Memory management in Spark is currently broken down into two disjoint regions: one for execution and one for storage. The sizes of these regions are statically configured and fixed for the duration of the application.
There are several limitations to this approach. It requires user expertise to avoid unnecessary spilling, and there are no sensible defaults that will work for all workloads. As a Spark user, I want Spark to manage the memory more intelligently so I do not need to worry about how to statically partition the execution (shuffle) memory fraction and cache memory fraction. More importantly, applications that do not use caching use only a small fraction of the heap space, resulting in suboptimal performance.
Instead, we should unify these two regions and let one borrow from another if possible.