In experimental clusters we've observed that a 10 second timeout was insufficient,
despite having a low number of nodes and relatively small workload (16 nodes, <1.5 TB data).
This would cause an entire job to fail at the beginning of the reduce phase.
There is no particular reason for this value to be small as a timeout should only occur
in an exceptional situation.

Also centralized the reading of spark.akka.askTimeout to AkkaUtils (surely this can later
be cleaned up to use Typesafe).

Finally, deleted some lurking implicits. If anyone can think of a reason they should still
be there, please let me know.
Merge pull request #247 from aarondav/minor

Increase spark.akka.askTimeout default to 30 seconds

In experimental clusters we've observed that a 10 second timeout was insufficient, despite having a low number of nodes and relatively small workload (16 nodes, <1.5 TB data). This would cause an entire job to fail at the beginning of the reduce phase.
There is no particular reason for this value to be small as a timeout should only occur in an exceptional situation.

Also centralized the reading of spark.akka.askTimeout to AkkaUtils (surely this can later be cleaned up to use Typesafe).

Finally, deleted some lurking implicits. If anyone can think of a reason they should still be there, please let me know.
First cut at python mllib bindings.  Only LinearRegression is supported.
The rest of the Python side of those bindings.
[SPARK-959] Explicitly depend on org.eclipse.jetty.orbit jar

Without this, in some cases, Ivy attempts to download the wrong file
and fails, stopping the whole build. See bug for more details.

(This is probably also the beginning of the slow death of our
recently prettified dependencies. Form follow function.)
Minor changes.
Merge pull request #183 from aarondav/spark-959

[SPARK-959] Explicitly depend on org.eclipse.jetty.orbit jar

Without this, in some cases, Ivy attempts to download the wrong file and fails, stopping the whole build. See [bug](https://spark-project.atlassian.net/browse/SPARK-959) for more details.

Note that this may not be the best solution, as I do not understand the root cause of why this only happens for some people. However, it is reported to work.
Incorporate most of Josh's style suggestions.  I don't want to deal with the type and length checking errors until we've got at least one working stub that we're all happy with.
Add toString to Java RDD, and __repr__ to Python RDD
Merge pull request #278 from MLnick/java-python-tostring

Add toString to Java RDD, and __repr__ to Python RDD

Addresses [SPARK-992](https://spark-project.atlassian.net/browse/SPARK-992)
Merge branch 'scheduler-update' into filestream-fix

Conflicts:
	core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
	streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
	streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
	streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
	streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
Make collectPartitions take an array of partitions
Change the implementation to use runJob instead of PartitionPruningRDD.
Also update the unit tests and the python take implementation
to use the new interface.
Add comment explaining collectPartitions's use
Merge branch 'scheduler-update' into window-improvement

Conflicts:
	streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala
Merge pull request #276 from shivaram/collectPartition

Add collectPartition to JavaRDD interface.

This interface is useful for implementing `take` from other language frontends where the data is serialized. Also remove `takePartition` from PythonRDD and use `collectPartition` in rdd.py.

Thanks @concretevitamin for the original change and tests.
Clean up shuffle files once their metadata is gone

Previously, we would only clean the in-memory metadata for consolidated
shuffle files.

Additionally, fixes a bug where the Metadata Cleaner was ignoring type-
specific TTLs.
Extraordinarily minor code/comment cleanup
Remove firstApp from the standalone scheduler Master

As a lonely child with no one to care for it... we had to put it down.
Fix compiler warning in SparkZooKeeperSession
Merge pull request #272 from tmyklebu/master

Track and report task result serialisation time.

 - DirectTaskResult now has a ByteBuffer valueBytes instead of a T value.
 - DirectTaskResult now has a member function T value() that deserialises valueBytes.
 - Executor serialises value into a ByteBuffer and passes it to DTR's ctor.
 - Executor tracks the time taken to do so and puts it in a new field in TaskMetrics.
 - StagePage now reports serialisation time from TaskMetrics along with the other things it reported.