Interactive AI-Powered Dashboards on Azure Web PubSub

Question

Pulumi · Accepted Answer

Interactive AI-powered dashboards are powerful tools for visualizing and interacting with data in real-time. Azure Web PubSub provides real-time web socket support which can be used to create such interactive dashboards where clients can send and receive messages with low-latency. In this context, the dashboard would use Azure's Web PubSub service for real-time messaging.

You will need to set up an Azure Web PubSub service, which will act as a backend for handling the bi-directional communication between your dashboard clients and the Azure service. You can then create a front-end to handle message visualization and user interaction, which could be developed using any web technology like HTML5, JavaScript, and supporting libraries/frameworks.

Here’s a Pulumi Python program to create Azure Web PubSub resources to back your dashboards:

```python
import pulumi
import pulumi_azure_native as azure_native

# Web PubSub resource names and location
resource_group_name = 'myResourceGroup'
web_pubsub_name = 'myWebPubSub'
location = 'West US'

# Create an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup('resourceGroup',
    resource_group_name=resource_group_name,
    location=location,
)

# Create an Azure Web PubSub service instance
web_pubsub_service = azure_native.webpubsub.WebPubSub('webPubSubService',
    resource_group_name=resource_group.name,
    resource_name=web_pubsub_name,
    location=resource_group.location,
    sku=azure_native.webpubsub.SkuArgs(
        name='Standard_S1',  # Choose the SKU that best fits your needs
        capacity=1,  # You can scale this according to your expected load
    ),
    # Define the kinds of events your application will listen to
    properties=azure_native.webpubsub.WebPubSubPropertiesArgs(
        event_handlers=[
            azure_native.webpubsub.EventHandlerArgs(
                url_template="{Your EventHandler URL Here}",
                event_name="message",  # Could be 'connect', 'connected', 'disconnected' etc.
                # Specify filters if you want to handle only specific messages/events
                system_events=["message"],
            ),
        ],
    ),
)

# Export the primary connection string of the Web PubSub service to use in your application
primary_connection_string = pulumi.Output.all(resource_group.name, web_pubsub_service.name).apply(
    lambda args: azure_native.webpubsub.list_web_pub_sub_keys(ListWebPubSubKeysArgs(
        resource_group_name=args[0],
        resource_name=args[1],
    )).apply(lambda keys: keys.primary_connection_string)
)

pulumi.export('primaryConnectionString', primary_connection_string)
```

This program accomplishes the following:

1. We start by importing `pulumi` and the `pulumi_azure_native` module that contains our Azure resources.
   
2. We define a resource group which is a logical container into which Azure resources like our Web PubSub will be deployed.

3. We create an instance of Azure's Web PubSub service using the `WebPubSub` class. We provide it with the necessary arguments such as the name, resource group, location, and SKU details. We configure a standard SKU which should suffice for a dashboard use-case unless it has to scale to very high loads.

4. We define an `event_handler` within our Web PubSub properties. This `event_handler` would contain the logic to handle incoming WebSocket messages and send responses back to the clients.

5. Finally, we export the primary_connection_string which will be used to connect your dashboard's frontend with the Azure Web PubSub service you just provisioned.

You'll need to replace `{Your EventHandler URL Here}` with the URL of the service that will handle WebSocket connection events.

Azure Web PubSub serves as the messaging backbone and you need to handle the dashboards' interactive elements through the WebSocket connections facilitated by it. You can use this connection string in your frontend dashboard application to allow it to communicate with this backend service and thus dynamically update and display the dashboard data in real-time.

This program should be saved into a file, for example `main.py`, and run using the Pulumi CLI. Please ensure you have the Azure CLI set up and logged in, and Pulumi installed and configured before running this program. After running `pulumi up`, this code will provision the necessary infrastructure on Azure.