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

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 Azure PowerShell.
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 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.
For ease of use, try the PowerShell environment in Azure Cloud Shell. For more information, see Quickstart for Azure Cloud Shell.

If you want to use PowerShell locally, then install the Az PowerShell module and connect to your Azure account using the Connect-AzAccount cmdlet. Make sure that you run the commands with administrative privileges. For more information, see Install Azure PowerShell.
Make sure that the identity you're using 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).
If you have more than one Azure subscription, set the subscription that you wish to use for the quickstart by calling the Set-AzContext cmdlet. For more information, see Manage Azure subscriptions with Azure PowerShell.

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.

The following example creates a resource group named myResourceGroup in the eastus location.

Create a resource group using the New-AzResourceGroup cmdlet.

New-AzResourceGroup -Name myResourceGroup -Location eastus

The following example output resembles successful creation of the resource group:

ResourceGroupName : myResourceGroup
Location : eastus
ProvisioningState : Succeeded
Tags :
ResourceId : /subscriptions/a0a0a0a0-bbbb-cccc-dddd-e1e1e1e1e1e1/resourceGroups/myResourceGroup

Create AKS cluster

To create an AKS cluster, use the New-AzAksCluster cmdlet. The following example creates a cluster named myAKSCluster with one node and enables a system-assigned managed identity.

New-AzAksCluster -ResourceGroupName myResourceGroup `
 -Name myAKSCluster `
 -NodeCount 1 `
 -EnableManagedIdentity `
 -GenerateSshKey

After a few minutes, the command completes and returns information about the cluster.

Note

When you create an AKS cluster, a second resource group called the node resource group is automatically created to store the AKS resources. For more information, see Node resource group. When you delete the resource group for the AKS cluster, the node resource group is also deleted. You also see a NetworkWatcherRG resource group created by default. This resource group is used by Azure Network Watcher to store monitoring data. You can safely ignore this resource group. For more information, see Enable or disable Azure Network Watcher.

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, call the Install-AzAksCliTool cmdlet.

Configure kubectl to connect to your Kubernetes cluster using the Import-AzAksCredential cmdlet. This command downloads credentials and configures the Kubernetes CLI to use them.
```
Import-AzAksCredential -ResourceGroupName myResourceGroup -Name myAKSCluster
```
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-nodepool1-11853318-vmss000000 Ready agent 2m26s v1.27.7
```

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.

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.

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.

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

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 by calling the Remove-AzResourceGroup cmdlet.

Remove-AzResourceGroup -Name myResourceGroup

Note

The AKS cluster was created with system-assigned managed identity (default identity option used in this quickstart), the 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.

AKS tutorial

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Additional resources

Last updated on

URL: https://learn.microsoft.com/en-us/azure/aks/learn/quick-kubernetes-deploy-powershell