1. Anomaly Detection for AI Metrics with New Relic AlertCondition


    Anomaly detection is a critical task in monitoring applications and infrastructure in the cloud. It helps to identify unusual patterns that may indicate a potential issue with the application or infrastructure. By setting up anomaly detection, you can proactively address issues before they become more significant problems, improving the reliability and performance of your systems.

    In this context, we are going to use New Relic's Alert Conditions to create anomaly detection for AI metrics. New Relic is a popular observability platform that provides a rich set of tools for monitoring applications, including setting alert conditions based on queries of their data.

    Pulumi's New Relic provider gives us the ability to configure these monitoring tools as code. This means we can automate the setup of our monitoring configurations and track them in version control, like any other piece of our infrastructure.

    Here is a program written in Python using Pulumi's New Relic provider to create an anomaly detection condition for AI metrics. The NrqlAlertCondition resource allows us to define a condition based on a NRQL (New Relic Query Language) query. This query will specify the AI metrics we want to monitor and the thresholds that will trigger an alert.

    import pulumi import pulumi_newrelic as newrelic # Configure the New Relic provider # Ensure you have the appropriate New Relic API key configured in your environment, as Pulumi will use it to authenticate. # Define a NRQL alert condition for AI metrics anomaly detection ai_metrics_alert_condition = newrelic.NrqlAlertCondition("aiMetricsAlertCondition", policy_id=123456789, # Replace with your New Relic policy ID name="AI Metrics Anomaly Detection", nrql=newrelic.NrqlAlertConditionNrqlArgs( query="SELECT average(duration) FROM Transaction WHERE appName='YourAppName'", # Replace with your specific NRQL query evaluation_offset=3 ), critical=newrelic.NrqlAlertConditionCriticalArgs( operator="above", threshold=1.5, # Set your desired threshold for the alert threshold_duration=300, # Duration (in seconds) to evaluate the threshold threshold_occurrences="AT_LEAST_ONCE" ), type="static", value_function="SINGLE_VALUE", runbook_url="https://example.com/runbook", # Optional: URL to a runbook with remediation steps if alert is triggered enabled=True ) # Export the ID of the alert condition pulumi.export("alert_condition_id", ai_metrics_alert_condition.id)

    In this example, we define a NRQL query that selects the average duration of transactions for a specified application. It's important to replace the query expression with one that fits your own use case. The threshold set in the critical argument specifies the value at which an alert will be created. In this case, if the average duration goes above 1.5 seconds, the policy will trigger an alert.

    Note that the policy_id parameter should be replaced with the ID of the policy you want to associate this condition with. To get policy ID, you may need to use New Relic's web interface or API.

    The evaluation_offset in the NRQL query structure defines the number of minutes to offset for data aggregation, which can help to prevent false positives due to short-term spikes.

    Finally, the runbook_url is an optional field where you can provide a link to a set of instructions (a runbook) that should be followed when this condition triggers an alert.

    To use this Pulumi program, make sure to have Pulumi installed and configured with your New Relic provider. The New Relic API key must be set in the environment or through the Pulumi configuration.

    This automation can save significant time and reduce errors in monitoring setup, especially when the same pattern needs to be rolled out across multiple services or environments.