1. Predictive Maintenance with Custom Performance Counters


    Predictive maintenance is the strategy of monitoring equipment during operation to predict when maintenance should be performed. In the context of cloud infrastructure and application monitoring, predictive maintenance often involves tracking custom performance counters or metrics to assess the health of an application or system. By monitoring and analyzing these metrics, one can predict potential system failures before they occur and perform maintenance preemptively.

    To set up predictive maintenance using Pulumi, we could use the dynatrace.CustomAnomalies resource from the Dynatrace provider. Dynatrace is a software intelligence platform that provides full-stack observability across applications, cloud infrastructure, and user experience. Below, I will guide you through creating a Pulumi program in Python which configures custom anomalies for application performance monitoring with Dynatrace.

    First, ensure you have the appropriate Dynatrace provider installed for Pulumi:

    pip install pulumi-dynatrace

    Then, you can use the following Pulumi program to define custom anomalies:

    import pulumi import pulumi_dynatrace as dynatrace # Custom anomaly configuration for application performance monitoring custom_anomaly = dynatrace.CustomAnomalies("customAnomaly", name="High CPU Usage Alert", scopes=[dynatrace.CustomAnomaliesScopesArgs( tags=[dynatrace.CustomAnomaliesScopesTagsArgs( filter=dynatrace.CustomAnomaliesScopesTagsFilterArgs( key="Host", value="production", context="CONTEXTLESS" ) )] )], enabled=True, metricId="builtin:host.cpu.usage", severity="HIGH", strategy=dynatrace.CustomAnomaliesStrategyArgs( auto=dynatrace.CustomAnomaliesStrategyAutoArgs( samples=5, alertCondition="ABOVE", alertingOnMissingData=False, dealertingSamples=5, violatingSamples=5, signalFluctuations=0.1 ) ), description="Triggers an alert when CPU usage is consistently high over a period of time." ) # Export the ID of the custom anomaly configuration pulumi.export("customAnomalyId", custom_anomaly.id)

    In the example above, we create a CustomAnomalies resource named customAnomaly with the following attributes:

    • name: This is a human-readable name for the custom anomaly that we're defining, in this case, "High CPU Usage Alert".
    • scopes: Specifies the scope of the entities this custom anomaly applies to. Here, we're filtering by a tag with the key "Host" and value "production".
    • enabled: Indicates whether the custom anomaly detection rule is active. We are setting this to True.
    • metricId: This is the identifier of the metric we are tracking, in this case, CPU usage of the host.
    • severity: Defines the level of the anomaly, which is "HIGH" for significant events.
    • strategy: Details the anomaly detection rules. We're using an automated strategy that samples 5 data points, triggers an alert when the condition is "ABOVE" a threshold, and does not alert on missing data.
    • description: Provides a more detailed explanation of the custom anomaly detection rule and when it should trigger.

    Make sure to adjust the scope, metricId, and other parameters to suit the specific performance counters and thresholds relevant to your environment. After running this Pulumi program, it will deploy the custom anomaly rule to your Dynatrace environment, helping you to monitor application performance and tackle potential issues proactively.

    To apply this configuration in your environment, save this code in a file (for example, main.py), ensure you have the Pulumi CLI setup, and run pulumi up.

    This will show you a preview of the changes Pulumi plans to make. After you confirm the preview, Pulumi will apply the changes, and you'll have the Dynatrace custom anomaly configured as code, which helps streamline and version control your monitoring as infrastructure configuration.