1. Datadog Synthetics for AI Application Availability Monitoring


    To create a Datadog Synthetic Test for AI Application Availability Monitoring using Pulumi, you will use the datadog.SyntheticsTest resource. This allows you to define various checks and assert certain conditions that must be met to consider your service available.

    Below is a high-level explanation of what each primary section of the Pulumi code does, followed by a detailed Pulumi Python program that implements a basic but functional synthetic test for availability monitoring:

    1. Import Pulumi and Datadog SDKs: The pulumi and pulumi_datadog SDKs need to be imported to allow us to define and provision infrastructure.

    2. Datadog Synthetics Test Resource: This is the heart of your application availability monitoring. Inside, you will define the type of test (e.g., api or browser), the configuration for the test, and what conditions must be met for the service to be considered available. You will specify the URL or endpoint to check, the interval for checks, and the assertions that define the expected response to consider the service operational.

    3. Pulumi Export: Finally, the test's details are exported, such as the public ID of the synthetic test, so you can use it to integrate with other systems or services.

    Here is the Pulumi program, which implements a basic synthetic test:

    import pulumi import pulumi_datadog as datadog # Create an HTTP check that ensures your API returns a success status within a reasonable time. # Adjust the `request` and `assertions` to match your service's specifics. synthetic_test = datadog.SyntheticsTest("ai-app-availability-monitor", type="api", # Use "api" for an HTTP test. For browser-based tests, you'd use "browser". request=datadog.SyntheticsTestRequestArgs( # Define the request to be performed. method="GET", # Replace "GET" with "POST", "PUT", etc., as per your API's needs. url="https://api.example.com/health", # This should be the endpoint you want to check. timeout=30, # Timeout in seconds for the request. ), assertion=datadog.SyntheticsTestAssertionArgs( # Define what conditions must be met. type="statusCode", # Checking the status code is a common assertion. operator="is", # This could also be "isNot", "lessThan", etc. target=200, # The status code expected for a successful response. ), locations=["aws:us-east-1"], # Define the locations to run the test from. message="AI Application is not responding as expected", # The message for alerting. tags=["AI", "app", "monitoring", "availability"], # Useful tags for filtering and organizing. status="live", # Set the status to "live" to enable the test, use "paused" to disable. ) # Export the ID of the synthetic test, useful for tracking and integration purposes. pulumi.export("synthetic_test_id", synthetic_test.id)

    This code provides a synthetic test that monitors an endpoint (in this case, a hypothetical "https://api.example.com/health"). It checks that a GET request to this endpoint returns a 200 status code within 30 seconds from the AWS US East 1 region.

    After running this Pulumi program, a test will be created and executed periodically in Datadog according to the defined configuration. If the API response diverges from the 200 status code assertion, Datadog's alerting system will notify you that your AI application may be experiencing availability issues.

    Remember to have the Datadog provider configuration set up in your Pulumi stack for the program to run successfully. This typically includes specifying the necessary API and application keys for Datadog:

    # Datadog provider configuration (not included in the main program block above). datadog_provider = datadog.Provider("datadog_provider", api_key="your-datadog-api-key", app_key="your-datadog-app-key" ) # You should pass `opts=pulumi.ResourceOptions(provider=datadog_provider)` # in your resource definitions if you are not using the default provider.

    This authentication configuration is not included in the synthetic test code block to emphasize the test definition itself, but it's crucial for authenticating with Datadog's API. The api_key and app_key should be replaced with your actual Datadog API and application keys.

    By implementing this Pulumi code, you ensure that there is always an automated, cloud-based watcher checking the heartbeat of your AI application, which is critical for maintaining a high level of service availability.