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Deploy a Helm chart to a Kubernetes cluster with Azure IoT Orchestrator Preview

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Important

Azure IoT Operations Preview – enabled by Azure Arc is currently in PREVIEW. You shouldn't use this preview software in production environments.

See the Supplemental Terms of Use for Microsoft Azure Previews for legal terms that apply to Azure features that are in beta, preview, or otherwise not yet released into general availability.

Once you have Azure IoT Operations Preview deployed to a connected cluster, you can use Azure IoT Orchestrator Preview to deploy custom workloads including Helm charts.

Prerequisites

Deploy a helm chart with override values

This section shows you how to deploy a helm chart using the orchestrator through Bicep. The helm values used during the helm install are stored in separate files that are imported at the time of the deployment. There are two helm value files that are unioned together to form the helm values used by the helm chart: a base helm values file and an overlay helm values file. The base helm values file defines values common to many deployment environments. The overlay helm values file defines a few specific values for a single deployment environment.

The following example is a Bicep template that deploys a helm chart:

Note

The Bicep union() method is used to combine two sets of configuration values.

@description('The location of the existing Arc-enabled cluster resource in Azure.')
@allowed(['eastus2', 'westus3'])
param clusterLocation string

@description('The namespace on the K8s cluster where the resources are installed.')
param clusterNamespace string

@description('The extended location resource name.')
param customLocationName string

// Load the base helm chart config and the overlay helm chart config.
// Apply the overlay config over the base config using union().
var baseAkriValues = loadYamlContent('base.yml')
var overlayAkriValues = loadYamlContent('overlay.yml')
var akriValues = union(baseAkriValues, overlayAkriValues)

resource helmChart 'Microsoft.iotoperationsorchestrator/targets@2023-10-04-preview' = {
  name: 'akri-helm-chart-override'
  location: clusterLocation
  extendedLocation: {
    type: 'CustomLocation'
    name: resourceId('Microsoft.ExtendedLocation/customLocations', customLocationName)
  }
  properties: {
    scope: clusterNamespace
    components: [
      {
        name: 'akri'
        type: 'helm.v3'
        properties: {
          chart: {
            repo: 'oci://azureiotoperations.azurecr.io/simple-chart',
            version: '0.1.0'
          }
          values: akriValues
        }
      }
    ]
    topologies: [
      {
        bindings: [
          {
            role: 'instance'
            provider: 'providers.target.k8s'
            config: {
              inCluster: 'true'
            }
          }
          {
            role: 'helm.v3'
            provider: 'providers.target.helm'
            config: {
              inCluster: 'true'
            }
          }
        ]
      }
    ]
  }
}

You can use the Azure CLI to deploy the Bicep file.

  1. Save the Bicep file as main.bicep to your local computer.

  2. Save the following helm values YAML as base.yml to your local computer.

    custom:
  configuration:
    enabled: true
    name: akri-opcua-asset
    discoveryHandlerName: opcua-asset
    discoveryDetails: |
      opcuaDiscoveryMethod:
        - asset:
            endpointUrl: "opc.tcp://<INSERT FQDN>:50000"
            useSecurity: false
            autoAcceptUntrustedCertificates: true   
  discovery:
    enabled: true
    name: akri-opcua-asset-discovery
    image:
      repository: e4ipreview.azurecr.io/e4i/workload/akri-opc-ua-asset-discovery
      tag: latest
      pullPolicy: Always
    useNetworkConnection: true
    port: 80
    resources:
      memoryRequest: 64Mi
      cpuRequest: 10m
      memoryLimit: 512Mi
      cpuLimit: 1000m
kubernetesDistro: k8s
prometheus:
  enabled: true
opentelemetry:
  enabled: true

  3. Save the following helm values YAML as overlay.yml to your local computer.

    custom:
  configuration:
    discoveryDetails: |
      opcuaDiscoveryMethod:
        - asset:
            endpointUrl: "opc.tcp://site.specific.endpoint:50000"
            useSecurity: false
            autoAcceptUntrustedCertificates: true

  4. Deploy the Bicep file using the Azure CLI.

    az deployment group create --resource-group exampleRG --template-file ./main.bicep --parameters clusterLocation=<existing-cluster-location> clusterNamespace=<namespace-on-cluster> customLocationName=<existing-custom-location-name>

    Replace <existing-cluster-location> with the existing Arc-enabled cluster resource's location. Replace <namespace-on-cluster> with the namespace where you want resources deployed on the Arc-enabled cluster. Replace <existing-custom-location-name> with the name of the existing custom location resource that is linked to your Arc-enabled cluster.

    When the deployment finishes, you should see a message indicating the deployment succeeded.

Review deployed resources

Use the Azure portal or Azure CLI to list the deployed resources in the resource group.

az resource list --resource-group exampleRG