Add support for Pivotal HD in the Maven build: SPARK-1992

Allow Spark to build against particular Pivotal HD distributions. For example to build Spark against Pivotal HD 2.0.1 one can run:
```
mvn -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0-gphd-3.0.1.0 -DskipTests clean package
```

Author: tzolov <christian.tzolov@gmail.com>

Closes #942 from tzolov/master and squashes the following commits:

bc3e05a [tzolov] Add support for Pivotal HD in the Maven build and SBT build: [SPARK-1992]
[SPARK-1468] Modify the partition function used by partitionBy.

Make partitionBy use a tweaked version of hash as its default partition function
since the python hash function does not consistently assign the same value
to None across python processes.

Associated JIRA at https://issues.apache.org/jira/browse/SPARK-1468

Author: Erik Selin <erik.selin@jadedpixel.com>

Closes #371 from tyro89/consistent_hashing and squashes the following commits:

201c301 [Erik Selin] Make partitionBy use a tweaked version of hash as its default partition function since the python hash function does not consistently assign the same value to None across python processes.
fix java.lang.ClassCastException

get Exception when run：bin/run-example org.apache.spark.examples.sql.RDDRelation
Exception's detail is:
Exception in thread "main" java.lang.ClassCastException: java.lang.Long cannot be cast to java.lang.Integer
	at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
	at org.apache.spark.sql.catalyst.expressions.GenericRow.getInt(Row.scala:145)
	at org.apache.spark.examples.sql.RDDRelation$.main(RDDRelation.scala:49)
	at org.apache.spark.examples.sql.RDDRelation.main(RDDRelation.scala)
change sql("SELECT COUNT(*) FROM records").collect().head.getInt(0) to sql("SELECT COUNT(*) FROM records").collect().head.getLong(0), then the Exception do not occur any more

Author: baishuo(白硕) <vc_java@hotmail.com>

Closes #949 from baishuo/master and squashes the following commits:

f4b319f [baishuo(白硕)] fix java.lang.ClassCastException
Synthetic GraphX Benchmark

This PR accomplishes two things:

1. It introduces a Synthetic Benchmark application that generates an arbitrarily large log-normal graph and executes either PageRank or connected components on the graph.  This can be used to profile GraphX system on arbitrary clusters without access to large graph datasets

2. This PR improves the implementation of the log-normal graph generator.

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

Closes #720 from jegonzal/graphx_synth_benchmark and squashes the following commits:

e40812a [Ankur Dave] Exclude all of GraphX from compatibility checks vs. 1.0.0
bccccad [Ankur Dave] Fix long lines
374678a [Ankur Dave] Bugfix and style changes
1bdf39a [Joseph E. Gonzalez] updating options
d943972 [Joseph E. Gonzalez] moving the benchmark application into the examples folder.
f4f839a [Joseph E. Gonzalez] Creating a synthetic benchmark script.
[SPARK-1991] Support custom storage levels for vertices and edges

This PR adds support for specifying custom storage levels for the vertices and edges of a graph. This enables GraphX to handle graphs larger than memory size by specifying MEMORY_AND_DISK and then repartitioning the graph to use many small partitions, each of which does fit in memory. Spark will then automatically load partitions from disk as needed.

The user specifies the desired vertex and edge storage levels when building the graph by passing them to the graph constructor. These are then stored in the `targetStorageLevel` attribute of the VertexRDD and EdgeRDD respectively. Whenever GraphX needs to cache a VertexRDD or EdgeRDD (because it plans to use it more than once, for example), it uses the specified target storage level. Also, when the user calls `Graph#cache()`, the vertices and edges are persisted using their target storage levels.

In order to facilitate propagating the target storage levels across VertexRDD and EdgeRDD operations, we remove raw calls to the constructors and instead introduce the `withPartitionsRDD` and `withTargetStorageLevel` methods.

