Explanation
Spark's memory usage can be divided into three categories: Execution, transaction, and storage.
No, it is either execution or storage.
As a general rule for garbage collection, Spark performs better on many small objects than few big objects.
No, Spark's garbage collection runs faster on fewer big objects than many small objects.
Disabling serialization potentially greatly reduces the memory footprint of a Spark application.
The opposite is true - serialization reduces the memory footprint, but may impact performance in a negative way.
Spark uses a subset of the reserved system memory.
No, the reserved system memory is separate from Spark memory. Reserved memory stores Spark's internal objects.
More info: Tuning - Spark 3.1.2 Documentation, Spark Memory Management | Distributed Systems Architecture, Learning Spark, 2nd Edition, Chapter 7