Spark Executor Memory Breakdown -short

#spark #bigdata

🚀 Spark Executor Memory Breakdown 🚀

When optimizing our Spark jobs, understanding the composition of executor memory is paramount. Here’s a deep dive:

✨ User Memory (UM):

Role: Memory utilized for Spark RDDs, DataFrames, and user-created objects.
Key tasks: Storing data structures, RDDs, and more.
✨ Reserved Memory (RM):

Approximately 300MB.
Reserved exclusively for Spark’s internal operations.
✨ Execution Memory (EM):

Role: Handles computational tasks.
Key processes: Shuffles, joins, sorts, and aggregations.
🔍 Total Executor Memory: The complete memory is the cumulative sum: Executor Memory = UM + RM + EM

Diving into these details empowers us to better allocate resources for our Spark applications, ensuring efficient operations and optimal performance.