So I've seen in the documentation that (after the overhead memory is subtracted), the memory allocations of each executor are as follows (assume default settings):
60% for cache 40% for tasks to process data Reading about how Spark implements shuffling, I've also seen it say "20% of executor memory is utilized for shuffles" Does this 20% cut into the 40% for tasks to process data or the 60% for the data cache?
