Hello,

Thanks for the follow up!

The configuration for spark.executor.memory and spark.executor.memoryOverhead serves distinct purposes within Spark's memory management:

spark.executor.memory: This controls the allocated memory for each executor's JVM heap. The JVM uses this memory to store application objects and execute tasks. However, as the heap memory usage grows, garbage collection processes can become slow and introduce latency.

spark.executor.memoryOverhead: This parameter accounts for additional memory beyond the JVM heap for handling specific elements:
JVM-related overhead, such as garbage collection metadata.
Internal Spark structures, including task metadata and shuffle buffers.
Other system-level activities, like Python interpreter memory when using PySpark

spark.executor.memoryOverhead helps to isolate and manage memory outside of the JVM heap. This ensures that operations requiring memory not directly related to application execution, such as managing task metadata or shuffle data buffers, do not interfere with the JVM heap space. Without this dedicated allocation, JVM heap memory might experience additional pressure, causing increased garbage collection overhead and performance instability.

Use of spark.executor.memory: Prioritized for application objects and task execution when JVM garbage collection overhead is not critical and workload fits well within the allocated heap memory

Use of spark.executor.memoryOverhead: Necessary for workloads with frequent shuffle operations or substantial auxiliary memory needs. It ensures operational stability by isolating this overhead