Optimizing Databricks Cluster Usage in DataFactory Pipeline

Question

I'm working with various Data bricks notebooks to read, transform, and write data to and from ADLS. These notebooks are connected in a DataFactory pipeline as follows:

NB1 -->NB2-->NB3--->NB4

I've set up a connection from DataFactory to Databricks and added it to my notebook activities. I want to start a Databricks cluster when the pipeline is triggered. While everything is functioning, Databricks starts a job cluster for each notebook activity, which seems unnecessary and takes too long.

Is it possible to start a single cluster at the beginning of the pipeline and shut it down after all notebooks are completed? Or are there reasons why having a separate job cluster for each activity is beneficial?

Answer 1

Yes, it is possible to start a single Databricks cluster at the beginning of your pipeline and shut it down after all the notebooks are completed. This approach can be more efficient in terms of resource usage and can significantly reduce the time taken by your pipeline since you avoid the overhead of starting and stopping a cluster for each notebook.

Optimizing Databricks Cluster Usage in Azure Data Factory Pipeline

1. Use an Existing Interactive Cluster:

• You can configure your Data Factory pipeline to use an existing interactive (also known as an all-purpose) cluster instead of creating new job clusters for each notebook. This allows you to use the same cluster throughout the entire pipeline.
• How to configure:
  • In your Data Factory pipeline, when setting up the Databricks notebook activity, select the option to use an existing cluster instead of creating a new job cluster. You will need to specify the cluster ID of the interactive cluster.
• Pros:
  • Reduced cluster start-up time.
  • Better resource utilization as the cluster is shared across multiple notebook activities.
• Cons:
  • Requires manual management of the cluster, including starting and stopping it, which could lead to additional costs if the cluster is not properly shut down after use.

2. Reuse Job Clusters:

• While you cannot directly "reuse" job clusters within Data Factory, you can create a cluster at the beginning of the pipeline and use it across multiple notebook activities by passing the cluster information (such as cluster ID) between the activities.
• How to configure:
  • In the first step of your pipeline, trigger a Databricks API call to create a cluster. Store the cluster ID in a variable or an Azure Key Vault.
  • In subsequent notebook activities, reference the cluster ID from the variable or Key Vault to reuse the same cluster.
  • After the last notebook activity, add another activity to shut down the cluster using the Databricks API.
• Pros:
  • Similar benefits as using an existing cluster with the additional advantage of automated cluster lifecycle management.
• Cons:
  • Slightly more complex setup due to the need for API calls and cluster management.

3. Reasons to Use Separate Job Clusters:

• Isolation: Each job cluster is isolated, ensuring that resources (CPU, memory) are dedicated to a specific notebook without interference from other notebooks. This is particularly useful if the notebooks have vastly different resource requirements or if one notebook might exhaust the resources.
  • Scalability: Separate job clusters can be configured with different specs depending on the needs of each notebook. For instance, a notebook performing heavy data processing might require a more powerful cluster compared to a notebook executing lightweight operations.
  • Fault Isolation: If one notebook activity fails, the failure is isolated to that specific job cluster, potentially making it easier to troubleshoot.

Best Practices:

• Evaluate Resource Needs: Assess the resource requirements of each notebook. If they are similar and resource constraints are not a concern, using a single cluster might be optimal.
• Monitor Costs: Always keep an eye on the cluster costs, especially when using long-running or interactive clusters.
• Automate Cluster Management: Whether you choose to use an existing cluster or create/reuse a job cluster, ensure that cluster start-up and shut-down processes are automated to avoid unnecessary costs.