(WIP) SPARK-2045 Sort-based shuffle

This adds a new ShuffleManager based on sorting, as described in https://issues.apache.org/jira/browse/SPARK-2045. The bulk of the code is in an ExternalSorter class that is similar to ExternalAppendOnlyMap, but sorts key-value pairs by partition ID and can be used to create a single sorted file with a map task's output. (Longer-term I think this can take on the remaining functionality in ExternalAppendOnlyMap and replace it so we don't have code duplication.)

The main TODOs still left are enabling ExternalSorter to merge *across* spilled files, though this may be of limited utility (if the data is highly combinable this will happen early on), and adding more tests (e.g. a version of our shuffle suite that runs on this). In addition, this will need to be rebased on top of the size-tracking refactoring in #1165.

Despite this though, this seems to work pretty well (running successfully in cases where the hash shuffle would OOM, such as 1000 reduce tasks on executors with only 1G memory), and it seems to be comparable in speed or faster than hash-based shuffle (it will create much fewer files for the OS to keep track of). So I'm posting it to get some early feedback.

After these TODOs are done, I'd also like to enable ExternalSorter to sort data within each partition by a key as well, which will allow us to use it to implement external spilling in reduce tasks in `sortByKey`.
It appears that we'll either use the map or the buffer throughout the whole sorting process (rather than switch from one to another). In this case, would it make sense to put a `val usingMap/shouldCombine = aggregator.isDefined` out here, and then avoid passing it around elsewhere?