Spark Optimization

概要

Provides production-ready patterns and configuration guidance for scaling Apache Spark data processing pipelines. It offers technical insights into the Spark execution model, covering everything from adaptive query execution (AQE) and memory tuning to sophisticated join strategies like salting and bucket joins. This skill is essential for data engineers looking to resolve performance bottlenecks, eliminate data skew, and minimize resource overhead in high-throughput distributed environments.

主な機能

• Memory tuning guidelines for executors and overhead management
• Performance monitoring tools for detecting data skew and analyzing query plans
• Advanced partitioning strategies including repartitioning and coalescing
• Join optimization techniques like broadcast, bucket, and salted joins
• Adaptive Query Execution (AQE) and shuffle optimization patterns
• 23,139 GitHub stars

ユースケース

• Debugging and resolving slow-running Spark jobs or OOM errors
• Scaling data pipelines to handle significantly larger datasets
• Implementing cost-effective resource allocation for production clusters