Implementing SPARK-878 for PySpark: adding zip and egg files to context and passing it down to workers which add these to their sys.path

One unfortunate consequence of this fix is that we materialize any
collections that are given to us as generators, but this seems necessary
to get reasonable behavior on small collections. We could add a
batchSize parameter later to bypass auto-computation of batch size if
this becomes a problem (e.g. if users really want to parallelize big
generators nicely)

The problem was that the gateway was being initialized whenever the
pyspark.context module was loaded.  The fix uses lazy initialization
that occurs only when SparkContext instances are actually constructed.

I also made the gateway and jvm variables private.

This change results in ~3-4x performance improvement when running the
PySpark unit tests.

Fix minor documentation formatting issues.

This should avoid exceptions caused by existing
files with different contents.

I also removed some unused code.

Add options to pyspark.SparkContext constructor.

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.

Objects serialized with JSON can be compared for equality, but JSON can be slow
to serialize and only supports a limited range of data types.