Agent skill · data analytics · aiskillstore
spark-optimization
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
Why this skill is useful
Provides specific optimization patterns and executable code snippets for Apache Spark that enhance performance tuning and debugging capabilities.
What it needs
About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 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 Partitions Parallelism Right-size partitions Quick Start Patterns Pattern 1: Optimal Partitioning Pattern 2: Join Optimization Pattern 3: Caching and Persistence Pattern 4: Memory Tuning Pattern 5: Shuffle Optimization Pattern 6: Data Format Optimization Pattern 7: Monitoring and Debugging Configuration Cheat Sheet Best Practices Do's Enable AQE - Adaptive query execution handles many issues Use Parquet/Delta - Columnar formats with compression Broadcast small tables - Avoid shuffle for small joins Monitor Spark UI - Check for skew, spills, GC Right-size partitions - 128MB - 256MB per partition Don'ts Don't collect large data - Keep data distributed Don't use UDFs unnecessarily - Use built-in functions Don't over-cache - Memory is limited Don't ignore data skew - It dominates job time Don't use .count() for existence - Use .take(1) or .isEmpty() Resources Spark Performance Tuning Spark Configuration Databricks Optimization Guide
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/spark-optimization--e43939