Aggregating Logs for AI Applications Across GKE Clusters

Question

Pulumi · Accepted Answer

Aggregating logs across multiple Google Kubernetes Engine (GKE) clusters is a common requirement for monitoring and analyzing the performance and behavior of AI applications that may be distributed over several clusters. You can achieve this by using Google Cloud's logging features: logs-based metrics, logs sinks, and log views.

In the context of Pulumi, we will be utilizing the `google-native` Pulumi provider which provides direct mappings to Google Cloud resources. We will work with the following resources:

- **google-native.logging.v2.Metric**: To create logs-based metrics, which are useful for extracting and analyzing specific information from your logs data. They can provide valuable insights and trigger alerts based on log data patterns.
  
- **google-native.logging.v2.Sink**: To create a logging sink, which can export log entries outside of the Stackdriver Logging, such as to Pub/Sub, BigQuery, or Cloud Storage. You can use the sink to aggregate logs from multiple GKE clusters and store them centrally for AI analysis.

- **google-native.logging.v2.Bucket**: This can be used in conjunction with views and sinks to create a centralized logging solution. A bucket can hold logs and a view provides read access to logs.

- **google-native.container.v1beta1.Cluster**: While not directly involved in log aggregation, this resource is listed as it is important for enabling log export when creating or configuring GKE clusters. You want to ensure that logging is properly configured for each GKE cluster you intend to aggregate logs from.

Let's set up a Pulumi program in Python that could create such an infrastructure:

1. Define the logs-based metrics to extract desired data from the logs.
2. Set up a logging sink to export and aggregate logs to a desired destination.
3. Create log buckets and views to store and access the aggregated logs.

We'll demonstrate creating a log-based metric and a sink that exports logs to a BigQuery dataset, which could be useful for AI applications to analyze the data. We assume that the GKE clusters are already created and configured to emit logs.

Here is how you would set it up in Python using Pulumi:

```python
import pulumi
import pulumi_google_native as google_native

# Configuration
project_id = 'your-google-cloud-project-id' # Replace with your GCP project ID.
destination_dataset = "bigquery.googleapis.com/projects/{project_id}/datasets/my_dataset"  # Replace with your destination BigQuery dataset.

# Create a logs-based metric for your GKE clusters
ai_app_log_metric = google_native.logging.v2.Metric("aiAppLogMetric",
    parent=f"projects/{project_id}",
    metric={
        "name": "ai-application-metric",
        "description": "Metric for AI application logs",
        "filter": 'resource.type="k8s_cluster" AND jsonPayload.message:"AI Model Training"',
        "metricDescriptor": {
            "metricKind": "DELTA",
            "valueType": "INT64",
            "displayName": "AI Model Training Logs",
            "labels": [{"key": "cluster_name", "description": "Name of the GKE cluster"}],
        },
    })

# Create a sink to aggregate logs and export them to BigQuery
ai_app_logs_sink = google_native.logging.v2.Sink("aiAppLogsSink",
    parent=f"projects/{project_id}",
    sink={
        "name": "ai-application-logs-sink",
        "description": "Sink for AI application logs",
        "destination": destination_dataset,
        "filter": 'resource.type="k8s_cluster" AND jsonPayload.message:"AI Model Training"',
        "includeChildren": True, # Include logs from all associated GKE clusters.
    })

pulumi.export("aiAppLogMetricName", ai_app_log_metric.metric.name)
pulumi.export("aiAppLogsSinkName", ai_app_logs_sink.sink.name)
```

In this program, we:

1. Import the required Pulumi modules for Google Cloud services.
2. Define our GCP project ID and the destination for our logs (a BigQuery dataset in this case).
3. Create a logs-based metric, which filters for specific log messages that are associated with AI model training in GKE clusters.
4. Create a logging sink that matches the same filter used in the metric, directs the logs to our BigQuery dataset, and includes children resources (additional GKE clusters).
5. Export the name of the created metric and sink, which are identifiers you can use to interface with these resources via GCP's SDK or command-line tools.

You would need to ensure that the GKE clusters are already set up and correctly configured to send logs to Google Cloud Logging for the above setup to be effective. It's important to have appropriate permissions to create these resources in your GCP project. This setup is scalable and can be modified to suit various logging requirements and export destinations, such as Pub/Sub or Cloud Storage for different analytical needs or workflows.

Remember to replace the `project_id` and `destination_dataset` with your actual Google Cloud project ID and the BigQuery dataset URI you wish to aggregate logs to. Your BigQuery dataset should already be created and have the appropriate schema for handling log entries.