[SPARK-1777] Prevent OOMs from single partitions

**Problem.** When caching, we currently unroll the entire RDD partition before making sure we have enough free memory. This is a common cause for OOMs especially when (1) the BlockManager has little free space left in memory, and (2) the partition is large.

**Solution.** We maintain a global memory pool of `M` bytes shared across all threads, similar to the way we currently manage memory for shuffle aggregation. Then, while we unroll each partition, periodically check if there is enough space to continue. If not, drop enough RDD blocks to ensure we have at least `M` bytes to work with, then try again. If we still don't have enough space to unroll the partition, give up and drop the block to disk directly if applicable.

**New configurations.**
- `spark.storage.unrollFraction` - the value of `M` as a fraction of the storage memory. (default: 0.2)
- `spark.storage.safetyFraction` - a margin of safety in case size estimation is slightly off. This is the equivalent of the existing `spark.shuffle.safetyFraction`. (default 0.9)

For more detail, see the [design document](https://issues.apache.org/jira/secure/attachment/12651793/spark-1777-design-doc.pdf). Tests pending for performance and memory usage patterns.

@liyezhang556520 Thanks for your detailed comments.

1) Your analysis is correct. If we return an array, we still hold onto the space occupied for unrolling, it's just that we are using this space to additionally store the return values. In retrospect it makes little sense to release memory here.

2) If we return an iterator, we cannot actually release the occupied memory here. Note that the iterator that we return depends on the buffer we used for unrolling, so we can't just release it. Unfortunately I don't think there is an alternative here because the original iterator passed in as an argument is already partially exhausted, so the only way to retrieve the traversed items is through the buffer we temporarily stored them in.