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Enable telemetry for feature flags in a Python application

In this tutorial, you use telemetry in your Python application to track feature flag evaluations and custom events. Telemetry allows you to make informed decisions about your feature management strategy. You utilize the feature flag with telemetry enabled created in the overview for enabling telemetry for feature flags. Before proceeding, ensure that you create a feature flag named Greeting in your Configuration store with telemetry enabled. This tutorial builds on top of the tutorial for using variant feature flags in a Python application.

Prerequisites

Add telemetry to your Python application

  1. Install the required packages using pip:

    pip install azure-appconfiguration-provider
pip install featuremanagement["AzureMonitor"]
pip install azure-monitor-opentelemetry

  2. Open app.py and configure your code to connect to Application Insights to publish telemetry.

    import os
from azure.monitor.opentelemetry import configure_azure_monitor

# Configure Azure Monitor
configure_azure_monitor(connection_string=os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING"))

  3. Also in app.py load your feature flags from App Configuration and load them into feature management. FeatureManager uses the publish_telemetry callback function to publish telemetry to Azure Monitor.

    from featuremanagement.azuremonitor import publish_telemetry

feature_manager = FeatureManager(config, on_feature_evaluated=publish_telemetry)

  4. Open routes.py and update your code to track your own events in your application. When track_event is called, a custom event is published to Azure Monitor with the provided user.

    from featuremanagement import track_event

@bp.route("/heart", methods=["POST"])
def heart():
    if current_user.is_authenticated:
        user = current_user.username

        # Track the appropriate event based on the action
        track_event("Liked", user)
    return jsonify({"status": "success"})

  5. Open index.html and update the code to implement the like button. The like button sends a POST request to the /heart endpoint when clicked.

    <script>
    function heartClicked(button) {
        var icon = button.querySelector('i');

        // Toggle the heart icon appearance
        icon.classList.toggle('far');
        icon.classList.toggle('fas');

        // Only send a request to the dedicated heart endpoint when it's a like action
        if (icon.classList.contains('fas')) {
            fetch('/heart', {
                method: 'POST',
                headers: {
                    'Content-Type': 'application/json',
                }
            });
        }
    }
</script>

Build and run the app

  1. Application insights requires a connection string to connect to your Application Insights resource. Set the APPLICATIONINSIGHTS_CONNECTION_STRING environment variable to the connection string for your Application Insights resource.

    setx APPLICATIONINSIGHTS_CONNECTION_STRING "applicationinsights-connection-string"

    If you use PowerShell, run the following command:

    $Env:APPLICATIONINSIGHTS_CONNECTION_STRING = "applicationinsights-connection-string"

    If you use macOS or Linux, run the following command:

    export APPLICATIONINSIGHTS_CONNECTION_STRING='applicationinsights-connection-string'

Collect telemetry

Deploy your application to begin collecting telemetry from your users. To test its functionality, you can simulate user activity by creating many test users. Each user will experience a different variant of greeting messages, and they can interact with the application by clicking the heart button to like a quote. As your user base grows, you can monitor the increasing volume of telemetry data collected in Azure App Configuration. Additionally, you can drill down into the data to analyze how each variant of the feature flag influences user behavior.

Additional resources