[SPARK-1991] Support custom storage levels for vertices and edges

This PR adds support for specifying custom storage levels for the vertices and edges of a graph. This enables GraphX to handle graphs larger than memory size by specifying MEMORY_AND_DISK and then repartitioning the graph to use many small partitions, each of which does fit in memory. Spark will then automatically load partitions from disk as needed.

The user specifies the desired vertex and edge storage levels when building the graph by passing them to the graph constructor. These are then stored in the `targetStorageLevel` attribute of the VertexRDD and EdgeRDD respectively. Whenever GraphX needs to cache a VertexRDD or EdgeRDD (because it plans to use it more than once, for example), it uses the specified target storage level. Also, when the user calls `Graph#cache()`, the vertices and edges are persisted using their target storage levels.

In order to facilitate propagating the target storage levels across VertexRDD and EdgeRDD operations, we remove raw calls to the constructors and instead introduce the `withPartitionsRDD` and `withTargetStorageLevel` methods.

I tested this change by running PageRank and triangle count on a severely memory-constrained cluster (1 executor with 300 MB of memory, and a 1 GB graph). Before this PR, these algorithms used to fail with OutOfMemoryErrors. With this PR, and using the DISK_ONLY storage level, they succeed.