SPARK-3962 Marked scope as provided for external projects.

Somehow maven shade plugin is set in infinite loop of creating effective pom.

Author: Prashant Sharma <prashant.s@imaginea.com>
Author: Prashant Sharma <scrapcodes@gmail.com>

Closes #2959 from ScrapCodes/SPARK-3962/scope-provided and squashes the following commits:

994d1d3 [Prashant Sharma] Fixed failing flume tests
270b4fb [Prashant Sharma] Removed most of the unused code.
bb3bbfd [Prashant Sharma] SPARK-3962 Marked scope as provided for external.
[DOC][PySpark][Streaming] Fix docstring for sphinx

This commit should be merged for 1.2 release.
cc tdas

Author: Ken Takagiwa <ugw.gi.world@gmail.com>

Closes #3311 from giwa/patch-3 and squashes the following commits:

ab474a8 [Ken Takagiwa] [DOC][PySpark][Streaming] Fix docstring for sphinx
[SPARK-4429][BUILD] Build for Scala 2.11 using sbt fails.

I tried to build for Scala 2.11 using sbt with the following command:

```
$ sbt/sbt -Dscala-2.11 assembly
```

but it ends with the following error messages:

```
[error] (streaming-kafka/*:update) sbt.ResolveException: unresolved dependency: org.apache.kafka#kafka_2.11;0.8.0: not found
[error] (catalyst/*:update) sbt.ResolveException: unresolved dependency: org.scalamacros#quasiquotes_2.11;2.0.1: not found
```

The reason is:
If system property `-Dscala-2.11` (without value) was set, `SparkBuild.scala` adds `scala-2.11` profile, but also `sbt-pom-reader` activates `scala-2.10` profile instead of `scala-2.11` profile because the activator `PropertyProfileActivator` used by `sbt-pom-reader` internally checks if the property value is empty or not.

The value is set to non-empty value, then no need to add profiles in `SparkBuild.scala` because `sbt-pom-reader` can handle as expected.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #3342 from ueshin/issues/SPARK-4429 and squashes the following commits:

14d86e8 [Takuya UESHIN] Add a comment.
4eef52b [Takuya UESHIN] Remove unneeded condition.
ce98d0f [Takuya UESHIN] Set non-empty value to system property "scala-2.11" if the property exists instead of adding profile.
[SPARK-4384] [PySpark] improve sort spilling

If there some big broadcasts (or other object) in Python worker, the free memory could be used for sorting will be too small, then it will keep spilling small files into disks, finally failed with too many open files.

This PR try to delay the spilling until the used memory goes over limit and start to increase since last spilling, it will increase the size of spilling files, improve the stability and performance in this cases. (We also do this in ExternalAggregator).

Author: Davies Liu <davies@databricks.com>

Closes #3252 from davies/sort and squashes the following commits:

711fb6c [Davies Liu] improve sort spilling
[SPARK-4294][Streaming] UnionDStream stream should express the requirements in the same way as TransformedDStream

In class TransformedDStream:
```scala
require(parents.length > 0, "List of DStreams to transform is empty")
require(parents.map(.ssc).distinct.size == 1, "Some of the DStreams have different contexts")
require(parents.map(.slideDuration).distinct.size == 1,
"Some of the DStreams have different slide durations")
```

In class UnionDStream:
```scala
if (parents.length == 0)
{ throw new IllegalArgumentException("Empty array of parents") }
if (parents.map(.ssc).distinct.size > 1)
{ throw new IllegalArgumentException("Array of parents have different StreamingContexts") }
if (parents.map(.slideDuration).distinct.size > 1)
{ throw new IllegalArgumentException("Array of parents have different slide times") }
```

The function is the same, but the realization is not. I think they shoule be the same.

Author: Yadong Qi <qiyadong2010@gmail.com>

Closes #3152 from watermen/bug-fix1 and squashes the following commits:

ed66db6 [Yadong Qi] Change transform to union
b6b3b8b [Yadong Qi] The same function should have the same realization.
[SPARK-4495] Fix memory leak in JobProgressListener

This commit fixes a memory leak in JobProgressListener that I introduced in SPARK-2321 and adds a testing framework to ensure that it’s very difficult to inadvertently introduce new memory leaks.

