Incorporated Patrick's feedback comment on #211 and made maven build/dep-resolution atleast a bit faster.

Passed the sbt/sbt compile and test

Also remove unused imports as I found them along the way.
Remove return statements when returning value in the Scala code.

Passing compile and tests.

I've diff'd this patch against my own -- since they were both created
independently, this means that two sets of eyes have gone over all the
merge conflicts that were created, so I'm feeling significantly more
confident in the resulting PR.

@rxin has looked at the changes to the repl and is resoundingly
confident that they are correct.

Kafka uses an older version of jopt that causes bad conflicts with the version
used by spark-perf. It's not easy to remove this downstream because of the way
that spark-perf uses Spark (by including a spark assembly as an unmanaged jar).
This fixes the problem at its source by just never including it.

This patch adds an operator called repartition with more straightforward
semantics than the current `coalesce` operator. There are a few use cases
where this operator is useful:

1. If a user wants to increase the number of partitions in the RDD. This
is more common now with streaming. E.g. a user is ingesting data on one
node but they want to add more partitions to ensure parallelism of
subsequent operations across threads or the cluster.

Right now they have to call rdd.coalesce(numSplits, shuffle=true) - that's
super confusing.

2. If a user has input data where the number of partitions is not known. E.g.

> sc.textFile("some file").coalesce(50)....

This is both vague semantically (am I growing or shrinking this RDD) but also,
may not work correctly if the base RDD has fewer than 50 partitions.

The new operator forces shuffles every time, so it will always produce exactly
the number of new partitions. It also throws an exception rather than silently
not-working if a bad input is passed.

I am currently adding streaming tests (requires refactoring some of the test
suite to allow testing at partition granularity), so this is not ready for
merge yet. But feedback is welcome.

Fixed bug in Java transformWith, added more Java testcases for transform and transformWith, added missing variations of Java join and cogroup, updated various Scala and Java API docs.

Updated TransformDStream to allow n-ary DStream transform. Added transformWith, leftOuterJoin and rightOuterJoin operations to DStream for Scala and Java APIs. Also added n-ary union and n-ary transform operations to StreamingContext for Scala and Java APIs.

This is an unfortunately invasive change which converts all of our BlockId
strings into actual BlockId types. Here are some advantages of doing this now:

+ Type safety

+ Code clarity - it's now obvious what the key of a shuffle or rdd block is,
  for instance. Additionally, appearing in tuple/map type signatures is a big
  readability bonus. A Seq[(String, BlockStatus)] is not very clear.
  Further, we can now use more Scala features, like matching on BlockId types.

+ Explicit usage - we can now formally tell where various BlockIds are being used
  (without doing string searches); this makes updating current BlockIds a much
  clearer process, and compiler-supported.
  (I'm looking at you, shuffle file consolidation.)

+ It will only get harder to make this change as time goes on.

Since this touches a lot of files, it'd be best to either get this patch
in quickly or throw it on the ground to avoid too many secondary merge conflicts.

* RDD, *RDDFunctions -> org.apache.spark.rdd
* Utils, ClosureCleaner, SizeEstimator -> org.apache.spark.util
* JavaSerializer, KryoSerializer -> org.apache.spark.serializer