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Lab 02: Application Gateway Ingress

This lab deploys Application Gateway Ingress Controller (AGIC) for north-south traffic so you can validate listener, backend, TLS, and DNS behavior in a controlled way.

Prerequisites

  • Azure subscription with permission to create AKS, networking, and monitoring resources
  • Azure CLI, kubectl, and a shell environment capable of exporting variables
  • Existing or planned variable set for $RG, $CLUSTER_NAME, $LOCATION, and any lab-specific names
  • A Log Analytics workspace resource ID stored in $WORKSPACE_ID for Container Insights validation
  • Awareness that all commands use long flags only so they are easy to read and automate later

Architecture Diagram

flowchart TD
    CLIENT[Client or DNS] --> APPGW[Application Gateway]
    subgraph AKS Cluster
        AGIC[AGIC add-on]
        INGRESS[Ingress resource]
        SVC[Service]
        PODS[Application pods]
    end
    APPGW --> AGIC
    AGIC --> INGRESS
    INGRESS --> SVC
    SVC --> PODS

Step-by-Step Instructions

Step 1: Create the Application Gateway subnet

az network vnet subnet create \
    --resource-group "$RG" \
    --vnet-name "$VNET_NAME" \
    --name "$APPGW_SUBNET_NAME" \
    --address-prefixes 10.40.8.0/24

This step is important because it establishes the control point for create the application gateway subnet. After running it, pause and verify the Azure resource state before moving on so you do not compound errors later in the lab.

Step 2: Deploy Application Gateway

az network application-gateway create \
    --resource-group "$RG" \
    --name "$APPGW_NAME" \
    --location "$LOCATION" \
    --vnet-name "$VNET_NAME" \
    --subnet "$APPGW_SUBNET_NAME" \
    --capacity 2 \
    --sku WAF_v2

This step is important because it establishes the control point for deploy application gateway. After running it, pause and verify the Azure resource state before moving on so you do not compound errors later in the lab.

Step 3: Enable AGIC on AKS

az aks enable-addons \
    --resource-group "$RG" \
    --name "$CLUSTER_NAME" \
    --addons ingress-appgw \
    --appgw-id "$APPGW_ID"

This step is important because it establishes the control point for enable agic on aks. After running it, pause and verify the Azure resource state before moving on so you do not compound errors later in the lab.

Step 4: Deploy a sample application and ingress

kubectl apply \
    --filename appgw-namespace.yaml

kubectl apply \
    --filename appgw-deployment.yaml

kubectl apply \
    --filename appgw-service.yaml

kubectl apply \
    --filename appgw-ingress.yaml

This step is important because it establishes the control point for deploy a sample application and ingress. After running it, pause and verify the Azure resource state before moving on so you do not compound errors later in the lab.

Step 5: Inspect ingress and gateway health

kubectl get ingress \
    --all-namespaces \
    --output wide

az network application-gateway show-backend-health \
    --resource-group "$RG" \
    --name "$APPGW_NAME"

This step is important because it establishes the control point for inspect ingress and gateway health. After running it, pause and verify the Azure resource state before moving on so you do not compound errors later in the lab.

Validation Steps

Use the following validation flow after the deployment steps complete:

  • Confirm the AKS cluster and all required node pools are visible with kubectl get nodes --output wide.
  • Confirm the Azure resource provisioning state is Succeeded for any new network, gateway, identity, or policy resource.
  • Run at least one Container Insights query to prove telemetry is flowing before you declare the lab complete.
  • Capture screenshots or exported JSON only after sanitizing identifiers such as subscription IDs or object IDs.

Example validation commands:

kubectl get pods \
    --all-namespaces \
    --output wide

az aks show \
    --resource-group "$RG" \
    --name "$CLUSTER_NAME" \
    --query "{name:name,provisioningState:provisioningState,kubernetesVersion:kubernetesVersion}" \
    --output json

az monitor log-analytics query \
    --workspace "$WORKSPACE_ID" \
    --analytics-query "KubeNodeInventory | where TimeGenerated > ago(15m) | summarize Nodes=dcount(Computer) by ClusterName" \
    --timespan "PT15M"

Cleanup Instructions

Delete lab resources when you are finished to avoid unnecessary spend. If the lab created shared resources that other exercises still need, remove only the lab-specific objects first.

az group delete \
    --name "$RG" \
    --yes \
    --no-wait

If you created secondary resource groups, Application Gateway, or user-assigned identities, delete those resources as part of the same cleanup workflow or document why they remain.

See Also

Sources