Also documents the spark.deploy.spreadOut option.

Suggested small changes to Java code for slightly more standard style, encapsulation and in some cases performance

Sorry if this is too abrupt or not a welcome set of changes, but thought I'd see if I could contribute a little. I'm a Java developer and just getting seriously into Spark. So I thought I'd suggest a number of small changes to the couple Java parts of the code to make it a little tighter, more standard and even a bit faster.

Feel free to take all, some or none of this. Happy to explain any of it.

spark -> org.apache.spark

Changed package name spark to org.apache.spark which was missing in some of the files

Conf improvements

There are two new features.

1. Allow users to set arbitrary akka configurations via spark conf.

2. Allow configuration to be printed in logs for diagnosis.

Add a script to download sbt if not present on the system

As per the discussion on the dev mailing list this script will use the system sbt if present or otherwise attempt to install the sbt launcher. The fall back error message in the event it fails instructs the user to install sbt. While the URLs it fetches from aren't controlled by the spark project directly, they are stable and the current authoritative sources.

Update stop-slaves.sh

The most recently version has changed the directory structure, but this script "sbin/stop-all.sh" doesn't change with it accordingly. This mistake makes "sbin/stop-all.sh" can't stop the slave node.

The most recently version has changed the directory structure, but this script "sbin/stop-all.sh" doesn't change with it accordingly. This mistake makes "sbin/stop-all.sh" can't stop the slave node.

Fix test breaking downstream builds

This wasn't detected in the pull-request-builder because it manually sets SPARK_HOME. I'm going to change that (it should't do this) to make it like the other builds.

Made java options to be applied during tests so that they become self explanatory.

SPARK-1005 Ning upgrade

Clarify spark.cores.max in docs

It controls the count of cores across the cluster, not on a per-machine basis.

Change protobuf version for yarn alpha back to 2.4.1

The maven build for yarn-alpha uses the wrong protobuf version and hence the generated assembly jar doesn't work with Hadoop 0.23.  Removing the setting for the yarn-alpha profile since the default protobuf version is 2.4.1 at the top of the pom file.

Fix handling of empty SPARK_EXAMPLES_JAR

Currently if SPARK_EXAMPLES_JAR is left unset you get a null pointer exception when running the examples (atleast on spark on yarn).  The null now gets turned into a string of "null" when its put into the SparkConf so addJar no longer properly ignores it. This fixes that so that it can be left unset.

It controls the count of cores across the cluster, not on a per-machine basis.

Quiet ERROR-level Akka Logs

This fixes an issue I've seen where akka logs a bunch of things at ERROR level when connecting to a standalone cluster, even in the normal case. I noticed that even when lifecycle logging was disabled, the netty code inside of akka still logged away via akka's EndpointWriter class. There are also some other log streams that I think are new in akka 2.2.1 that I've disabled.

Finally, I added some better logging to the standalone client. This makes it more clear when a connection failure occurs what is going on. Previously it never explicitly said if a connection attempt had failed.

The commit messages here have some more detail.

Removing SPARK_EXAMPLES_JAR in the code

This re-writes all of the examples to use the `SparkContext.jarOfClass` mechanism for loading the examples jar. This necessary for environments like YARN and the Standalone mode where example programs will be submit from inside the cluster rather than at the client using `./spark-example`.

This still leaves SPARK_EXAMPLES_JAR in place in the shell scripts for setting up the classpath if `./spark-example` is run.

Fall back to zero-arg constructor for Serializer initialization if there is no constructor that accepts SparkConf.

This maintains backward compatibility with older serializers implemented by users.

Fall back to zero-arg constructor for Serializer initialization if there is no constructor that accepts SparkConf.
This maintains backward compatibility with older serializers implemented by users.

Without these it's a bit less clear what's going on for the user.

One thing I realize when doing this is that akka itself actually retries
the initial association. So the retry we currently have is redundant with
akka's.

I noticed when connecting to a standalone cluster Spark gives a bunch
of Akka ERROR logs that make it seem like something is failing.

This patch does two things:

1. Akka dead letter logging is turned on/off according to the existing
   lifecycle spark property.
2. We explicitly silence akka's EndpointWriter log in log4j. This is necessary
   because for some reason that log doesn't pick up on the lifecycle
   logging settings. After a few hours of debugging this was the only solution
   I found that worked.