(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:
- [x] enabling ExternalSorter to merge across spilled files
  - [x] with an Ordering
  - [x] without an Ordering, using the keys' hash codes
- [x] adding more tests (e.g. a version of our shuffle suite that runs on this)
- [ ] rebasing on top of the size-tracking refactoring in #1165 when that is merged
- [x] disabling spilling if spark.shuffle.spill is set to false

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`.
Or maybe, to improve performance, we should do as mridulm suggested, set indexInPartition to 0, then in additition, do partition skip before actually read k,c object. thus, the caller will get correct partitionID for hasnext judgements for the last item. And then, this if judgement could be moved in to the while loop, and set the finish flag, and do close stream in the begining of next readNextItem iteration? so that this if judgement won't need to be done upon each item but each partition?