Setting Cluster Level Parameters for Serverless Spark Pool

In Azure Databricks, a serverless Spark pool (also known as an Auto-Scaling Pool) is a dynamic resource allocation mechanism that automatically scales up or down based on the workload demand. Cluster level parameters in a serverless Spark pool can be set using the Azure Databricks REST API or the Databricks CLI.

Using Databricks REST API to Set Cluster Level Parameters

To set cluster level parameters for a serverless Spark pool using the Databricks REST API, follow these steps:

Step 1: Generate an Access Token

Before making API calls, you need to generate a Databricks personal access token. You can do this from the Databricks UI by navigating to the "User Settings" -> "Access Tokens" and creating a new token.

Step 2: Get the Cluster ID

You need to know the Cluster ID of the serverless Spark pool for which you want to set the parameters. You can find the Cluster ID by navigating to the Databricks workspace and looking for the cluster's details.

Step 3: Set the Parameters

To set the cluster level parameters, use the "clusters/edit" API endpoint. You can send a POST request with the desired configuration parameters in the request body. The configuration parameter names and values are specific to Spark configuration settings.

Example: Set Cluster Level Parameters with cURL

        # Replace the values below with your actual information
        DATABRICKS_HOST=https://<databricks-instance>.azuredatabricks.net
        DATABRICKS_TOKEN=<your-access-token>
        CLUSTER_ID=<your-cluster-id>

        # Set the parameters in JSON format
        PARAMETERS='{
        "spark.databricks.cluster.profile":"serverless",
        "spark.databricks.cluster.instanceType":"Standard_DS3_v2"
        }'

        # Make the API call to set parameters
        curl -X POST -H "Authorization:Bearer $DATABRICKS_TOKEN" \
            "$DATABRICKS_HOST/api/2.0/clusters/edit" \
            -d "cluster_id=$CLUSTER_ID" \
            -d "spark_conf=$PARAMETERS"
    

In this example, we are setting two cluster level parameters: "spark.databricks.cluster.profile" to "serverless" and "spark.databricks.cluster.instanceType" to "Standard_DS3_v2". You can add more parameters as needed.

Please note that setting cluster level parameters for a serverless Spark pool will apply to all instances in the pool when they are created or restarted.

Keep in mind that programmatically setting cluster configurations via the Databricks REST API requires appropriate access permissions. Always exercise caution while changing cluster settings, as incorrect configurations could lead to unexpected behavior or resource inefficiencies.

Conclusion

Setting cluster level parameters for a serverless Spark pool in Azure Databricks can be accomplished using the Databricks REST API. By configuring the appropriate parameters, you can optimize the performance and resource usage of your Spark pool to meet the demands of your workloads effectively.

However, it's crucial to understand the implications of each parameter and make informed decisions based on your specific use case. Always thoroughly test your changes to ensure they align with your desired outcomes and optimize your Spark pool's performance.