[Minor] Remove unused val in Master

Author: Andrew Or <andrewor14@gmail.com>

Closes #1365 from andrewor14/master-fs and squashes the following commits:

497f100 [Andrew Or] Sneak in a space and hope no one will notice
05ba6da [Andrew Or] Remove unused val
[SPARK-2437] Rename MAVEN_PROFILES to SBT_MAVEN_PROFILES and add SBT_MAVEN_PROPERTIES

NOTE: It is not possible to use both env variable  `SBT_MAVEN_PROFILES`  and `-P` flag at same time. `-P` if specified takes precedence.

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

Closes #1374 from ScrapCodes/SPARK-2437/rename-MAVEN_PROFILES and squashes the following commits:

8694bde [Prashant Sharma] [SPARK-2437] Rename MAVEN_PROFILES to SBT_MAVEN_PROFILES and add SBT_MAVEN_PROPERTIES
[SPARK-2457] Inconsistent description in README about build option

Now, we should use -Pyarn instead of SPARK_YARN when building but README says as follows.

    For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
    with YARN, also set `SPARK_YARN=true`:

      # Apache Hadoop 2.0.5-alpha
      $ sbt/sbt -Dhadoop.version=2.0.5-alpha -Pyarn assembly

      # Cloudera CDH 4.2.0 with MapReduce v2
      $ sbt/sbt -Dhadoop.version=2.0.0-cdh4.2.0 -Pyarn assembly

      # Apache Hadoop 2.2.X and newer
      $ sbt/sbt -Dhadoop.version=2.2.0 -Pyarn assembly

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #1382 from sarutak/SPARK-2457 and squashes the following commits:

e7b2d64 [Kousuke Saruta] Replaced "SPARK_YARN=true" with "-Pyarn" in README
[SPARK-1969][MLlib] Online summarizer APIs for mean, variance, min, and max

It basically moved the private ColumnStatisticsAggregator class from RowMatrix to public available DeveloperApi with documentation and unitests.

Changes:
1) Moved the private implementation from org.apache.spark.mllib.linalg.ColumnStatisticsAggregator to org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
2) When creating OnlineSummarizer object, the number of columns is not needed in the constructor. It's determined when users add the first sample.
3) Added the APIs documentation for MultivariateOnlineSummarizer.
4) Added the unittests for MultivariateOnlineSummarizer.

Author: DB Tsai <dbtsai@dbtsai.com>

Closes #955 from dbtsai/dbtsai-summarizer and squashes the following commits:

b13ac90 [DB Tsai] dbtsai-summarizer
use specialized axpy in RowMatrix for SVD

After running some more tests on large matrix, found that the BV axpy (breeze/linalg/Vector.scala, axpy) is slower than the BSV axpy (breeze/linalg/operators/SparseVectorOps.scala, sv_dv_axpy), 8s v.s. 2s for each multiplication. The BV axpy operates on an iterator while BSV axpy directly operates on the underlying array. I think the overhead comes from creating the iterator (with a zip) and advancing the pointers.

Author: Li Pu <lpu@twitter.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Li Pu <li.pu@outlook.com>

Closes #1378 from vrilleup/master and squashes the following commits:

6fb01a3 [Li Pu] use specialized axpy in RowMatrix
5255f2a [Li Pu] Merge remote-tracking branch 'upstream/master'
7312ec1 [Li Pu] very minor comment fix
4c618e9 [Li Pu] Merge pull request #1 from mengxr/vrilleup-master
a461082 [Xiangrui Meng] make superscript show up correctly in doc
861ec48 [Xiangrui Meng] simplify axpy
62969fa [Xiangrui Meng] use BDV directly in symmetricEigs change the computation mode to local-svd, local-eigs, and dist-eigs update tests and docs
c273771 [Li Pu] automatically determine SVD compute mode and parameters
7148426 [Li Pu] improve RowMatrix multiply
5543cce [Li Pu] improve svd api
819824b [Li Pu] add flag for dense svd or sparse svd
eb15100 [Li Pu] fix binary compatibility
4c7aec3 [Li Pu] improve comments
e7850ed [Li Pu] use aggregate and axpy
827411b [Li Pu] fix EOF new line
9c80515 [Li Pu] use non-sparse implementation when k = n
fe983b0 [Li Pu] improve scala style
96d2ecb [Li Pu] improve eigenvalue sorting
e1db950 [Li Pu] SPARK-1782: svd for sparse matrix using ARPACK
Use the Executor's ClassLoader in sc.objectFile().

