Databricks and Apache Spark Performance Tuning

Lesson 2: The Low Hanging Fruit

Overview

• Presenter: Brian Caky
• Series: Databricks & Apache Spark Performance Tuning
• Lesson Focus: Easy and initial fixes for performance tuning by getting resources right at the start.

Topics Covered

1. Why compute resources matter.
2. Databricks workspace: Standard vs Premium.
3. Databricks and Apache Spark cluster architecture.
4. Hardware considerations under the architecture.
5. Step-by-step guide to optimizing cluster configuration.
6. Focus on shuffles and spills.

Importance of Compute Resources

• Cloud Performance Tuning: Often overlooked, but crucial for optimizing workloads.
• Cost vs Performance: Legacy SQL servers could throw hardware at a problem, but with Databricks, it's about finding the right balance to get best performance at optimal cost.
• Initial Setup: Getting resource allocation right initially is the most effective way to boost performance.
• Interdependencies: Performance optimization and compute resource selection are interconnected.

Databricks Workspace: Standard vs Premium

• Recommendation: Use Premium for better features and performance, especially for role-based access controls and Unity Catalog.
• Unity Catalog & Delta Live Tables: Both require Premium tier.
• Photon: Does not require Premium tier but offers excellent performance improvements.

Cluster Architecture

• Driver Node: Coordinates all the work, partitions data, distributes tasks to worker nodes.
• Worker Nodes: Perform tasks such as data storage and execution.
  • Core: Determines level of parallelism.
  • Memory: Split between storage (caching) and working memory.
  • Disk Speed: Important for performance; SSDs are faster.
• Shuffles and Spills: Costly operations that occur during data rearrangement (e.g., joins, aggregations).

Hardware Considerations

• CPU and GPU Types: Use depends on workload (e.g., CPUs for data transformations, GPUs for machine learning).
• Memory: Needs to be sufficient to avoid spills and crashes.
• Network: Latency and bandwidth can affect performance, especially when using external storage.
• Storage: Fast, localized storage (e.g., SSDs) is better.

Optimizing Cluster Configuration

• Cluster Creation: Using Azure Databricks Workspace, selecting compute resources.
• Workload Types: Different recommendations for analysis, ETL, and machine learning.
• Node Types: Larger, fewer nodes generally better than many small nodes.
• Spot Instances: Cost-effective but less reliable option.
• Serverless Compute: Instantly available clusters, saving time but may have some costs.
• Photon: Vastly improves performance by replacing JVM with C code.
• Access Modes: Single user vs shared with isolation vs no isolation.
• Worker and Driver Configuration: Type and size of VMs can be optimized based on workload.

Shuffles and Spills

• Spills: Occur when nodes run out of memory, leading to data being written to disk, which is slower.
• Monitoring: Use Spark UI to monitor and identify spills and shuffles.
• Adaptive Query Execution (AQE): Automatically optimizes query execution plans.

Summary

• Compute Resources are Critical: The foundation of performance optimization.
• Premium Workspace: Generally recommended for better features and performance.
• Cluster Architecture: Understanding the architecture helps in making informed choices about resource allocation.
• Step-by-Step Optimization: Focus on node type, size, and configuration to match workloads.
• Shuffles and Spills: Key areas where performance hits occur; monitoring and optimization are crucial.

Conclusion

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• Personal Note: Thank you for watching and supporting.

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