Real-Time Anomaly Detection using Dynatrace AI

Question

Pulumi · Accepted Answer

To implement real-time anomaly detection using Dynatrace AI, we'll leverage the capabilities of Pulumi to deploy and manage the required cloud infrastructure and monitoring setup.

Dynatrace offers AI-powered monitoring which provides full-stack visibility along with advanced anomaly detection and problem resolution. Using Dynatrace's AI capabilities, you can automate the detection of anomalies in your cloud infrastructure, applications, and services.

In this program, we will use Pulumi's Dynatrace provider to create resources that monitor anomalies in a Kubernetes cluster. Specifically, we'll create a resource for monitoring Kubernetes cluster anomalies `dynatrace.K8sClusterAnomalies`, which integrates with Dynatrace AI-powered monitoring.

Here's a Pulumi program in Python that demonstrates how to set up real-time anomaly detection for a Kubernetes cluster using Dynatrace:

```python
import pulumi
import pulumi_dynatrace as dynatrace

# Instantiate a new Dynatrace K8sClusterAnomalies resource.
# This resource allows Dynatrace's AI to monitor the Kubernetes cluster for potential
# saturation issues, readiness issues, and monitoring issues.
k8s_cluster_anomalies = dynatrace.K8sClusterAnomalies(
    "my-cluster-anomalies",
    # The podsSaturation property defines the conditions under which Dynatrace AI
    # will consider pod saturation to be an anomaly.
    podsSaturation=dynatrace.K8sClusterAnomaliesPodsSaturationArgs(
        enabled=True, # Enable monitoring for pod saturation.
        configuration=dynatrace.K8sClusterAnomaliesPodsSaturationConfigurationArgs(
            threshold=90,                    # Set threshold for pod CPU/memory usage.
            samplePeriodInMinutes=5,         # Define the sampling period for monitoring data.
            observationPeriodInMinutes=30,   # Define the observation period for detecting trends.
        ),
    ),
    # Similar configuration for readiness and monitoring issues follows the same pattern as above.
)

# Export the ID of the K8sClusterAnomalies monitoring setup to view later in the Pulumi CLI.
pulumi.export("k8s_cluster_anomalies_id", k8s_cluster_anomalies.id)
```

In this program, we have defined a Dynatrace K8sClusterAnomalies resource named `my-cluster-anomalies` which will allow Dynatrace to monitor our Kubernetes cluster for potential issues that might saturate the pods' resources. We have configured it to monitor for conditions where the CPU/memory usage exceeds 90%, sampling every 5 minutes, and observing trends over a 30-minute period. If these conditions are met, Dynatrace AI will consider it an anomaly and notify according to the configured alerts.

Please note that you would need to have Dynatrace set up with access to your Kubernetes cluster and appropriate permissions associated with your Pulumi script to create these resources through the Pulumi Dynatrace provider.

To use this Pulumi program, ensure that you have the following prerequisites in place:

1. A Dynatrace account with access to the AI-powered monitoring features.
2. The Pulumi CLI installed and configured with appropriate credentials.
3. The `pulumi-dynatrace` plugin installed, which provides the resources needed to create Dynatrace monitoring configurations via Pulumi.

Once all prerequisites are met, you can run this program using the Pulumi CLI to deploy your anomaly detection setup.

The `pulumi.export` line in the code makes it convenient to retrieve the ID of the monitoring resource after Pulumi has successfully performed the deployment. You can use the `pulumi stack output k8s_cluster_anomalies_id` command in the CLI to get this ID which you can then use within the Dynatrace console to further inspect or configure your monitoring settings.