Conflicts:
	core/src/main/scala/org/apache/spark/SparkContext.scala
	core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
	core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
	core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManager.scala
	core/src/main/scala/org/apache/spark/scheduler/local/LocalScheduler.scala
	core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala
	core/src/test/scala/org/apache/spark/scheduler/TaskResultGetterSuite.scala
	core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
	new-yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
	streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
	streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
	streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
	streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
	streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
	streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
	streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
	streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala

The test in context.py created two different instances of the
SparkContext class by copying "globals", so that some tests can have a
global "sc" object and others can try initializing their own contexts.
This led to two JVM gateways being created since SparkConf also looked
at pyspark.context.SparkContext to get the JVM.

sometimes be set that way (undoes a change in previous commit)

- Got rid of global SparkContext.globalConf
- Pass SparkConf to serializers and compression codecs
- Made SparkConf public instead of private[spark]
- Improved API of SparkContext and SparkConf
- Switched executor environment vars to be passed through SparkConf
- Fixed some places that were still using system properties
- Fixed some tests, though others are still failing

This still fails several tests in core, repl and streaming, likely due
to properties not being set or cleared correctly (some of the tests run
fine in isolation).

Removed unused OtherFailure TaskEndReason.

The OtherFailure TaskEndReason was added by @mateiz 3 years ago in this commit: https://github.com/apache/incubator-spark/commit/24a1e7f8380bfd8d4fbdda688482a451bd6ea215

Unless I am missing something, it doesn't seem to have been used then, and is not used now, so seems safe for deletion.

Remove unused hasPendingTasks methods

Fixed >100char lines in DAGScheduler.scala

There's no changed functionality here -- only line spacing and one grammatical fix in a comment.

Minor: Decrease margin of left side of Log page

Before
![before](https://f.cloud.github.com/assets/1400247/1812647/1a4be53e-6e87-11e3-9d5b-f851274be0e9.png)

After
![after](https://f.cloud.github.com/assets/1400247/1812648/1ca1ea2c-6e87-11e3-946c-31be9258f450.png)

It's a start anyway...

SPARK-1007: spark-class2.cmd should change SCALA_VERSION to be 2.10

Reported by Qiuzhuang Lian

Avoid a lump of coal (NPE) in JobProgressListener's stocking.

Renamed ClusterScheduler to TaskSchedulerImpl for yarn and new-yarn package

Python bindings for mllib

This pull request contains Python bindings for the regression, clustering, classification, and recommendation tools in mllib.

For each 'train' frontend exposed, there is a Scala stub in PythonMLLibAPI.scala and a Python stub in mllib.py.  The Python stub serialises the input RDD and any vector/matrix arguments into a mutually-understood format and calls the Scala stub.  The Scala stub deserialises the RDD and the vector/matrix arguments, calls the appropriate 'train' function, serialises the resulting model, and returns the serialised model.

ALSModel is slightly different since a MatrixFactorizationModel has RDDs inside.  The Scala stub returns a handle to a Scala MatrixFactorizationModel; prediction is done by calling the Scala predict method.

I have tested these bindings on an x86_64 machine running Linux.  There is a risk that these bindings may fail on some choose-your-own-endian platform if Python's endian differs from java.nio.ByteBuffer's idea of the native byte order.

Typo: avaiable -> available

Fixed job name in the java streaming example.

Deduplicate Local and Cluster schedulers.

The code in LocalScheduler/LocalTaskSetManager was nearly identical
to the code in ClusterScheduler/ClusterTaskSetManager. The redundancy
made making updating the schedulers unnecessarily painful and error-
prone. This commit combines the two into a single TaskScheduler/
TaskSetManager.

Unfortunately the diff makes this change look much more invasive than it is -- TaskScheduler.scala is only superficially changed (names updated, overrides removed) from the old ClusterScheduler.scala, and the same with
TaskSetManager.scala.

Thanks @rxin for suggesting this change!

Clean up shuffle files once their metadata is gone

Previously, we would only clean the in-memory metadata for consolidated shuffle files.

Additionally, fixes a bug where the Metadata Cleaner was ignoring type-specific TTLs.