AI Application Insights with Azure Monitor for Performance Tracking.

Question

Pulumi · Accepted Answer

To set up AI Application Insights with Azure Monitor for performance tracking, we will use the Azure Monitor service provided by Azure, which includes Application Insights. We will create an instance of Application Insights using Pulumi's `azure-native` provider.

Application Insights is an extensible Application Performance Management (APM) service for developers and DevOps professionals. It allows you to monitor your live applications, automatically detect performance anomalies, and includes powerful analytics tools to help you diagnose issues and to understand what users actually do with your app.

Below is a Pulumi program written in Python that provides a basic setup for Application Insights with Azure Monitor:

1. We define an Azure Monitor Workspace - a container where data from different sources can be aggregated and analyzed together.
2. We create an instance of Application Insights tied to the workspace.
3. We use an Alert Rule to listen for specific metrics or log patterns that might indicate performance issues.

First, we need to define the workspace where all our monitoring data will be sent:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create a Resource Group if you don't have one already
resource_group = azure_native.resources.ResourceGroup("my-resource-group")

# Create an Azure Monitor Log Analytics Workspace
monitor_workspace = azure_native.operationalinsights.Workspace("my-monitor-workspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    # Specify the SKU name; 'PerGB2018' is generally used for pricing based on data volume
    sku=azure_native.operationalinsights.SkuArgs(
        name="PerGB2018"
    )
)

# Documentation reference for Azure Monitor Workspace:
# https://www.pulumi.com/registry/packages/azure-native/api-docs/operationalinsights/workspace/
```

Next, we will attach Application Insights to this workspace:

```python
# Create an Application Insights instance
app_insights = azure_native.insights.Component("my-app-insights",
    resource_group_name=resource_group.name,
    kind="web",
    application_type="web",
    location=resource_group.location,
    workspace_resource_id=monitor_workspace.id,
)

# Documentation reference for Application Insights Component:
# https://www.pulumi.com/registry/packages/azure-native/api-docs/insights/component/
```

Finally, we set up an Alert Rule that can notify us about specific scenarios, such as performance issues:

```python
# Create an Alert Rule for monitoring performance metrics
alert_rule = azure_native.insights.AlertRule("my-alert-rule",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    enabled=True,
    condition=azure_native.insights.MetricAlertCriteriaArgs(
        metric_name="requests/failed",
        metric_namespace="microsoft.insights/components",
        operator="GreaterThan",
        threshold=5,
        time_aggregation="Count",
        dimensions=None,
    ),
    actions=[
        azure_native.insights.AlertRuleEmailArgs(
            send_to_service_owners=True,
            custom_emails=["admin@example.com"],  # Replace with your email
        ),
    ]
)

# Documentation reference for Azure Monitor Alert Rule:
# https://www.pulumi.com/registry/packages/azure-native/api-docs/insights/alertrule/
```

To complete this program, we'll export some of the key details we might need to access or reference our resources:

```python
# Export the Application Insights instrumentation key
pulumi.export('app_insights_instrumentation_key', app_insights.instrumentation_key)
# Export the ID of the monitor workspace
pulumi.export('monitor_workspace_id', monitor_workspace.id)
```

This program sets up the necessary Azure resources to start tracking the performance of your application with Azure Monitor and Application Insights. Once this infrastructure is deployed, you can integrate the Application Insights instrumentation key into your application, which allows telemetry data to be collected and analyzed within the Azure portal.

If you're new to Pulumi and Azure, before running this code, ensure you've [installed Pulumi](https://www.pulumi.com/docs/get-started/azure/) and have set up your Azure credentials. Save this code to a file named `__main__.py` in a directory, and run `pulumi up` within that directory to create the resources.