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

  • Статия

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.

Бележка

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

Бележка

The Azure Linux node pool is now generally available (GA). To learn about the benefits and deployment steps, see the Introduction to the Azure Linux Container Host for 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 Containers > 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, such as 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 on preset configurations, see Cluster configuration presets in the Azure portal.

        Бележка

        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, such as myAKSCluster.

      • Region: Select a region, such as East US 2.

      • Availability zones: Select None.

      • 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 enter a Node pool name, such as 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, select D2s_v3, and then select Select.

    • Leave 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.

  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.

    Azure CLI 
    az aks get-credentials --resource-group myResourceGroup --name myAKSCluster

  2. Verify the connection to your cluster using kubectl get to return a list of the cluster nodes.

    Azure CLI 
    kubectl get nodes

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

    Output 
    NAME                       STATUS   ROLES   AGE     VERSION
aks-nodepool1-31718369-0   Ready    agent   6m44s   v1.15.10

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.

Бележка

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

  2. Paste the following manifest into the editor:

    YAML 
    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.

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

    Bash 
    kubectl apply -f aks-store-quickstart.yaml

    The following example output shows the deployments and services:

    Output 
    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.

    Bash 
    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.

    Azure CLI 
    kubectl get service store-front --watch

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

    Output 
    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:

    Output 
    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.

    Бележка

    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.

AKS tutorial series