This solution might be overkill, but the main idea is to partition JobProgressListener's state into three buckets: collections that should be empty once Spark is idle, collections that must obey some hard size limit, and collections that have a soft size limit (they can grow arbitrarily large when Spark is active but must shrink to fit within some bound after Spark becomes idle).

Based on this, we can write fairly generic tests that run workloads that submit more than `spark.ui.retainedStages` stages and `spark.ui.retainedJobs` jobs then check that these various collections' sizes obey their contracts.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #3372 from JoshRosen/SPARK-4495 and squashes the following commits:

c73fab5 [Josh Rosen] "data structures" -> collections
be72e81 [Josh Rosen] [SPARK-4495] Fix memory leaks in JobProgressListener
Updating GraphX programming guide and documentation

This pull request revises the programming guide to reflect changes in the GraphX API as well as the deprecated mapReduceTriplets operator.

Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>

Closes #3359 from jegonzal/GraphXProgrammingGuide and squashes the following commits:

4421964 [Joseph E. Gonzalez] updating documentation for graphx
[SPARK-4478] Keep totalRegisteredExecutors up-to-date

This rebases PR 3368.

This commit fixes totalRegisteredExecutors update [SPARK-4478], so that we can correctly keep track of number of registered executors.

Author: Akshat Aranya <aaranya@quantcast.com>

Closes #3373 from coolfrood/topic/SPARK-4478 and squashes the following commits:

8a4d1e4 [Akshat Aranya] Added comment
150ae93 [Akshat Aranya] [SPARK-4478] Keep totalRegisteredExecutors up-to-date
[Spark-4484] Treat maxResultSize as unlimited when set to 0; improve error message

The check for maxResultSize > 0 is missing, results in failures. Also, error message needs to be improved so the developers know that there is a new parameter to be configured

Author: Nishkam Ravi <nravi@cloudera.com>
Author: nravi <nravi@c1704.halxg.cloudera.com>
Author: nishkamravi2 <nishkamravi@gmail.com>

Closes #3360 from nishkamravi2/master_nravi and squashes the following commits:

5c9a4cb [nishkamravi2] Update TaskSetManagerSuite.scala
535295a [nishkamravi2] Update TaskSetManager.scala
3e1b616 [Nishkam Ravi] Modify test for maxResultSize
9f6583e [Nishkam Ravi] Changes to maxResultSize code (improve error message and add condition to check if maxResultSize > 0)
5f8f9ed [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi
636a9ff [nishkamravi2] Update YarnAllocator.scala
8f76c8b [Nishkam Ravi] Doc change for yarn memory overhead
35daa64 [Nishkam Ravi] Slight change in the doc for yarn memory overhead
5ac2ec1 [Nishkam Ravi] Remove out
dac1047 [Nishkam Ravi] Additional documentation for yarn memory overhead issue
42c2c3d [Nishkam Ravi] Additional changes for yarn memory overhead issue
362da5e [Nishkam Ravi] Additional changes for yarn memory overhead
c726bd9 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi
f00fa31 [Nishkam Ravi] Improving logging for AM memoryOverhead
1cf2d1e [nishkamravi2] Update YarnAllocator.scala
ebcde10 [Nishkam Ravi] Modify default YARN memory_overhead-- from an additive constant to a multiplier (redone to resolve merge conflicts)
2e69f11 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi
efd688a [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark
2b630f9 [nravi] Accept memory input as "30g", "512M" instead of an int value, to be consistent with rest of Spark
3bf8fad [nravi] Merge branch 'master' of https://github.com/apache/spark
5423a03 [nravi] Merge branch 'master' of https://github.com/apache/spark
eb663ca [nravi] Merge branch 'master' of https://github.com/apache/spark
df2aeb1 [nravi] Improved fix for ConcurrentModificationIssue (Spark-1097, Hadoop-10456)
6b840f0 [nravi] Undo the fix for SPARK-1758 (the problem is fixed)
5108700 [nravi] Fix in Spark for the Concurrent thread modification issue (SPARK-1097, HADOOP-10456)
681b36f [nravi] Fix for SPARK-1758: failing test org.apache.spark.JavaAPISuite.wholeTextFiles
[SPARK-4480] Avoid many small spills in external data structures

**Summary.** Currently, we may spill many small files in `ExternalAppendOnlyMap` and `ExternalSorter`. The underlying root cause of this is summarized in [SPARK-4452](https://issues.apache.org/jira/browse/SPARK-4452). This PR does not address this root cause, but simply provides the guarantee that we never spill the in-memory data structure if its size is less than a configurable threshold of 5MB. This config is not documented because we don't want users to set it themselves, and it is not hard-coded because we need to change it in tests.

