Synthetics Canary Testing for AI APIs with Datadog

Question

Pulumi · Accepted Answer

Synthetic monitoring is a technique used to simulate user interactions with a system to ensure availability and performance. Datadog provides synthetic tests that allow you to simulate traffic and interaction with your endpoints or webpages to ensure they're functioning correctly and efficiently.

In Datadog, a Synthetic Canary Test is used to continuously validate the uptime of an API or a website. You can configure it to run at specified intervals, verify the return of the expected data, and then alert you if something is wrong.

For your goal of setting up synthetic canary testing for AI APIs with Datadog, you would use the `datadog.Monitor` resource from the Datadog provider in Pulumi. This resource allows you to create and configure a Datadog monitor which can perform several types of checks, including those necessary for synthetic testing.

Below you'll find a Pulumi program written in Python, that sets up a synthetic API test in Datadog. We'll create an HTTP check, which is a common type of Synthetic test. This test will regularly send HTTP requests to the specified AI API endpoint and validate that it is accessible and returns the correct response.

Let's start with importing the required modules and setting up the Datadog `Monitor` resource. The `type` attribute specifies the nature of the monitoring (in this case, an HTTP check), `query` defines the request to be made and the conditions for considering the test to pass or fail, and `message` provides a description of the monitor which is useful for alerting.

```python
import pulumi
import pulumi_datadog as datadog

# API URL that you want to perform canary testing on
api_url = "https://api.yourdomain.com/v1/your_ai_service"

# This monitor will perform a check against the AI API to ensure it's online and responding correctly
canary_test_monitor = datadog.Monitor("ai-api-canary-test-monitor",
    name="AI API Canary Test",
    type="http check",
    query="""configurations({
        "request_method":"get",
        "url":"${api_url}",
        "check":{"expected_status_code":[200],"expected_response_time":3000}
    })""",
    message="AI API endpoint is down! @pagerduty", # Alerting channels can be specified here
    tags=["ai-api", "canary-test"],
    options={
        "notify_no_data": False,
        "notify_audit": False,
        "locked": False,
        "timeout_h": 24,
        "new_host_delay": 300,
        "require_full_window": True,
        "new_group_delay": 300,
        "include_tags": True,
        "renotify_interval": 0
    }
)

# Export the ID of the monitor so it can be referenced later if needed
pulumi.export("canary_test_monitor_id", canary_test_monitor.id)
```

In this Pulumi program, `canary_test_monitor` specifies an HTTP check that sends GET requests to the API URL. It expects a response with a status code 200 (HTTP OK) within 3000 milliseconds.

Additional configuration parameters are provided to customize the behavior of the monitor:
- `notify_no_data`: When set to `False`, this prevents alerts from being sent in the absence of data.
- `notify_audit`: When set to `False`, this prevents audit notifications.
- `locked`: When set to `False`, this allows changes to the monitor by any user. Setting it to `True` restricts edits to the creator and admins.
- `timeout_h`: This is the number of hours before the monitor will automatically resolve from a triggered state.
- `new_host_delay`: The delay in seconds before collecting data from a new host.
- `require_full_window`: A boolean indicating whether this monitor needs a full window of data before it's evaluated.
- `new_group_delay`: The delay in seconds before an alert notification is sent.
- `include_tags`: A boolean indicating whether tags should be included with events.
- `renotify_interval`: How often, in seconds, to resend notifications that remain in the "triggered" state.

Remember to replace `api_url` with your actual API endpoint and customize the `message` with the appropriate alerting handles you have set up in your Datadog account. Additionally, make sure you have your Datadog provider configured with your API and application keys.

After executing this program with Pulumi, you will have created a monitor in Datadog that regularly checks the health of your AI API and alerts you if it becomes unreachable or performs poorly.