The zip{Edge,Vertex}Partitions methods created doubly-nested closures
and passed them to zipPartitions. For some reason this caused an
AbstractMethodError when zipPartitions tried to invoke the closure. This
commit works around the problem by inlining these methods wherever they
are called, eliminating the doubly-nested closure.

Conflicts:
	README.md
	core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala
	core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala
	core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala
	pom.xml
	project/SparkBuild.scala
	repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala

fix make-distribution.sh show version: command not found

Set boolean param name for call to SparkHadoopMapReduceUtil.newTaskAttemptID

Set boolean param name for call to SparkHadoopMapReduceUtil.newTaskAttemptID to make it clear which param being set.

Add CDH Repository to Maven Build

At some point this was removed from the Maven build... so I'm adding it back. It's needed for the Hadoop2 tests we run on Jenkins and it's also included in the SBT build.

Remove calls to deprecated mapred's OutputCommitter.cleanupJob

Since Hadoop 1.0.4 the mapred OutputCommitter.commitJob should do cleanup job via call to OutputCommitter.cleanupJob,

Remove SparkHadoopWriter.cleanup since it is used only by PairRDDFunctions.

In fact the implementation of mapred OutputCommitter.commitJob looks like this:

  public void commitJob(JobContext jobContext) throws IOException {
    cleanupJob(jobContext);
  }

support distributing extra files to worker for yarn client mode

So that user doesn't need to package all dependency into one assemble jar as spark app jar

it clear which param being set.

the mapred OutputCommitter.commitJob should do cleanup job.

In fact the implementation of mapred OutputCommitter.commitJob looks like this:

  public void commitJob(JobContext jobContext) throws IOException {
    cleanupJob(jobContext);
  }

(The jobContext input argument is type of org.apache.hadoop.mapred.JobContext)

SPARK-1009 Updated MLlib docs to show how to use it in Python

In addition added detailed examples for regression, clustering and recommendation algorithms in a separate Scala section. Fixed a few minor issues with existing documentation.

Update README.md

The link does not work otherwise.

Refactored the streaming project to separate external libraries like Twitter, Kafka, Flume, etc.

At a high level, these are the following changes.

1. All the external code was put in `SPARK_HOME/external/` as separate SBT projects and Maven modules. Their artifact names are `spark-streaming-twitter`, `spark-streaming-kafka`, etc. Both SparkBuild.scala and pom.xml files have been updated. References to external libraries and repositories have been removed from the settings of root and streaming projects/modules.

2. To avail the external functionality (say, creating a Twitter stream), the developer has to `import org.apache.spark.streaming.twitter._` . For Scala API, the developer has to call `TwitterUtils.createStream(streamingContext, ...)`. For the Java API, the developer has to call `TwitterUtils.createStream(javaStreamingContext, ...)`.

3.  Each external project has its own scala and java unit tests. Note the unit tests of each external library use classes of the streaming unit tests (`TestSuiteBase`, `LocalJavaStreamingContext`, etc.). To enable this code sharing among test classes, `dependsOn(streaming % "compile->compile,test->test")` was used in the SparkBuild.scala . In the streaming/pom.xml, an additional `maven-jar-plugin` was necessary to capture this dependency (see comment inside the pom.xml for more information).

4. Jars of the external projects have been added to examples project but not to the assembly project.

5. In some files, imports have been rearrange to conform to the Spark coding guidelines.

The link does not work otherwise.

Get rid of `Either[ActorRef, ActorSelection]'

In this pull request, instead of returning an `Either[ActorRef, ActorSelection]`, `registerOrLookup` identifies the remote actor blockingly to obtain an `ActorRef`, or throws an exception if the remote actor doesn't exist or the lookup times out (configured by `spark.akka.lookupTimeout`).  This function is only called when an `SparkEnv` is constructed (instantiating driver or executor), so the blocking call is considered acceptable.  Executor side `ActorSelection`s/`ActorRef`s to driver side `MapOutputTrackerMasterActor` and `BlockManagerMasterActor` are affected by this pull request.

`ActorSelection` is dangerous and should be used with care.  It's only absolutely safe to send messages via an `ActorSelection` when the remote actor is stateless, so that actor incarnation is irrelevant.  But as pointed by @ScrapCodes in the comments below, executor exits immediately once the connection to the driver lost, `ActorSelection`s are not harmful in this scenario.  So this pull request is mostly a code style patch.

Added ‘-i’ command line option to Spark REPL

We had to create a new implementation of both scala.tools.nsc.CompilerCommand and scala.tools.nsc.Settings, because using scala.tools.nsc.GenericRunnerSettings would bring in other options (-howtorun, -save and -execute) which don’t make sense in Spark.
Any new Spark specific command line option could now be added to org.apache.spark.repl.SparkRunnerSettings class.

Since the behavior of loading a script from the command line should be the same as loading it using the “:load” command inside the shell, the script should be loaded when the SparkContext is available, that’s why we had to move the call to ‘loadfiles(settings)’ _after_ the call to postInitialization(). This still doesn’t work if ‘isAsync = true’.

Add ASF header to the new sbt script.

Add ASF header to the new sbt script.

Add way to limit default # of cores used by apps in standalone mode

Also documents the spark.deploy.spreadOut option, and fixes a config option that had a dash in its name.

Don't leave os.arch unset after BlockManagerSuite

Recent SparkConf changes meant that BlockManagerSuite was now leaving the os.arch System.property unset.  That's a problem for any subsequent tests that rely upon having a valid os.arch.  This is true for CompressionCodecSuite in the usual maven build test order, even though it isn't usually true for the sbt build.

SPARK-1012: DAGScheduler Exception Fix

Added a predict method to MatrixFactorizationModel to enable bulk prediction. This method takes and RDD[(Int, Int)] of users and products and return an RDD with a Rating element per each element in the input RDD.

Also added python bindings to the new bulk prediction methods to address SPARK-1011 issue.

This is ready to be merged now.

Add log4j exclusion rule to maven.

To make this work I had to rename the defaults file. Otherwise
maven's pattern matching rules included it when trying to match
other log4j.properties files.

I also fixed a bug in the existing maven build where two
<transformers> tags were present in assembly/pom.xml
such that one overwrote the other.

To make this work I had to rename the defaults file. Otherwise
maven's pattern matching rules included it when trying to match
other log4j.properties files.

I also fixed a bug in the existing maven build where two
<transformers> tags were present in assembly/pom.xml
such that one overwrote the other.

Also documents the spark.deploy.spreadOut option.

Mllib 16 bugfix

Bug fix: https://spark-project.atlassian.net/browse/MLLIB-16

Hi, I fixed the bug and added a test suite for `GradientDescent`. There are 2 checks in the test case. First, the final loss must be lower than the initial one. Second, the trend of loss sequence should be decreasing, i.e., at least 80% iterations have lower losses than their prior iterations.

Thanks!

add the comments about SPARK_WORKER_DIR

this env variable seems to be forgotten

in many cases we need to set this variable, e.g. in EC2, we have to move the large application log files from the EBS to the ephemeral storage

this env variable seems to be forgotten …