Dynatrace AI-powered Root Cause Analysis

Question

Pulumi · Accepted Answer

If you're looking to integrate Dynatrace's AI-powered root cause analysis into your infrastructure for monitoring and insights, you will first need to utilize resources from the Dynatrace provider in Pulumi. Pulumi allows you to define and manage your infrastructure using code, which includes setting up and configuring monitoring tools like Dynatrace.

The Dynatrace provider in Pulumi offers various resources that you can use to set up AI-powered monitoring. One of the key resources is `dynatrace.K8sClusterAnomalies`, which provides capabilities for anomaly detection in Kubernetes clusters. This resource can be used to monitor the health and performance of your K8s clusters, leveraging Dynatrace's AI to automatically detect issues and their root causes.

Let's go through a Pulumi Python program that sets up Dynatrace AI-powered root cause analysis for a Kubernetes cluster:

```python
import pulumi
import pulumi_dynatrace as dynatrace

# This Pulumi program sets up Dynatrace's AI-powered root cause analysis for a Kubernetes cluster.

# Configure the Dynatrace resource for monitoring Kubernetes cluster anomalies.
# This example monitors pod saturation, where you can define your own thresholds and monitoring periods.
# You can adjust the configuration to monitor different aspects according to your requirements.
k8s_cluster_anomalies = dynatrace.K8sClusterAnomalies(
    "k8s-cluster-anomalies",
    pods_saturation=dynatrace.K8sClusterAnomaliesPodsSaturationArgs(
        enabled=True,
        configuration=dynatrace.K8sClusterAnomaliesPodsSaturationConfigurationArgs(
            threshold=80,  # Threshold for pod saturation in percentage
            sample_period_in_minutes=5,
            observation_period_in_minutes=30
        )
    ),
    readiness_issues=dynatrace.K8sClusterAnomaliesReadinessIssuesArgs(
        enabled=True,
        configuration=dynatrace.K8sClusterAnomaliesReadinessIssuesConfigurationArgs(
            sample_period_in_minutes=5,
            observation_period_in_minutes=30
        )
    ),
    monitoring_issues=dynatrace.K8sClusterAnomaliesMonitoringIssuesArgs(
        enabled=True,
        configuration=dynatrace.K8sClusterAnomaliesMonitoringIssuesConfigurationArgs(
            sample_period_in_minutes=5,
            observation_period_in_minutes=30
        )
    ),
    cpu_requests_saturation=dynatrace.K8sClusterAnomaliesCpuRequestsSaturationArgs(
        enabled=True,
        configuration=dynatrace.K8sClusterAnomaliesCpuRequestsSaturationConfigurationArgs(
            threshold=80,  # CPU requests saturation threshold in percentage
            sample_period_in_minutes=5,
            observation_period_in_minutes=30
        )
    ),
    memory_requests_saturation=dynatrace.K8sClusterAnomaliesMemoryRequestsSaturationArgs(
        enabled=True,
        configuration=dynatrace.K8sClusterAnomaliesMemoryRequestsSaturationConfigurationArgs(
            threshold=80,  # Memory requests saturation threshold in percentage
            sample_period_in_minutes=5,
            observation_period_in_minutes=30
        )
    )
)

# Export the resource name for further usage or reference.
pulumi.export("k8s_cluster_anomalies_name", k8s_cluster_anomalies.name)
```

Explanation of this code:

- **Import Statements**: We import the necessary Pulumi packages to work with. In this case, `pulumi_dynatrace` is used to interact with Dynatrace resources.
- **Resource Configuration**: We define a `K8sClusterAnomalies` resource named `k8s-cluster-anomalies`, where we specify different types of issues to monitor like pod saturation, readiness issues, and resource requests saturation for CPU and memory.
- **Configuration Arguments**: For each anomaly we aim to monitor, we create appropriate configuration arguments, defining thresholds and periods for sampling and observation. This is where the AI-power comes into play, as Dynatrace will use these configurations to determine what constitutes an anomaly and thereby aid in root cause analysis.
- **Export**: Finally, we export the resource name, which can be useful if you need to reference this particular setup elsewhere in your infrastructure code or in Pulumi's outputs.

Keep in mind that a real-world setup would require additional configuration, such as credentials and specific definitions tailored to your Kubernetes cluster and what aspects you want the AI analysis to focus on. Ensure that all required properties are appropriately set as per your Dynatrace environment qualities.

For a more detailed overview and any extra configuration options you may need, refer to the [Dynatrace Pulumi Provider documentation](https://www.pulumi.com/registry/packages/dynatrace/api-docs/).