I tested this change by running PageRank and triangle count on a severely memory-constrained cluster (1 executor with 300 MB of memory, and a 1 GB graph). Before this PR, these algorithms used to fail with OutOfMemoryErrors. With this PR, and using the DISK_ONLY storage level, they succeed.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #946 from ankurdave/SPARK-1991 and squashes the following commits:

ce17d95 [Ankur Dave] Move pickStorageLevel to StorageLevel.fromString
ccaf06f [Ankur Dave] Shadow members in withXYZ() methods rather than using underscores
c34abc0 [Ankur Dave] Exclude all of GraphX from compatibility checks vs. 1.0.0
c5ca068 [Ankur Dave] Revert "Exclude all of GraphX from binary compatibility checks"
34bcefb [Ankur Dave] Exclude all of GraphX from binary compatibility checks
6fdd137 [Ankur Dave] [SPARK-1991] Support custom storage levels for vertices and edges
Fixed a typo

in RowMatrix.scala

Author: DB Tsai <dbtsai@dbtsai.com>

Closes #959 from dbtsai/dbtsai-typo and squashes the following commits:

fab0e0e [DB Tsai] Fixed typo
[SPARK-1161] Add saveAsPickleFile and SparkContext.pickleFile in Python

Author: Kan Zhang <kzhang@apache.org>

Closes #755 from kanzhang/SPARK-1161 and squashes the following commits:

24ed8a2 [Kan Zhang] [SPARK-1161] Fixing doc tests
44e0615 [Kan Zhang] [SPARK-1161] Adding an optional batchSize with default value 10
d929429 [Kan Zhang] [SPARK-1161] Add saveAsObjectFile and SparkContext.objectFile in Python
SPARK-1941: Update streamlib to 2.7.0 and use HyperLogLogPlus instead of HyperLogLog.

I also corrected some errors made in the previous HLL count approximate API, including relativeSD wasn't really a measure for error (and we used it to test error bounds in test results).

Author: Reynold Xin <rxin@apache.org>

Closes #897 from rxin/hll and squashes the following commits:

4d83f41 [Reynold Xin] New error bound and non-randomness.
f154ea0 [Reynold Xin] Added a comment on the value bound for testing.
e367527 [Reynold Xin] One more round of code review.
41e649a [Reynold Xin] Update final mima list.
9e320c8 [Reynold Xin] Incorporate code review feedback.
e110d70 [Reynold Xin] Merge branch 'master' into hll
354deb8 [Reynold Xin] Added comment on the Mima exclude rules.
acaa524 [Reynold Xin] Added the right exclude rules in MimaExcludes.
6555bfe [Reynold Xin] Added a default method and re-arranged MimaExcludes.
1db1522 [Reynold Xin] Excluded util.SerializableHyperLogLog from MIMA check.
9221b27 [Reynold Xin] Merge branch 'master' into hll
88cfe77 [Reynold Xin] Updated documentation and restored the old incorrect API to maintain API compatibility.
1294be6 [Reynold Xin] Updated HLL+.
e7786cb [Reynold Xin] Merge branch 'master' into hll
c0ef0c2 [Reynold Xin] SPARK-1941: Update streamlib to 2.7.0 and use HyperLogLogPlus instead of HyperLogLog.
use env default python in merge_spark_pr.py

A minor change to use env default python instead of fixed `/usr/bin/python`.

Author: Xiangrui Meng <meng@databricks.com>

Closes #965 from mengxr/merge-pr-python and squashes the following commits:

1ae0013 [Xiangrui Meng] use env default python in merge_spark_pr.py
Enable repartitioning of graph over different number of partitions

It is currently very difficult to repartition a graph over a different number of partitions.  This PR adds an additional `partitionBy` function that takes the number of partitions.