This makes it possible to read classes from the object file which were specified in the user-provided jars. (By default ObjectInputStream uses latestUserDefinedLoader, which may or may not be the right one.)

I created this because I ran into the following problem. I have x:RDD[X] with X being defined in the jar that I provide to SparkContext. I save it with x.saveAsObjectFile("x"). I try to load it with sc.objectFile\[X\]("x"). It fails with ClassNotFoundException.

After a good while of debugging I figured out that Utils.deserialize() most likely uses the ClassLoader of Utils. This is the bootstrap ClassLoader, so it is not aware of the dynamically added jars. This patch fixes the issue.

A more robust fix would be to always default to Thread.currentThread.getContextClassLoader. This would prevent this problem from biting anyone in the future. It would be a bit harder to test though. On the topic of testing, if you'd like to see tests for this, I will need some hand-holding. Thanks!

Author: Daniel Darabos <darabos.daniel@gmail.com>

Closes #181 from darabos/master and squashes the following commits:

45a011a [Daniel Darabos] Add test for SPARK-1877. (Fixed in 52eb54d.)
e13e090 [Daniel Darabos] Merge branch 'master' of https://github.com/apache/spark
61fe0d0 [Daniel Darabos] Fix style (line too long).
1b5df2c [Daniel Darabos] Use the Executor's ClassLoader in sc.objectFile(). This makes it possible to read classes from the object file which were specified in the user-provided jars. (By default ObjectInputStream uses latestUserDefinedLoader, which may or may not be the right one.)
[SPARK-2455] Mark (Shippable)VertexPartition serializable

VertexPartition and ShippableVertexPartition are contained in RDDs but are not marked Serializable, leading to NotSerializableExceptions when using Java serialization.

The fix is simply to mark them as Serializable. This PR does that and adds a test for serializing them using Java and Kryo serialization.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #1376 from ankurdave/SPARK-2455 and squashes the following commits:

ed4a51b [Ankur Dave] Make (Shippable)VertexPartition serializable
1fd42c5 [Ankur Dave] Add failing tests for Java serialization
[SPARK-2441][SQL] Add more efficient distinct operator.

Author: Michael Armbrust <michael@databricks.com>

Closes #1366 from marmbrus/partialDistinct and squashes the following commits:

12a31ab [Michael Armbrust] Add more efficient distinct operator.
[SPARK-2405][SQL] Reusue same byte buffers when creating new instance of InMemoryRelation

Reuse byte buffers when creating unique attributes for multiple instances of an InMemoryRelation in a single query plan.

Author: Michael Armbrust <michael@databricks.com>

Closes #1332 from marmbrus/doubleCache and squashes the following commits:

4a19609 [Michael Armbrust] Clean up concurrency story by calculating buffersn the constructor.
b39c931 [Michael Armbrust] Allocations are kind of a side effect.
f67eff7 [Michael Armbrust] Reusue same byte buffers when creating new instance of InMemoryRelation
SPARK-2462.  Make Vector.apply public.

Apologies if there's an already-discussed reason I missed for why this doesn't make sense.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1389 from sryza/sandy-spark-2462 and squashes the following commits:

2e5e201 [Sandy Ryza] SPARK-2462.  Make Vector.apply public.
SPARK-2363. Clean MLlib's sample data files

(Just made a PR for this, mengxr was the reporter of:)

MLlib has sample data under serveral folders:
1) data/mllib
2) data/
3) mllib/data/*
Per previous discussion with Matei Zaharia, we want to put them under `data/mllib` and clean outdated files.

