 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 $RESOURCE_GROUP --name $CLUSTER_NAME
    
  2. Verify the connection to your cluster using the kubectl get command. This command returns a list of the cluster nodes.

    kubectl get nodes
    
    NAME                                STATUS   ROLES    AGE     VERSION
    aks-nodepool1-123456789-vmss000000   Ready    <none>   15m     v1.34.4
    aks-userpool1-123456789-vmss000000   Ready    <none>   5m36s   v1.34.4
    

    There are two nodes, nodepool1 is ths system node pool created with the cluster and userpool1 is the user node pool added to the cluster.

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.
  • RabbitMQ: Message queue for an order queue.

Note

We don't recommend running stateful containers, such as RabbitMQ, without persistent storage for production. We use it here for simplicity, but we recommend using managed services, such as Azure Cosmos DB or Azure Service Bus.

  1. Create a file named aks-store-quickstart.yaml and copy in the following manifest. Replace the two placeholders for <defaultPassword> with your own password. This password is used for the default user of the RabbitMQ instance.

    apiVersion: apps/v1
    kind: StatefulSet
    metadata:
      name: rabbitmq
    spec:
      serviceName: rabbitmq
      replicas: 1
      selector:
        matchLabels:
          app: rabbitmq
      template:
        metadata:
          labels:
            app: rabbitmq
        spec:
          nodeSelector:
            kubernetes.io/os: linux
            kubernetes.azure.com/mode: user
          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: "<defaultPassword>"
            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
            kubernetes.azure.com/mode: user
          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: "<defaultPassword>"
            - 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
            startupProbe:
              httpGet:
                path: /health
                port: 3000
              failureThreshold: 5
              initialDelaySeconds: 20
              periodSeconds: 10
            readinessProbe:
              httpGet:
                path: /health
                port: 3000
              failureThreshold: 3
              initialDelaySeconds: 3
              periodSeconds: 5
            livenessProbe:
              httpGet:
                path: /health
                port: 3000
              failureThreshold: 5
              initialDelaySeconds: 3
              periodSeconds: 3
          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
            kubernetes.azure.com/mode: user
          containers:
          - name: product-service
            image: ghcr.io/azure-samples/aks-store-demo/product-service:latest
            ports:
            - containerPort: 3002
            env:
            - name: AI_SERVICE_URL
              value: "http://ai-service:5001/"
            resources:
              requests:
                cpu: 1m
                memory: 1Mi
              limits:
                cpu: 2m
                memory: 20Mi
            readinessProbe:
              httpGet:
                path: /health
                port: 3002
              failureThreshold: 3
              initialDelaySeconds: 3
              periodSeconds: 5
            livenessProbe:
              httpGet:
                path: /health
                port: 3002
              failureThreshold: 5
              initialDelaySeconds: 3
              periodSeconds: 3
    ---
    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
            kubernetes.azure.com/mode: user
          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
            startupProbe:
              httpGet:
                path: /health
                port: 8080
              failureThreshold: 3
              initialDelaySeconds: 5
              periodSeconds: 5
            readinessProbe:
              httpGet:
                path: /health
                port: 8080
              failureThreshold: 3
              initialDelaySeconds: 3
              periodSeconds: 3
            livenessProbe:
              httpGet:
                path: /health
                port: 8080
              failureThreshold: 5
              initialDelaySeconds: 3
              periodSeconds: 3
    ---
    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 Cloud Shell 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
    
  3. Run the following command to verify the application is deployed on a user node pool.

    kubectl get pods -o wide
    

    The output will show that rabbitmq, order-service, product-service, and store-front pods are running on a node in the user node pool.

Test the application

You can validate that the application is running by visiting the public IP address or the application URL.

Run the following commands to display the application URL:

runtime="5 minutes"
endtime=$(date -ud "$runtime" +%s)
while [[ $(date -u +%s) -le $endtime ]]
do
   STATUS=$(kubectl get pods -l app=store-front -o 'jsonpath={..status.conditions[?(@.type=="Ready")].status}')
   echo $STATUS
   if [ "$STATUS" == 'True' ]
   then
      export IP_ADDRESS=$(kubectl get service store-front --output 'jsonpath={..status.loadBalancer.ingress[0].ip}')
      echo "service IP address: $IP_ADDRESS"
      break
   else
      sleep 10
   fi
done

The output shows the application's public IP address in the <applicationIPAddress> placeholder. You use that IP address to view the application in the next steps.

service IP Address: <applicationIPAddress>

Run the following command to send a request to the application URL. Replace <applicationIPAddress> with the service IP Address.

curl <applicationIPAddress>

The command returns HTML output that shows the application responds to the request.

<!doctype html>
<html lang="">
  <head>
    <meta charset="UTF-8" />
    <link rel="icon" href="/favicon.ico" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>Contoso Pet Store</title>
    <script type="module" crossorigin src="/assets/index-CLiaTzSi.js"></script>
    <link rel="stylesheet" crossorigin href="/assets/index-Cv6jORyk.css">
  </head>
  <body>
    <div id="app"></div>
  </body>
</html>

To view the application website, open a browser and enter the service IP address. The page looks like the following example.

Screenshot of AKS Store sample application.

Delete the cluster

If you don't plan on doing the AKS tutorial, clean up unnecessary resources to avoid Azure billing charges. You can remove the resource group, container service, and all related resources using the az group delete command.

az group delete --name $RESOURCE_GROUP --no-wait --yes

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. This sample application is for demo purposes only and doesn't represent all the best practices for Kubernetes applications. For guidance about how to create full solutions with AKS for production, see AKS solution guidance.

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