spark-optimization

Apache Spark job optimization through partitioning, memory tuning, shuffle reduction, and join strategies. Production patterns for optimizing Apache Spark jobs including partitioning strategies, memory management, shuffle optimization, and performance tuning. - Covers partitioning strategies, broadcast joins, bucketed joins, and skew handling with salting techniques to minimize shuffle overhead - Includes caching and persistence patterns with storage level selection, checkpointing for complex lineages, and memory configuration breakdown - Provides data format optimization for Parquet and Delta Lake, column pruning, predicate pushdown, and Z-ordering for multi-dimensional queries - Features monitoring and debugging patterns including query plan analysis, stage metrics tracking, and partition skew detection - Adaptive Query Execution (AQE) configuration and production-ready settings for executor memory, parallelism, serialization, and compression - 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 - 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...