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.

Fix target JVM version in scala build

SPARK-660: Add StorageLevel support in Python

Provide docs to describe running on CDH/HDP cluster.

Adding Apache license to two files

0.8 Doc changes for make-distribution.sh

Fixed the bug that ResultTask was not properly deserializing outputId.

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

YARN build fixes

Docs describing Spark monitoring and instrumentation

This doc consolidates information relevant to CDH/HDP users in a single place.

SPARK-821: Don't cache results when action run locally on driver

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

[SPARK-864]DAGScheduler Exception if we delete Worker and StandaloneExecutorBackend then add Worker

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.

SPARK-884: Add unit test to validate Spark JSON output

Updating assembly README to reflect recent changes in the build.