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

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 the Azure portal.
• Run a sample multi-container application with a group of microservices and web front ends simulating a retail scenario.

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.
• If you're unfamiliar with the Azure Cloud Shell, review Overview of Azure Cloud Shell.
• Make sure that the identity you use to create your cluster has the appropriate minimum permissions. For more information about access and identity for AKS, see Access and identity options for Azure Kubernetes Service (AKS).

Create an AKS cluster

1. Sign in to the Azure portal.

2. On the Azure portal home page, select Create a resource.

3. In the Categories section, select Infrastructure Services > Azure Kubernetes Service (AKS).

4. On the Basics tab, configure the following settings:

   • Under Project details:

     • Subscription: Select the Azure subscription you want to use for this AKS cluster.
     • Resource group: Select Create new, enter a resource group name, like myResourceGroup, and then select Ok. While you can select an existing resource group, for testing or evaluation purposes, we recommend creating a resource group to temporarily host these resources and avoid impacting your production or development workloads.

   • Under Cluster details:

     • Cluster preset configuration: Select Dev/Test. For more details about preset configurations, see Cluster configuration presets in the Azure portal. You can change the preset configuration when creating your cluster by selecting Compare presets and choosing a different option.

     👁 Screenshot of Create AKS cluster - portal preset options.
     

     • Kubernetes cluster name: Enter a cluster name, like myAKSCluster.

     • Region: Select a region, like East US 2.

     • Fleet manager: None

     • Availability zones: None. Deselect any selected zones.

     • AKS pricing tier: Select Free.

     • Leave the default values for the remaining settings, and select Next.

       👁 Screenshot showing how to configure an AKS cluster in Azure portal.
       

5. On the Node pools tab, configure the following settings:

   • Select Add node pool and select Add a Virtual Machine Scale Set node pool

   • Name: Enter a name like nplinux.

   • Mode: Select User.

   • OS SKU: Select Ubuntu Linux.

   • Availability zones: Select None.

   • Leave the Enable Azure Spot instances checkbox unchecked.

   • Node size: Select Choose a size. On the Select a VM size page, search for D2s_v5, select that VM size, and Select.

   • Use the default values for the remaining settings, and select Add.

     👁 Screenshot showing how to create a node pool running Ubuntu Linux.
     

6. Select Review + create to run validation on the cluster configuration. After validation completes, select Create.

   It takes a few minutes to create the AKS cluster. When your deployment is complete, navigate to your resource by selecting Go to resource, or by browsing to the AKS cluster resource group and selecting the AKS resource.

Connect to the cluster

You use the Kubernetes command-line client, kubectl, to manage Kubernetes clusters. kubectl is already installed if you use Azure Cloud Shell. If you're unfamiliar with the Cloud Shell, review Overview of Azure Cloud Shell.

If you're using Cloud Shell, open it with the >_ button on the top of the Azure portal. If you're using PowerShell locally, connect to Azure via the Connect-AzAccount command. If you're using Azure CLI locally, connect to Azure via the az login command.

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, like 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.

1. In the Cloud Shell, open an editor and create a file named aks-store-quickstart.yaml.

2. Paste the following manifest into the editor:

   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
    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
    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
    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
    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 CloudShell by selecting the Upload/Download files button and selecting the file from your local file system.

3. 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 sure 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
   

   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
   

3. 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 series, clean up unnecessary resources to avoid Azure charges.

1. In the Azure portal, navigate to your AKS cluster resource group.

2. Select Delete resource group.

3. Enter the name of the resource group to delete, and then select Delete > Delete.

   Note

   The AKS cluster was created with a system-assigned managed identity. This identity is managed by the platform and doesn't require removal.

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 series.