(Continued from old repo, prior discussion at https://github.com/apache/incubator-spark/pull/615)

This patch cements our deprecation of the SPARK_MEM environment variable by replacing it with three more specialized variables:
SPARK_DAEMON_MEMORY, SPARK_EXECUTOR_MEMORY, and SPARK_DRIVER_MEMORY

The creation of the latter two variables means that we can safely set driver/job memory without accidentally setting the executor memory. Neither is public.

SPARK_EXECUTOR_MEMORY is only used by the Mesos scheduler (and set within SparkContext). The proper way of configuring executor memory is through the "spark.executor.memory" property.

SPARK_DRIVER_MEMORY is the new way of specifying the amount of memory run by jobs launched by spark-class, without possibly affecting executor memory.

Other memory considerations:
- The repl's memory can be set through the "--drivermem" command-line option, which really just sets SPARK_DRIVER_MEMORY.
- run-example doesn't use spark-class, so the only way to modify examples' memory is actually an unusual use of SPARK_JAVA_OPTS (which is normally overriden in all cases by spark-class).

This patch also fixes a lurking bug where spark-shell misused spark-class (the first argument is supposed to be the main class name, not java options), as well as a bug in the Windows spark-class2.cmd. I have not yet tested this patch on either Windows or Mesos, however.

Author: Aaron Davidson <aaron@databricks.com>

Closes #99 from aarondav/sparkmem and squashes the following commits:

9df4c68 [Aaron Davidson] SPARK-929: Fully deprecate usage of SPARK_MEM

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.