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

Closes #719 from jegonzal/graph_partitioning_options and squashes the following commits:

730b405 [Joseph E. Gonzalez] adding an additional number of partitions option to partitionBy
Update spark-ec2 scripts for 1.0.0 on master

The change was previously committed only to branch-1.0 as part of https://github.com/apache/spark/commit/a34e6fda1d6fb8e769c21db70845f1a6dde968d8

Author: Aaron Davidson <aaron@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Patrick Wendell <pwendell@gmail.com>

Closes #938 from aarondav/sparkec2 and squashes the following commits:

067cc31 [Aaron Davidson] Update spark-ec2 scripts for 1.0.0 on master
SPARK-1806 (addendum) Use non-deprecated methods in Mesos 0.18

The update to Mesos 0.18 caused some deprecation warnings in the build. The change to the non-deprecated version is straightforward as it emulates what the Mesos driver does with the deprecated method anyway (https://github.com/apache/mesos/blob/c5aa1dd22155d79c5a7c33076319299a40fd63b3/src/sched/sched.cpp#L1354)

Author: Sean Owen <sowen@cloudera.com>

Closes #920 from srowen/SPARK-1806 and squashes the following commits:

8d76b6a [Sean Owen] Use non-deprecated methods in Mesos 0.18
[SPARK-1817] RDD.zip() should verify partition sizes for each partition

RDD.zip() will throw an exception if it finds partition sizes are not the same.

Author: Kan Zhang <kzhang@apache.org>

Closes #944 from kanzhang/SPARK-1817 and squashes the following commits:

c073848 [Kan Zhang] [SPARK-1817] Cosmetic updates
524c670 [Kan Zhang] [SPARK-1817] RDD.zip() should verify partition sizes for each partition
[MLLIB] set RDD names in ALS

This is very useful when debugging & fine tuning jobs with large data sets.

Author: Neville Li <neville@spotify.com>

Closes #966 from nevillelyh/master and squashes the following commits:

6747764 [Neville Li] [MLLIB] use string interpolation for RDD names
3b15d34 [Neville Li] [MLLIB] set RDD names in ALS
SPARK-1973. Add randomSplit to JavaRDD (with tests, and tidy Java tests)

I'd like to use randomSplit through the Java API, and would like to add a convenience wrapper for this method to JavaRDD. This is fairly trivial. (In fact, is the intent that JavaRDD not wrap every RDD method? and that sometimes users should just use JavaRDD.wrapRDD()?)

Along the way, I added tests for it, and also touched up the Java API test style and behavior. This is maybe the more useful part of this small change.

Author: Sean Owen <sowen@cloudera.com>
Author: Xiangrui Meng <meng@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Xiangrui Meng <meng@databricks.com>

Closes #919 from srowen/SPARK-1973 and squashes the following commits:

148cb7b [Sean Owen] Some final Java test polish, while we are at it
1fc3f3e [Xiangrui Meng] more cleaning on Java 8 tests
9ebc57f [Sean Owen] Use accumulator instead of temp files to test foreach
5efb0be [Sean Owen] Add Java randomSplit, and unit tests (including for sample)
5dcc158 [Sean Owen] Simplified Java 8 test with new language features, and fixed the name of MLB's greatest team
91a1769 [Sean Owen] Touch up minor style issues in existing Java API suite test
[SPARK-1752][MLLIB] Standardize text format for vectors and labeled points

We should standardize the text format used to represent vectors and labeled points. The proposed formats are the following:

1. dense vector: `[v0,v1,..]`
2. sparse vector: `(size,[i0,i1],[v0,v1])`
3. labeled point: `(label,vector)`

where "(..)" indicates a tuple and "[...]" indicate an array. `loadLabeledPoints` is added to pyspark's `MLUtils`. I didn't add `loadVectors` to pyspark because `RDD.saveAsTextFile` cannot stringify dense vectors in the proposed format automatically.

`MLUtils#saveLabeledData` and `MLUtils#loadLabeledData` are deprecated. Users should use `RDD#saveAsTextFile` and `MLUtils#loadLabeledPoints` instead. In Scala, `MLUtils#loadLabeledPoints` is compatible with the format used by `MLUtils#loadLabeledData`.

