Also replaced SparkConf.getOrElse with just a "get" that takes a default
value, and added getInt, getLong, etc to make code that uses this
simpler later on.

Conflicts:
	core/src/main/scala/org/apache/spark/SparkContext.scala
	core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
	core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala

SPARK-1008: Logging improvments

1. Adds a default log4j file that gets loaded if users haven't specified a log4j file.
2. Isolates use of the tools assembly jar. I found this produced SLF4J warnings
   after building with SBT (and I've seen similar warnings on the mailing list).

Conflicts:
	streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala

Conflicts:
	project/SparkBuild.scala

restore core/pom.xml file modification

Approximate distinct count

Added countApproxDistinct() to RDD and countApproxDistinctByKey() to PairRDDFunctions to approximately count distinct number of elements and distinct number of values per key, respectively. Both functions use HyperLogLog from stream-lib for counting. Both functions take a parameter that controls the trade-off between accuracy and memory consumption. Also added Scala docs and test suites for both methods.

Conflicts:
	core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
	streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
	streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala

upgrade Netty from 4.0.0.Beta2 to 4.0.13.Final

the changes are listed at https://github.com/netty/netty/wiki/New-and-noteworthy

Bug fixes for file input stream and checkpointing

- Fixed bugs in the file input stream that led the stream to fail due to transient HDFS errors (listing files when a background thread it deleting fails caused errors, etc.)
- Updated Spark's CheckpointRDD and Streaming's CheckpointWriter to use SparkContext.hadoopConfiguration, to allow checkpoints to be written to any HDFS compatible store requiring special configuration.
- Changed the API of SparkContext.setCheckpointDir() - eliminated the unnecessary 'useExisting' parameter. Now SparkContext will always create a unique subdirectory within the user specified checkpoint directory. This is to ensure that previous checkpoint files are not accidentally overwritten.
- Fixed bug where setting checkpoint directory as a relative local path caused the checkpointing to fail.

Changed naming of StageCompleted event to be consistent

The rest of the SparkListener events are named with "SparkListener"
as the prefix of the name; this commit renames the StageCompleted
event to SparkListenerStageCompleted for consistency.

1. Adds a default log4j file that gets loaded if users haven't specified a log4j file.
2. Isolates use of the tools assembly jar. I found this produced SLF4J warnings
   after building with SBT (and I've seen similar warnings on the mailing list).

tests so we don't get the test spark.conf on the classpath.

Typesafe Config caches system properties the first time it's invoked
by default, ignoring later changes unless you do something special

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.