listFiles() could return null if the I/O fails, and this currently results in an ugly NPE which is hard to diagnose.

StandaloneSchedulerBackend instead of the smaller IDs used within Spark
(that lack the application name).

This was reported by ClearStory in
https://github.com/clearstorydata/spark/pull/9.

Also fixed some messages that said slave instead of executor.

Include the useful tip that if shuffle=true, coalesce can actually
increase the number of partitions.

This makes coalesce more like a generic `RDD.repartition` operation.

(Ideally this `RDD.repartition` could automatically choose either a coalesce or
a shuffle if numPartitions was either less than or greater than, respectively,
the current number of partitions.)

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"

The sc.StorageLevel -> StorageLevel pathway is a bit janky, but otherwise
the shell would have to call a private method of SparkContext. Having
StorageLevel available in sc also doesn't seem like the end of the world.
There may be a better solution, though.

As for creating the StorageLevel object itself, this seems to be the best
way in Python 2 for creating singleton, enum-like objects:
http://stackoverflow.com/questions/36932/how-can-i-represent-an-enum-in-python

It uses reflection... I am not proud of that fact, but it at least ensures
compatibility (sans refactoring of the StorageLevel stuff).

Caching the results of local actions (e.g., rdd.first()) causes the driver to
store entire partitions in its own memory, which may be highly constrained.
This patch simply makes the CacheManager avoid caching the result of all locally-run computations.

This unit test simply validates that the outputs of
the JsonProtocol methods are syntactically valid JSON.