Author: Sean Owen <sowen@cloudera.com>

Closes #1394 from srowen/SPARK-2363 and squashes the following commits:

54313dd [Sean Owen] Move ML example data from /mllib/data/ and /data/ into /data/mllib/
Made rdd.py pep8 complaint by using Autopep8 and a little manual editing.

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

Closes #1354 from ScrapCodes/pep8-comp-1 and squashes the following commits:

9858ea8 [Prashant Sharma] Code Review
d8851b7 [Prashant Sharma] Found # noqa works even inside comment blocks. Not sure if it works with all versions of python.
10c0cef [Prashant Sharma] Made rdd.py pep8 complaint by using Autopep8 and a little manual tweaking.
move some test file to match src code

Just move some test suite to corresponding package

Author: Daoyuan <daoyuan.wang@intel.com>

Closes #1401 from adrian-wang/movetestfiles and squashes the following commits:

d1a6803 [Daoyuan] move some test file to match src code
[SPARK-2443][SQL] Fix slow read from partitioned tables

This fix obtains a comparable performance boost as [PR #1390](https://github.com/apache/spark/pull/1390) by moving an array update and deserializer initialization out of a potentially very long loop. Suggested by yhuai. The below results are updated for this fix.

## Benchmarks
Generated a local text file with 10M rows of simple key-value pairs. The data is loaded as a table through Hive. Results are obtained on my local machine using hive/console.

Without the fix:

Type | Non-partitioned | Partitioned (1 part)
------------ | ------------ | -------------
First run | 9.52s end-to-end (1.64s Spark job) | 36.6s (28.3s)
Stablized runs | 1.21s (1.18s) | 27.6s (27.5s)

With this fix:

Type | Non-partitioned | Partitioned (1 part)
------------ | ------------ | -------------
First run | 9.57s (1.46s) | 11.0s (1.69s)
Stablized runs | 1.13s (1.10s) | 1.23s (1.19s)

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1408 from concretevitamin/slow-read-2 and squashes the following commits:

d86e437 [Zongheng Yang] Move update & initialization out of potentially long loop.
[SPARK-1946] Submit tasks after (configured ratio) executors have been registered

Because submitting tasks and registering executors are asynchronous, in most situation, early stages' tasks run without preferred locality.

A simple solution is sleeping few seconds in application, so that executors have enough time to register.

The PR add 2 configuration properties to make TaskScheduler submit tasks after a few of executors have been registered.

\# Submit tasks only after (registered executors / total executors) arrived the ratio, default value is 0
spark.scheduler.minRegisteredExecutorsRatio = 0.8

\# Whatever minRegisteredExecutorsRatio is arrived, submit tasks after the maxRegisteredWaitingTime(millisecond), default value is 30000
spark.scheduler.maxRegisteredExecutorsWaitingTime = 5000

Author: li-zhihui <zhihui.li@intel.com>

Closes #900 from li-zhihui/master and squashes the following commits:

b9f8326 [li-zhihui] Add logs & edit docs
1ac08b1 [li-zhihui] Add new configs to user docs
22ead12 [li-zhihui] Move waitBackendReady to postStartHook
c6f0522 [li-zhihui] Bug fix: numExecutors wasn't set & use constant DEFAULT_NUMBER_EXECUTORS
4d6d847 [li-zhihui] Move waitBackendReady to TaskSchedulerImpl.start & some code refactor
0ecee9a [li-zhihui] Move waitBackendReady from DAGScheduler.submitStage to TaskSchedulerImpl.submitTasks
4261454 [li-zhihui] Add docs for new configs & code style
ce0868a [li-zhihui] Code style, rename configuration property name of minRegisteredRatio & maxRegisteredWaitingTime
6cfb9ec [li-zhihui] Code style, revert default minRegisteredRatio of yarn to 0, driver get --num-executors in yarn/alpha
812c33c [li-zhihui] Fix driver lost --num-executors option in yarn-cluster mode
e7b6272 [li-zhihui] support yarn-cluster
37f7dc2 [li-zhihui] support yarn mode(percentage style)
3f8c941 [li-zhihui] submit stage after (configured ratio of) executors have been registered
[SPARK-2446][SQL] Add BinaryType support to Parquet I/O.

