Apache Spark Optimization
Production patterns for optimizing Apache Spark jobs including partitioning strategies, memory management, shuffle optimization, and performance tuning.
When to Use This Skill
- Optimizing slow Spark jobs
- Tuning memory and executor configuration
- Implementing efficient partitioning strategies
- Debugging Spark performance issues
- Scaling Spark pipelines for large datasets
- Reducing shuffle and data skew
Core Concepts
1. Spark Execution Model
Driver Program
↓
Job (triggered by action)
↓
Stages (separated by shuffles)
↓
Tasks (one per partition)
2. Key Performance Factors
| Factor | Impact | Solution |
|---|---|---|
| Shuffle | Network I/O, disk I/O | Minimize wide transformations |
| Data Skew | Uneven task duration | Salting, broadcast joins |
| Serialization | CPU overhead | Use Kryo, columnar formats |
| Memory | GC pressure, spills | Tune executor memory |