You should take also into account that spark has different option to represent
data in-memory, such as Java serialized objects, Kyro serialized, Tungsten
(columnar optionally compressed) etc. the tungsten thing depends heavily on the
underlying data and sorting especially if compressed.
Then,
I am working on profiling TPCH queries for Spark 2.0. I see lot of
temporary object creation (sometimes size as much as the data size) which
is justified for the kind of processing Spark does. But, from production
perspective, is there a guideline on how much memory should be allocated
for
