Como aprendi Apache Spark — Parte 5: Joins — Shuffle, Broadcast e estratégias de execução

Apache Spark performance relies on understanding how data moves between machines, as Shuffle operations during Joins are computationally expensive. While SQL joins appear simple, their physical execution in distributed clusters is complex. Furthermore, memory management extends beyond simply increasing RAM. Issues like OutOfMemory can stem from various components, including the Driver, Executors, Partitions, and Garbage Collection. Effective optimization requires identifying whether the problem lies in Cache, Serialization, or Memory Overhead rather than just adding hardware resources.