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4.3.3. Spark Performance Optimization
💡 First Principle: Spark performance depends on parallelism (partition count), data locality (caching), and resource utilization (executor configuration). Optimizing one dimension while ignoring others produces limited improvement.
Key Optimization Techniques
| Technique | Purpose | When to Use |
|---|---|---|
| Dynamic Allocation | Right-size executor count | Variable workloads |
| Caching | Keep frequently accessed data in memory | Iterative processing |
| Broadcast Join | Avoid shuffle for small tables | Joining large + small tables |
| Partition Pruning | Skip irrelevant partitions | Filtered queries |
Native Execution Engine
- Concept: Fabric's optimized Spark engine for common operations
- Benefit: Significant performance improvement for supported operations
- Limitation: Does not support User-Defined Functions (UDFs)
⚠️ Exam Trap: Using UDFs with native execution engine causes fallback to traditional Spark. If using UDFs, expect performance regression compared to native-supported operations. Questions about "slow UDF performance" are testing this knowledge.
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Written byAlvin Varughese
Founder•20 professional certifications