Note that this commit changes the semantics when loading in data that was created with prior versions of Spark SQL.  Before, we were writing out strings as Binary data without adding any other annotations. Thus, when data is read in from prior versions, data that was StringType will now become BinaryType.  Users that need strings can CAST that column to a String.  It was decided that while this breaks compatibility, it does make us compatible with other systems (Hive, Thrift, etc) and adds support for Binary data, so this is the right decision long term.

To support `BinaryType`, the following changes are needed:
- Make `StringType` use `OriginalType.UTF8`
- Add `BinaryType` using `PrimitiveTypeName.BINARY` without `OriginalType`

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

Closes #1373 from ueshin/issues/SPARK-2446 and squashes the following commits:

ecacb92 [Takuya UESHIN] Add BinaryType support to Parquet I/O.
616e04a [Takuya UESHIN] Make StringType use OriginalType.UTF8.
[SPARK-2467] Revert SparkBuild to publish-local to both .m2 and .ivy2.

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

Closes #1398 from ueshin/issues/SPARK-2467 and squashes the following commits:

7f01d58 [Takuya UESHIN] Revert SparkBuild to publish-local to both .m2 and .ivy2.
SPARK-2486: Utils.getCallSite is now resilient to bogus frames

When running Spark under certain instrumenting profilers,
Utils.getCallSite could crash with an NPE.  This commit
makes it more resilient to failures occurring while inspecting
stack frames.

Author: William Benton <willb@redhat.com>

Closes #1413 from willb/spark-2486 and squashes the following commits:

b7c0274 [William Benton] Use explicit null checks instead of Try()
0f0c1ae [William Benton] Utils.getCallSite is now resilient to bogus frames
Add/increase severity of warning in documentation of groupBy()

groupBy()/groupByKey() is notorious for being a very convenient API that can lead to poor performance when used incorrectly.

This PR just makes it clear that users should be cautious not to rely on this API when they really want a different (more performant) one, such as reduceByKey().

(Note that one source of confusion is the name; this groupBy() is not the same as a SQL GROUP-BY, which is used for aggregation and is more similar in nature to Spark's reduceByKey().)

Author: Aaron Davidson <aaron@databricks.com>

Closes #1380 from aarondav/warning and squashes the following commits:

f60da39 [Aaron Davidson] Give better advice
d0afb68 [Aaron Davidson] Add/increase severity of warning in documentation of groupBy()
[SPARK-2390] Files in staging directory cannot be deleted and wastes the space of HDFS

When running jobs with YARN Cluster mode and using HistoryServer, the files in the Staging Directory (~/.sparkStaging on HDFS) cannot be deleted.
HistoryServer uses directory where event log is written, and the directory is represented as a instance of o.a.h.f.FileSystem created by using FileSystem.get.

On the other hand, ApplicationMaster has a instance named fs, which also created by using FileSystem.get.

FileSystem.get returns cached same instance when URI passed to the method represents same file system and the method is called by same user.
Because of the behavior, when the directory for event log is on HDFS, fs of ApplicationMaster and fileSystem of FileLogger is same instance.
When shutting down ApplicationMaster, fileSystem.close is called in FileLogger#stop, which is invoked by SparkContext#stop indirectly.

And ApplicationMaster#cleanupStagingDir also called by JVM shutdown hook. In this method, fs.delete(stagingDirPath) is invoked.
Because fs.delete in ApplicationMaster is called after fileSystem.close in FileLogger, fs.delete fails and results not deleting files in the staging directory.

I think, calling fileSystem.delete is not needed.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #1326 from sarutak/SPARK-2390 and squashes the following commits:

10e1a88 [Kousuke Saruta] Removed fileSystem.close from FileLogger.scala not to prevent any other FileSystem operation