1. Continuous Bot Analytics and Insights with Azure Application Insights

    Python

    To create continuous bot analytics and insights using Azure Application Insights with Pulumi, you will need to do the following steps:

    1. Set Up Azure Application Insights: Begin by creating an instance of Azure Application Insights within your subscription. This service will collect telemetry data, such as requests, exceptions, and events, from your bot application.

    2. Integrate with Your Application: Modify your bot application to send telemetry to Azure Application Insights. For various languages and frameworks, Azure provides SDKs that you can use to facilitate this integration.

    3. Define Analytics Queries: Use Pulumi to define analytics queries and insights that you want to continuously run against your data. These can be specified as Application Insights Analytics Items.

    4. Setup Dashboard: Optionally, for better visibility, configure an Azure dashboard with Pulumi to display the insights generated from your analytics queries.

    In the below Pulumi Python program, we will establish an instance of Azure Application Insights and then define an analytics item that might be used to track custom events within your bot.

    import pulumi import pulumi_azure_native as azure_native # Establish a new Azure resource group for your application's resources resource_group = azure_native.resources.ResourceGroup('my-resource-group') # Create an instance of Azure Application Insights for your bot application_insights = azure_native.insights.Component("myAppInsights", resource_name="my-bot-app-insights", resource_group_name=resource_group.name, kind="web", application_type="web", location=resource_group.location ) # Define an analytics query for your Application Insights instance. # This query is a simple example and may need to be adjusted for your specific needs. analytics_item = azure_native.insights.AnalyticsItem("myAnalyticsQuery", resource_name=application_insights.name, resource_group_name=resource_group.name, content="requests | where timestamp > ago(30d) | summarize count() by name, success", name="TotalRequestsOverLast30Days", scope_path="", override_item=False, properties=azure_native.insights.AnalyticsItemPropertiesArgs( function_alias="totalRequests", content="requests | where timestamp > ago(30d) | summarize count() by name, success" ) ) # Export the Application Insights instrumentation key to use in your application code instrumentation_key = pulumi.Output.secret(application_insights.instrumentation_key) pulumi.export('instrumentation_key', instrumentation_key)

    This Pulumi program does the following:

    • Creates a new Azure resource group named my-resource-group.
    • Provisions a new Azure Application Insights instance within the resource group.
    • Configures an analytics item called myAnalyticsQuery, which contains a Kusto Query Language (KQL) query used to retrieve the total number of requests in the last 30 days grouped by request name and success status.

    Keep in mind, the KQL content in analytics_item is just an example. You'll need to adjust this query to reflect the actual insights you want to gain from your bot's telemetry.

    Moreover, the instrumentation_key of the Application Insights instance is exported as a secret. In your application, you would use this key to configure the telemetry client to send data to your Azure Application Insights instance.

    Before running this program, ensure you have the Azure CLI installed and configured. Once your CLI is configured with the appropriate credentials and permissions, you can run the pulumi up command in the directory of your Pulumi program.

    For further reading and more advanced scenarios, please refer to the Azure Application Insights documentation on Pulumi.


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