add http timeout for httpbroadcast

While pulling task bytecode from HttpBroadcast server, there's no timeout value set. This may cause spark executor code hang and other task in the same executor process wait for the lock. I have encountered the issue in my cluster. Here's the stacktrace I captured  : https://gist.github.com/haitaoyao/7655830

So add a time out value to ensure the task fail fast.

Custom Serializers for PySpark

This pull request adds support for custom serializers to PySpark.  For now, all Python-transformed (or parallelize()d RDDs) are serialized with the same serializer that's specified when creating SparkContext.

For now, PySpark includes `PickleSerDe` and `MarshalSerDe` classes for using Python's `pickle` and `marshal` serializers.  It's pretty easy to add support for other serializers, although I still need to add instructions on this.

A few notable changes:

- The Scala `PythonRDD` class no longer manipulates Pickled objects; data from `textFile` is written to Python as MUTF-8 strings.  The Python code performs the appropriate bookkeeping to track which deserializer should be used when reading an underlying JavaRDD.  This mechanism could also be used to support other data exchange formats, such as MsgPack.
- Several magic numbers were refactored into constants.
- Batching is implemented by wrapping / decorating an unbatched SerDe.

Log a warning if a task's serialized size is very big

As per Reynold's instructions, we now create a warning level log entry if a task's serialized size is too big. "Too big" is currently defined as 100kb. This warning message is generated at most once for each stage.

[SPARK-963] Fixed races in JobLoggerSuite

Improve docs for shuffle instrumentation

Add histogram functionality to DoubleRDDFunctions

This pull request add histogram functionality to the DoubleRDDFunctions.

OpenHashSet fixes

Incorporated ideas from pull request #200.
- Use Murmur Hash 3 finalization step to scramble the bits of HashCode
  instead of the simpler version in java.util.HashMap; the latter one
  had trouble with ranges of consecutive integers. Murmur Hash 3 is used
  by fastutil.
- Don't check keys for equality when re-inserting due to growing the
  table; the keys will already be unique.
- Remember the grow threshold instead of recomputing it on each insert

Also added unit tests for size estimation for specialized hash sets and maps.

Update tuning.md

Clarify when serializer is used based on recent user@ mailing list discussion.

Use the proper partition index in mapPartitionsWIthIndex

mapPartitionsWithIndex uses TaskContext.partitionId as the partition index. TaskContext.partitionId used to be identical to the partition index in a RDD. However, pull request #186 introduced a scenario (with partition pruning) that the two can be different. This pull request uses the right partition index in all mapPartitionsWithIndex related calls.

Also removed the extra MapPartitionsWIthContextRDD and put all the mapPartitions related functionality in MapPartitionsRDD.

Clarify when serializer is used based on recent user@ mailing list discussion.

For SPARK-527, Support spark-shell when running on YARN

sync to trunk and resubmit here

In current YARN mode approaching, the application is run in the Application Master as a user program thus the whole spark context is on remote.

This approaching won't support application that involve local interaction and need to be run on where it is launched.

So In this pull request I have a YarnClientClusterScheduler and backend added.

With this scheduler, the user application is launched locally,While the executor will be launched by YARN on remote nodes with a thin AM which only launch the executor and monitor the Driver Actor status, so that when client app is done, it can finish the YARN Application as well.

This enables spark-shell to run upon YARN.

This also enable other Spark applications to have the spark context to run locally with a master-url "yarn-client". Thus e.g. SparkPi could have the result output locally on console instead of output in the log of the remote machine where AM is running on.

Docs also updated to show how to use this yarn-client mode.

- Use Murmur Hash 3 finalization step to scramble the bits of HashCode
  instead of the simpler version in java.util.HashMap; the latter one
  had trouble with ranges of consecutive integers. Murmur Hash 3 is used
  by fastutil.

- Don't check keys for equality when re-inserting due to growing the
  table; the keys will already be unique

- Remember the grow threshold instead of recomputing it on each insert

 Fix Maven build for metrics-graphite

Add graphite sink for metrics

This adds a metrics sink for graphite.  The sink must
be configured with the host and port of a graphite node
and optionally may be configured with a prefix that will
be prepended to all metrics that are sent to graphite.

XORShift RNG with unit tests and benchmark

This patch was introduced to address SPARK-950 - the discussion below the ticket explains not only the rationale, but also the design and testing decisions: https://spark-project.atlassian.net/browse/SPARK-950

To run unit test, start SBT console and type:
compile
test-only org.apache.spark.util.XORShiftRandomSuite
To run benchmark, type:
project core
console
Once the Scala console starts, type:
org.apache.spark.util.XORShiftRandom.benchmark(100000000)
XORShiftRandom is also an object with a main method taking the
number of iterations as an argument, so you can also run it
from the command line.

Fix 'timeWriting' stat for shuffle files

Due to concurrent git branches, changes from shuffle file consolidation patch
caused the shuffle write timing patch to no longer actually measure the time,
since it requires time be measured after the stream has been closed.

Also changed the semantics of the index parameter in mapPartitionsWithIndex from the partition index of the output partition to the partition index in the current RDD.

AppendOnlyMap fixes

- Chose a more random reshuffling step for values returned by Object.hashCode to avoid some long chaining that was happening for consecutive integers (e.g. `sc.makeRDD(1 to 100000000, 100).map(t => (t, t)).reduceByKey(_ + _).count`)
- Some other small optimizations throughout (see commit comments)

- Don't check keys for equality when re-inserting due to growing the
  table; the keys will already be unique
- Remember the grow threshold instead of recomputing it on each insert

- Use Murmur Hash 3 finalization step to scramble the bits of HashCode
  instead of the simpler version in java.util.HashMap; the latter one
  had trouble with ranges of consecutive integers. Murmur Hash 3 is used
  by fastutil.
- Use Object.equals() instead of Scala's == to compare keys, because the
  latter does extra casts for numeric types (see the equals method in
  https://github.com/scala/scala/blob/master/src/library/scala/runtime/BoxesRunTime.java)

Support preservesPartitioning in RDD.zipPartitions

In `RDD.zipPartitions`, add support for a `preservesPartitioning` option (similar to `RDD.mapPartitions`) that reuses the first RDD's partitioner.

Due to concurrent git branches, changes from shuffle file consolidation patch
caused the shuffle write timing patch to no longer actually measure the time,
since it requires time be measured after the stream has been closed.

Fix Kryo Serializer buffer documentation inconsistency

The documentation here is inconsistent with the coded default and other documentation.

TimeTrackingOutputStream should pass on calls to close() and flush().

Without this fix you get a huge number of open files when running shuffles.

With this scheduler, the user application is launched locally,
While the executor will be launched by YARN on remote nodes.

This enables spark-shell to run upon YARN.

Without this fix you get a huge number of open shuffles after running
shuffles.

The documentation here is inconsistent with the coded default and other documentation.

PartitionPruningRDD is using index from parent

I was getting a ArrayIndexOutOfBoundsException exception after doing union on pruned RDD. The index it was using on the partition was the index in the original RDD not the new pruned RDD.

Cleanup to remove semicolons (;) from Scala code

-) The main reason for this PR is to remove semicolons from single statements of Scala code.
-) Remove unused imports as I see them
-) Fix ASF comment header from some of files (bad copy paste I suppose)