Expand the PySpark programming guide.

This version of the example crashes after the first iteration with
"OverflowError: math range error" because Python's math.exp()
behaves differently than Scala's; see SPARK-646.

Conflicts:
	docs/quick-start.md

Fix deletion of files in current working directory by clearFiles()

This fixes an issue where Spark could delete
original files in the current working directory
that were added to the job using addFile().

There was also the potential for addFile() to
overwrite local files, which is addressed by
changing Utils.fetchFile() to log a warning
instead of overwriting a file with new contents.

This is a short-term fix; a better long-term
solution would be to remove the dependence on
storing files in the current working directory,
since we can't change the cwd from Java.

- Bundle Py4J binaries, since it's hard to install
- Uses Spark's `run` script to launch the Py4J
  gateway, inheriting the settings in spark-env.sh

With these changes, (hopefully) nothing more than
running `sbt/sbt package` will be necessary to run
PySpark.

Add options to pyspark.SparkContext constructor.

Suggested by / based on code from @MLnick

Passing large volumes of data through Py4J seems
to be slow.  It appears to be faster to write the
data to the local filesystem and read it back from
Python.

lookup() needn't fail when there is no partitioner

Allow distinct() to be called without parentheses

Kryo2 update against Spark master

Added the ability in block manager to remove blocks.

SPARK-616: Logging dead workers in Web UI.

excessive debug messages.

Tweaked debian packaging to be a bit more in line with debian standards