Optimizing Spark Jobs in Fabric

Spark performance in Fabric notebooks can be significantly improved through proper configuration, data handling, and code optimization.

Understanding Fabric Spark

Auto-scaling Clusters Fabric manages cluster sizing automatically: - Starts with configured size - Scales based on workload - No manual cluster management

Spark Sessions Sessions are created per notebook run: - First cell may take time (cold start) - Subsequent cells run faster - Session ends after idle timeout

Optimization Strategies

Data Partitioning Partition by frequently filtered columns: - Date columns for time-based analysis - Region for geographic queries - Avoid over-partitioning small datasets

Caching Cache DataFrames used multiple times: - df.cache() for in-memory - df.persist() with storage level options - Remember to unpersist when done

Broadcast Joins For small lookup tables: - Broadcast the smaller table - Avoids expensive shuffle operations - Significantly faster for dimension lookups

Column Pruning Select only needed columns early: - Reduces data movement - Improves memory usage - Faster processing

Code Best Practices

• Avoid collect() on large datasets
• Use filter pushdown to sources
• Minimize shuffle operations
• Prefer DataFrame API over RDD
• Use appropriate data types

Monitoring Performance

• Review Spark UI for job details
• Check executor memory usage
• Identify shuffle-heavy stages
• Monitor data skew issues