Complete Azure Databricks Cluster Recommendation

Question

Hi,

We are working on a project where we need to create Databricks cluster configurations recommendation with cluster versions, modes etc etc.
Can anyone help ?

Answer 1

Hi @Prasant Kumar Das

Welcome to Microsoft Q&A platform and thanks for posting your query here.

Creating effective Databricks cluster configurations is essential for optimizing performance and cost based on your project needs. Here’s a general recommendation to help you get started:

1. Cluster Mode: There are two types of cluster modes - Standard and High Concurrency. Standard mode is recommended for most use cases, while High Concurrency mode is recommended for scenarios where multiple users are sharing the same cluster.
2. Databricks Runtime Version: You should select a Databricks runtime version that is compatible with your Spark version and has the latest features and bug fixes.
3. Auto Optimize: Databricks also offers Auto Optimize features to enhance performance. This includes:
   • Adaptive Query Execution (AQE): Automatically optimizes query execution plans based on runtime statistics.
   • Optimized File Placement: Ensures that data files are stored optimally for faster access, leveraging techniques like file compaction and partitioning.
   You can enable these features to improve job performance without needing manual intervention.
4. Spot Instances: For non-critical jobs, consider using spot instances to reduce costs. However, keep in mind that these can be preempted.

Factors to Consider for Recommendation

To provide tailored recommendations, consider the following factors:

Workload Characteristics:

• Data Volume: The amount of data being processed.
• Processing Complexity: The complexity of data transformations and algorithms.
• Latency Requirements: The required response time for queries and jobs.
• Concurrency: The number of concurrent users or jobs.

Cost Constraints:

• Budget: The available budget for running the cluster.
• Cost Optimization: The need to minimize costs while maintaining performance.

Scalability Requirements:

• Peak Load: The expected peak workload.
• Scalability Needs: The ability to scale the cluster up or down to handle varying workloads.

Example Recommendation Framework

While specific recommendations will depend on your unique use case, here's a general approach:

• For Small, Interactive Workloads:
  • Cluster Mode: Single Node
  • Cluster Version: Latest Stable
  • Worker Type: Standard
  • Instance Pool: Recommended
  • Auto-Scaling: Disabled
• For Medium-Sized, Batch Processing Workloads:
  • Cluster Mode: Standard
  • Cluster Version: Latest Stable
  • Worker Type: Standard or High Memory (depending on data complexity)
  • Instance Pool: Recommended
  • Auto-Scaling: Enabled
• For Large, Mission-Critical Workloads:
  • Cluster Mode: High Availability
  • Cluster Version: Latest Stable
  • Worker Type: High Memory or High Compute (depending on workload)
  • Instance Pool: Recommended
  • Auto-Scaling: Enabled

Based on these factors, you can create a Databricks cluster configuration recommendation. You can also refer to the Azure Databricks documentation for more information on cluster configuration and best practices.