Conflicts:
	streaming/src/test/java/spark/streaming/JavaAPISuite.java

Conflicts:
	core/src/main/scala/spark/rdd/CheckpointRDD.scala
	streaming/src/main/scala/spark/streaming/dstream/ReducedWindowedDStream.scala

A bug fix to AkkaWordCount example.

Don't call System.exit over uncaught exceptions from shutdown hooks

Fixes a broken link in documentation to issue tracker

Added a method to create PartitionPruningRDD.

STREAMING-51: Add RDD type as a type parameter in JavaDStreamLike Edit (streaming/ version) 

This commit uses the RDD type in `slice`, making it available to both normal
and pair RDD's in java. It also updates the signature for `slice` to match
changes in the Scala API.

This is an improved implementation of the `transform` operator in Java.
The main difference is that this allows all four possible types of
transform functions

1. JavaRDD -> JavaRDD
2. JavaRDD -> JavaPairRDD
3. JavaPairRDD -> JavaPairRDD
4. JavaPairRDD -> JavaRDD

whereas previously only (1) and (3) were possible.

Conflicts:

	streaming/src/test/java/spark/streaming/JavaAPISuite.java

Major modifications to fix driver fault-tolerance with file input stream

Conflicts:
	docs/plugin-custom-receiver.md
	streaming/src/main/scala/spark/streaming/StreamingContext.scala
	streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala
	streaming/src/main/scala/spark/streaming/dstream/PluggableInputDStream.scala
	streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala
	streaming/src/test/scala/spark/streaming/InputStreamsSuite.scala

Ganglia port change

Changed networkStream to socketStream and pluggableNetworkStream to become networkStream as a way to create streams from arbitrary network receiver.

Conflicts:
	streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala
	streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala

Added checkpointing and fault-tolerance semantics to the programming guide. Fixed default checkpoint interval to being a multiple of slide duration. Fixed visibility of some classes and objects to clean up docs.

recovering from 1st failure
- Made the scheduler to checkpoint after clearing old metadata which
  ensures that a new checkpoint is written as soon as at least one batch
  gets computed  while recovering from a failure. This ensures that if
  there is a 2nd failure while recovering from 1st failure, the system
  start 2nd recovery from a newer checkpoint.
- Modified Checkpoint writer to write checkpoint in a different thread.
- Added a check to make sure that compute for InputDStreams gets called
  only for strictly increasing times.
- Changed implementation of slice to call getOrCompute on parent DStream
  in time-increasing order.
- Added testcase to test slice.
- Fixed testGroupByKeyAndWindow testcase in JavaAPISuite to verify
  results with expected output in an order-independent manner.

Switch spark_ec2.py to use the new spark-ec2 scripts. 

Move ParallelCollection into spark.rdd package.

Make CoGroupedRDDs explicitly have the same key type.