1. Datadog AI-Powered Anomaly Detection for Infrastructure Health

    Python

    Certainly! AI-Powered Anomaly Detection in Datadog can be used to automate the monitoring of your infrastructure. It leverages machine learning to identify abnormal behavior in the performance data of your servers, databases, tools, applications, and services.

    Here's how you can set up Datadog anomaly detection using Pulumi in Python:

    We're going to define a Monitor resource using Pulumi's Datadog provider. This monitor will track a chosen metric for anomalies. The query parameter specifies what the monitor is tracking: for instance, the average CPU load, memory usage, etc., over a given timeframe. The type anomaly tells Datadog that this monitor is for anomaly detection.

    1. Install the Pulumi Datadog Provider: Before running the code, ensure you have installed the Pulumi Datadog provider by running pip install pulumi_datadog.

    2. Pulumi Program: In the Pulumi program, replace placeholders such as <DATADOG_API_KEY>, <DATADOG_APP_KEY>, and <YOUR_METRIC_QUERY> with your actual Datadog API and application keys and the metric query you want to monitor for anomalies.

    3. Metric Query: In the query parameter within the Monitor resource, you should define a Datadog query that fits what you want to track. For example, you might want to monitor the CPU usage on all hosts tagged role:webserver. A query for this might look like: "avg(last_10m):avg:system.cpu.user{role:webserver} by {host} > 50".

    Let's look at the program:

    import pulumi import pulumi_datadog as datadog # Configure Datadog provider with your Datadog API key and application key datadog_provider = datadog.Provider("datadog_provider", api_key="<DATADOG_API_KEY>", app_key="<DATADOG_APP_KEY>") # Create a Datadog Monitor for Anomaly detection anomaly_monitor = datadog.Monitor("anomaly-monitor", name="Anomaly Detection for CPU Usage", type="anomaly", query="<YOUR_METRIC_QUERY>", # Replace with your metric query message="Alert! Anomaly detected in CPU usage", tags=["environment:production", "team:core"], provider=datadog_provider) # After setting up the monitor, you can get the URL to access it directly in Datadog pulumi.export("monitor_url", anomaly_monitor.url)

    In the code above:

    • We begin by importing the necessary modules.
    • We configure the Datadog provider by directly passing the API and App keys. In a production environment, you should use Pulumi secrets to manage these keys securely.
    • We define anomaly_monitor, where you provide a descriptive name, set the type to anomaly, and write your Datadog query.
    • We include an alert message that will be sent when an anomaly is detected.
    • We add some descriptive tags for the monitor to group and filter within the Datadog dashboard.
    • At the end of the program, we're exporting the URL of the monitor, which will allow you to quickly navigate to this monitor's dashboard in Datadog.

    After writing this code in your Pulumi program file, executing it with pulumi up will reach out to Datadog and set up your anomaly detection monitor.

    You would need to adapt the query to match the specifics of the metric you are interested in monitoring. Datadog provides extensive documentation on how to construct these queries for different use cases. It's recommended to explore those or use predefined queries from your existing Datadog dashboards.


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