1. AI-Driven User Journey Simulation with Datadog Synthetics


    To perform AI-driven user journey simulation with Datadog Synthetics, you use Pulumi's Datadog provider. You'll create a synthetic test that mimics user interactions with your websites or APIs. This allows you to ensure that critical user journeys function correctly and to catch issues before they impact users.

    A synthetic test in Datadog is composed of multiple steps (browser or API tests) that follow the path a user would take through your application. Datadog's AI-driven capabilities can help you identify problems by automatically detecting changes in behavior, performance issues, or broken links.

    Below is a basic example of how you could set this up with Pulumi in Python. In this example, we create a Datadog Synthetics test for a fake API endpoint. The steps of the test will hit the endpoint and verify that it is responding successfully.

    Here's a breakdown of the elements in the program before we show you the code:

    • datadog.SyntheticsTest: This is a resource provided by Pulumi's Datadog provider that defines a synthetic test in Datadog.
    • synthetic_test: This is the name we give to our instance of a synthetic test.
    • type: In this parameter, we specify the type of the synthetic test (API or browser). For monitoring an API, we pass "api" as the value.
    • apiSteps: These are the steps for our synthetic test. We define what needs to be checked at each step.
    • assertions: These define the expected outcomes for each step (like expecting a certain HTTP response status code).

    The following program assumes you have already set up the Pulumi CLI, configured the Datadog provider, and have the necessary Datadog API and application keys in place:

    import pulumi import pulumi_datadog as datadog # Create a Datadog Synthetic Test to simulate and validate a user journey. synthetic_test = datadog.SyntheticsTest("ai-driven-user-journey", type="api", # This is an API test. Use "browser" for testing a web UI journey. subtype="http", # Use the HTTP subtype for an API test. request_definition={ "method": "GET", # The HTTP method to use. "url": "https://api.example.com/users", # The API endpoint to test. "timeout": 30 # How long to wait for a response before timing out. }, assertion=[ # Assertions are used to determine if the test was successful. { "type": "statusCode", # Verify the status code returned by the API. "operator": "is", # We use "is" to match a specific status code. "target": 200 # Expect a 200 OK response to signify success. }, { "type": "responseTime", # Ensure the response time is acceptable. "operator": "lessThan", # Check if the response time is less than a threshold. "target": 1000 # Target response time in milliseconds. } ], locations=["aws:us-east-1"], # Run this test from the US East Coast. message="User journey simulation test.", # A description for the test. tags=["env:production", "team:core"], # Tags to help organize and find tests in the Datadog UI. ) # Export the Synthetic Test public_id as a stack output, which you can use to access the test in the Datadog UI. pulumi.export("synthetic_test_id", synthetic_test.id)

    Upon running this Pulumi program, a Datadog Synthetic Test will be created. The test will be an API test that performs a GET request to https://api.example.com/users and checks that the response status code is 200 OK and the response time is less than 1 second.

    You can then go to the Datadog dashboard to see the test results, configure alerts based on this test, and use this as part of your continuous monitoring strategy. Remember to replace https://api.example.com/users with your actual API endpoint URL.

    For more detailed and tailored synthetic tests, you'd need to add more steps and assertions matching your user journey, perhaps including authenticated requests, specific payload checks, and multi-step transactions that resemble a real-world user flow. Datadog's documentation on synthetic tests can provide guidance on these more complex scenarios.

    Remember to consult the Datadog Pulumi provider documentation to understand all available properties and how they can be used to simulate user journeys accurately.