fix paths and change spark to use APP_MEM as application driver memory instead of SPARK_MEM, user should add application jars to SPARK_CLASSPATH

Signed-off-by: shane-huang <shengsheng.huang@intel.com>

Signed-off-by: shane-huang <shengsheng.huang@intel.com>

This commit makes Spark invocation saner by using an assembly JAR to
find all of Spark's dependencies instead of adding all the JARs in
lib_managed. It also packages the examples into an assembly and uses
that as SPARK_EXAMPLES_JAR. Finally, it replaces the old "run" script
with two better-named scripts: "run-examples" for examples, and
"spark-class" for Spark internal classes (e.g. REPL, master, etc). This
is also designed to minimize the confusion people have in trying to use
"run" to run their own classes; it's not meant to do that, but now at
least if they look at it, they can modify run-examples to do a decent
job for them.

As part of this, Bagel's examples are also now properly moved to the
examples package instead of bagel.

This addresses SPARK-885, a usability issue where PySpark's
Java gateway process would be killed if the user hit ctrl-c.

Note that SIGINT still won't cancel the running s

This fix is based on http://stackoverflow.com/questions/5045771

- 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.

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.