Allow users to pass broadcasted Configurations and cache InputFormats across Hadoop file reads.

Note: originally from https://github.com/mesos/spark/pull/942

Currently motivated by Shark queries on Hive-partitioned tables, where there's a JobConf broadcast for every Hive-partition (i.e., every subdirectory read). The only thing different about those JobConfs is the input path - the Hadoop Configuration that the JobConfs are constructed from remain the same.
This PR only modifies the old Hadoop API RDDs, but similar additions to the new API might reduce computation latencies a little bit for high-frequency FileInputDStreams (which only uses the new API right now).

As a small bonus, added InputFormats caching, to avoid reflection calls for every RDD#compute().

Few other notes:

Added a general soft-reference hashmap in SparkHadoopUtil because I wanted to avoid adding another class to SparkEnv.
SparkContext default hadoopConfiguration isn't cached. There's no equals() method for Configuration, so there isn't a good way to determine when configuration properties have changed.

Bumping EC2 default version in master to .

This change was already made on . This PR ports the change up to master.

SPARK-920/921 - JSON endpoint updates

920 - Removal of duplicate scheme part of Spark URI, it was appearing as spark://spark//host:port in the JSON field.

JSON now delivered as:
url:spark://127.0.0.1:7077

921 - Adding the URL of the Main Application UI will allow custom interfaces (that use the JSON output) to redirect from the standalone UI.

Fixing SPARK-602: PythonPartitioner

Currently PythonPartitioner determines partition ID by hashing a
byte-array representation of PySpark's key. This PR lets
PythonPartitioner use the actual partition ID, which is required e.g.
for sorting via PySpark.

Currently PythonPartitioner determines partition ID by hashing a
byte-array representation of PySpark's key. This PR lets
PythonPartitioner use the actual partition ID, which is required e.g.
for sorting via PySpark.

fixed a wildcard bug in make-distribution.sh; ask sbt to check local
maven repo in project/SparkBuild.scala

(1) fixed a wildcard bug in make-distribution.sh:
with the wildcard * in quotes, this cp command failed. it worked after
moving the wildcard out quotes.

(2) ask sbt to check local maven repo in SparkBuild.scala:
To build Spark (0.9.0-SNAPSHOT) with the HEAD of mesos (0.15.0), I must
do "make maven-install" under mesos/build, which publishes the java .jar
file under ~/.m2. However, when building Spark (after pointing mesos to
version 0.15.0), sbt uses ivy which by default only checks ~/.ivy2. This
change is to tell sbt to also check ~/.m2.

Update README: updated the link

Allow users to set the application name for Spark on Yarn

Send Task results through the block manager when larger than Akka frame size (fixes SPARK-669).

This change requires adding an extra failure mode: tasks can complete
successfully, but the result gets lost or flushed from the block manager
before it's been fetched.

This change also moves the deserialization of tasks into a separate thread, so it's no longer part of the DAG scheduler's tight loop. This should improve scheduler throughput, particularly when tasks are sending back large results.

Thanks Josh for writing the original version of this patch!

This is duplicated from the mesos/spark repo: https://github.com/mesos/spark/pull/835

Conflicts:
	core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterScheduler.scala
	core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManager.scala
	core/src/main/scala/org/apache/spark/scheduler/local/LocalTaskSetManager.scala

Remove -optimize flag

Bug fix in master build

Improved organization of scheduling packages.

This commit does not change any code -- only file organization.
Please let me know if there was some masterminded strategy behind
the existing organization that I failed to understand!

There are two components of this change:
(1) Moving files out of the cluster package, and down
a level to the scheduling package. These files are all used by
the local scheduler in addition to the cluster scheduler(s), so
should not be in the cluster package. As a result of this change,
none of the files in the local package reference files in the
cluster package.

(2) Moving the mesos package to within the cluster package.
The mesos scheduling code is for a cluster, and represents a
specific case of cluster scheduling (the Mesos-related classes
often subclass cluster scheduling classes). Thus, the most logical
place for it seems to be within the cluster package.

The one thing about the scheduling code that seems a little funny to me
is the naming of the SchedulerBackends.  The StandaloneSchedulerBackend
is not just for Standalone mode, but instead is used by Mesos coarse grained
mode and Yarn, and the backend that *is* just for Standalone mode is instead called SparkDeploySchedulerBackend. I didn't change this because I wasn't sure if there
was a reason for this naming that I'm just not aware of.

EC2 SSH improvements

Add mapPartitionsWithIndex

some minor fixes to MemoryStore

This is a repeat of #5, moved to its own branch in my repo.

This makes all updates to   on ; it skips on synchronizing the reads where it can get away with it.

Smarter take/limit implementation.

This commit does not change any code -- only file organization.

There are two components of this change:
(1) Moving files out of the cluster package, and down
a level to the scheduling package. These files are all used by
the local scheduler in addition to the cluster scheduler(s), so
should not be in the cluster package. As a result of this change,
none of the files in the local package reference files in the
cluster package.

(2) Moving the mesos package to within the cluster package.
The mesos scheduling code is for a cluster, and represents a
specific case of cluster scheduling (the Mesos-related classes
often subclass cluster scheduling classes). Thus, the most logical
place for it is within the cluster package.