CC: @mateiz, @srowen

Author: Xiangrui Meng <meng@databricks.com>

Closes #685 from mengxr/labeled-io and squashes the following commits:

2d1116a [Xiangrui Meng] make loadLabeledData/saveLabeledData deprecated since 1.0.1
297be75 [Xiangrui Meng] change LabeledPoint.parse to LabeledPointParser.parse to maintain binary compatibility
d6b1473 [Xiangrui Meng] Merge branch 'master' into labeled-io
56746ea [Xiangrui Meng] replace # by .
623a5f0 [Xiangrui Meng] merge master
f06d5ba [Xiangrui Meng] add docs and minor updates
640fe0c [Xiangrui Meng] throw SparkException
5bcfbc4 [Xiangrui Meng] update test to add scientific notations
e86bf38 [Xiangrui Meng] remove NumericTokenizer
050fca4 [Xiangrui Meng] use StringTokenizer
6155b75 [Xiangrui Meng] merge master
f644438 [Xiangrui Meng] remove parse methods based on eval from pyspark
a41675a [Xiangrui Meng] python loadLabeledPoint uses Scala's implementation
ce9a475 [Xiangrui Meng] add deserialize_labeled_point to pyspark with tests
e9fcd49 [Xiangrui Meng] add serializeLabeledPoint and tests
aea4ae3 [Xiangrui Meng] minor updates
810d6df [Xiangrui Meng] update tokenizer/parser implementation
7aac03a [Xiangrui Meng] remove Scala parsers
c1885c1 [Xiangrui Meng] add headers and minor changes
b0c50cb [Xiangrui Meng] add customized parser
d731817 [Xiangrui Meng] style update
63dc396 [Xiangrui Meng] add loadLabeledPoints to pyspark
ea122b5 [Xiangrui Meng] Merge branch 'master' into labeled-io
cd6c78f [Xiangrui Meng] add __str__ and parse to LabeledPoint
a7a178e [Xiangrui Meng] add stringify to pyspark's Vectors
5c2dbfa [Xiangrui Meng] add parse to pyspark's Vectors
7853f88 [Xiangrui Meng] update pyspark's SparseVector.__str__
e761d32 [Xiangrui Meng] make LabelPoint.parse compatible with the dense format used before v1.0 and deprecate loadLabeledData and saveLabeledData
9e63a02 [Xiangrui Meng] add loadVectors and loadLabeledPoints
19aa523 [Xiangrui Meng] update toString and add parsers for Vectors and LabeledPoint
SPARK-1518: FileLogger: Fix compile against Hadoop trunk

In Hadoop trunk (currently Hadoop 3.0.0), the deprecated
FSDataOutputStream#sync() method has been removed.  Instead, we should
call FSDataOutputStream#hflush, which does the same thing as the
deprecated method used to do.

Author: Colin McCabe <cmccabe@cloudera.com>

Closes #898 from cmccabe/SPARK-1518 and squashes the following commits:

752b9d7 [Colin McCabe] FileLogger: Fix compile against Hadoop trunk
SPARK-1790: Update EC2 scripts to support r3 instance types

Author: Varakhedi Sujeet <svarakhedi@gopivotal.com>

Closes #960 from sujeetv/ec2-r3 and squashes the following commits:

3cb9fd5 [Varakhedi Sujeet] SPARK-1790: Update EC2 scripts to support r3 instance
Minor: Fix documentation error from apache/spark#946

Author: Ankur Dave <ankurdave@gmail.com>

Closes #970 from ankurdave/SPARK-1991_docfix and squashes the following commits:

6d07343 [Ankur Dave] Minor: Fix documentation error from apache/spark#946
Fix issue in ReplSuite with hadoop-provided profile.

When building the assembly with the maven "hadoop-provided"
profile, the executors were failing to come up because Hadoop classes
were not found in the classpath anymore; so add them explicitly to
the classpath using spark.executor.extraClassPath. This is only
needed for the local-cluster mode, but doesn't affect other tests,
so it's added for all of them to keep the code simpler.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #781 from vanzin/repl-test-fix and squashes the following commits:

4f0a3b0 [Marcelo Vanzin] Fix issue in ReplSuite with hadoop-provided profile.