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.

Clarifies that this governs compression of internal data, not input
data or output data.

Updates to the documentation and changing some logError()s to logWarning()s.

Also fix a couple HTML/Markdown issues in other files.

the classpaths

Support distributed cache files and archives on spark on yarn and attempt to cleanup the staging directory on exit

details on monitoring

Also changed uses of "job" terminology to "application" when they
referred to an entire Spark program, to avoid confusion.

- Add job scheduling docs
- Rename some fair scheduler properties
- Organize intro page better
- Link to Apache wiki for "contributing to Spark"