**Symptom.** Each spill is orders of magnitude smaller than 1MB, and there are many spills. In environments where the ulimit is set, this frequently causes "too many open file" exceptions observed in [SPARK-3633](https://issues.apache.org/jira/browse/SPARK-3633).
```
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4792 B to disk (292769 spills so far)
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4760 B to disk (292770 spills so far)
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4520 B to disk (292771 spills so far)
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4560 B to disk (292772 spills so far)
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4792 B to disk (292773 spills so far)
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4784 B to disk (292774 spills so far)
```

**Reproduction.** I ran the following on a small 4-node cluster with 512MB executors. Note that the back-to-back shuffle here is necessary for reasons described in [SPARK-4522](https://issues.apache.org/jira/browse/SPARK-4452). The second shuffle is a `reduceByKey` because it performs a map-side combine.
```
sc.parallelize(1 to 100000000, 100)
  .map { i => (i, i) }
  .groupByKey()
  .reduceByKey(_ ++ _)
  .count()
```
Before the change, I notice that each thread may spill up to 1000 times, and the size of each spill is on the order of 10KB. After the change, each thread spills only up to 20 times in the worst case, and the size of each spill is on the order of 1MB.

Author: Andrew Or <andrew@databricks.com>

Closes #3353 from andrewor14/avoid-small-spills and squashes the following commits:

49f380f [Andrew Or] Merge branch 'master' of https://git-wip-us.apache.org/repos/asf/spark into avoid-small-spills
27d6966 [Andrew Or] Merge branch 'master' of github.com:apache/spark into avoid-small-spills
f4736e3 [Andrew Or] Fix tests
a919776 [Andrew Or] Avoid many small spills
[SPARK-4446] [SPARK CORE]

MetadataCleaner schedule task with a wrong param for delay time .

Author: Leolh <leosandylh@gmail.com>

Closes #3306 from Leolh/master and squashes the following commits:

4a21f4e [Leolh] Update MetadataCleaner.scala
[SPARK-4486][MLLIB] Improve GradientBoosting APIs and doc

There are some inconsistencies in the gradient boosting APIs. The target is a general boosting meta-algorithm, but the implementation is attached to trees. This was partially due to the delay of SPARK-1856. But for the 1.2 release, we should make the APIs consistent.

1. WeightedEnsembleModel -> private[tree] TreeEnsembleModel and renamed members accordingly.
1. GradientBoosting -> GradientBoostedTrees
1. Add RandomForestModel and GradientBoostedTreesModel and hide CombiningStrategy
1. Slightly refactored TreeEnsembleModel (Vote takes weights into consideration.)
1. Remove `trainClassifier` and `trainRegressor` from `GradientBoostedTrees` because they are the same as `train`
1. Rename class `train` method to `run` because it hides the static methods with the same name in Java. Deprecated `DecisionTree.train` class method.
1. Simplify BoostingStrategy and make sure the input strategy is not modified. Users should put algo and numClasses in treeStrategy. We create ensembleStrategy inside boosting.
1. Fix a bug in GradientBoostedTreesSuite with AbsoluteError
1. doc updates

manishamde jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #3374 from mengxr/SPARK-4486 and squashes the following commits:

7097251 [Xiangrui Meng] address joseph's comments
98dea09 [Xiangrui Meng] address manish's comments
4aae3b7 [Xiangrui Meng] add RandomForestModel and GradientBoostedTreesModel, hide CombiningStrategy
ea4c467 [Xiangrui Meng] fix unit tests
751da4e [Xiangrui Meng] rename class method train -> run
19030a5 [Xiangrui Meng] update boosting public APIs