Spark's published POMs are supposed to be flattened and not contain variable substitution (see SPARK-3812), but the dummy dependency that was required for this was accidentally removed. We should re-introduce this dependency in order to fix an issue where the un-flattened POMs cause the wrong dependencies to be included in Scala 2.10 published POMs.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #12706 from JoshRosen/SPARK-14925-published-poms-should-be-flattened.

## What changes were proposed in this pull request?

While the vectorized hash map in `TungstenAggregate` is currently supported for all primitive data types during partial aggregation, this patch only enables the hash map for a subset of cases that've been verified to show performance improvements on our benchmarks subject to an internal conf that sets an upper limit on the maximum length of the aggregate key/value schema. This list of supported use-cases should be expanded over time.

## How was this patch tested?

This is no new change in functionality so existing tests should suffice. Performance tests were done on TPCDS benchmarks.

Author: Sameer Agarwal <sameer@databricks.com>

Closes #12710 from sameeragarwal/vectorized-enable.

## What changes were proposed in this pull request?

There have been continuing requests (e.g., SPARK-7131) for allowing users to extend and modify MLlib models and algorithms.

This PR makes tree and ensemble classes, Node types, and Split types in spark.ml no longer final.  This matches most other spark.ml algorithms.

Constructors for models are still private since we may need to refactor how stats are maintained in tree nodes.

## How was this patch tested?

Existing unit tests

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #12711 from jkbradley/final-trees.

## What changes were proposed in this pull request?
Add the missing python example for VectorSlicer

## How was this patch tested?
manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12282 from zhengruifeng/vecslicer_pe.

## What changes were proposed in this pull request?

This PR changes `GLMRegressionModel.save` function like the following code that is similar to other algorithms' parquet write.
```
- val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
- // TODO: repartition with 1 partition after SPARK-5532 gets fixed
- dataRDD.write.parquet(Loader.dataPath(path))
+ sqlContext.createDataFrame(Seq(data)).repartition(1).write.parquet(Loader.dataPath(path))
```

## How was this patch tested?

Manual.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #12676 from dongjoon-hyun/SPARK-14907.

## What changes were proposed in this pull request?

This PR update SortMergeJoinExec to support LeftSemi/LeftAnti, so it could support all the join types, same as other three join implementations: BroadcastHashJoinExec, ShuffledHashJoinExec,and BroadcastNestedLoopJoinExec.

This PR also simplify the join selection in SparkStrategy.

## How was this patch tested?

Added new tests.

Author: Davies Liu <davies@databricks.com>

Closes #12668 from davies/smj_semi.

## What changes were proposed in this pull request?

SPARK-14071 changed MLWritable.write to be a property.  This reverts that change since there was not a good way to make MLReadable.read appear to be a property.

## How was this patch tested?

existing unit tests

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #12671 from jkbradley/revert-MLWritable-write-py.

## What changes were proposed in this pull request?
We deprecated  ```runs``` of mllib.KMeans in Spark 1.6 (SPARK-11358). In 2.0, we will make it no effect (with warning messages). We did not remove ```setRuns/getRuns``` for better binary compatibility.
This PR change `runs` which are appeared at the public API. Usage inside of ```KMeans.runAlgorithm()``` will be resolved at #10806.

## How was this patch tested?
Existing unit tests.

cc jkbradley

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #12608 from yanboliang/spark-11559.

## What changes were proposed in this pull request?

That patch mistakenly widened the visibility from `private[x]` to `protected[x]`. This patch reverts those changes.

Author: Andrew Or <andrew@databricks.com>

Closes #12686 from andrewor14/visibility.

## What changes were proposed in this pull request?
We currently have no way for users to propagate options to the underlying library that rely in Hadoop configurations to work. For example, there are various options in parquet-mr that users might want to set, but the data source API does not expose a per-job way to set it. This patch propagates the user-specified options also into Hadoop Configuration.

## How was this patch tested?
Used a mock data source implementation to test both the read path and the write path.

Author: Reynold Xin <rxin@databricks.com>

Closes #12688 from rxin/SPARK-14912.

## What changes were proposed in this pull request?
```AFTSurvivalRegressionModel``` supports ```save/load``` in SparkR.

## How was this patch tested?
Unit tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #12685 from yanboliang/spark-14313.

#### What changes were proposed in this pull request?
The existing `Describe Function` only support the function name in `identifier`. This is different from what Hive behaves. That is why many test cases `udf_abc` in `HiveCompatibilitySuite` are not using our native DDL support. For example,
- udf_not.q
- udf_bitwise_not.q

This PR is to resolve the issues. Now, we can support the command of `Describe Function` whose function names are in the following format:
- `qualifiedName` (e.g., `db.func1`)
- `STRING` (e.g., `'func1'`)
- `comparisonOperator` (e.g,. `<`)
- `arithmeticOperator` (e.g., `+`)
- `predicateOperator` (e.g., `or`)

Note, before this PR, we only have a native command support when the function name is in the format of `qualifiedName`.
#### How was this patch tested?
Added test cases in `DDLSuite.scala`. Also manually verified all the related test cases in `HiveCompatibilitySuite` passed.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #12679 from gatorsmile/descFunction.

## What changes were proposed in this pull request?

Minor typo fixes (too minor to deserve separate a JIRA)

## How was this patch tested?

local build

Author: Jacek Laskowski <jacek@japila.pl>

Closes #12469 from jaceklaskowski/minor-typo-fixes.

## What changes were proposed in this pull request?

Use Long.parseLong which returns a primative.
Use a series of appends() reduces the creation of an extra StringBuilder type

## How was this patch tested?

Unit tests

Author: Azeem Jiva <azeemj@gmail.com>

Closes #12520 from javawithjiva/minor.

[SPARK-14889][SPARK CORE] scala.MatchError: NONE (of class scala.Enumeration) when spark.scheduler.mode=NONE

## What changes were proposed in this pull request?

Handling exception for the below mentioned issue

```
➜  spark git:(master) ✗ ./bin/spark-shell -c spark.scheduler.mode=NONE
16/04/25 09:15:00 ERROR SparkContext: Error initializing SparkContext.
scala.MatchError: NONE (of class scala.Enumeration$Val)
	at org.apache.spark.scheduler.Pool.<init>(Pool.scala:53)
	at org.apache.spark.scheduler.TaskSchedulerImpl.initialize(TaskSchedulerImpl.scala:131)
	at org.apache.spark.SparkContext$.org$apache$spark$SparkContext$$createTaskScheduler(SparkContext.scala:2352)
	at org.apache.spark.SparkContext.<init>(SparkContext.scala:492)
```

The exception now looks like

```
java.lang.RuntimeException: The scheduler mode NONE is not supported by Spark.
```

## How was this patch tested?

manual tests

Author: Subhobrata Dey <sbcd90@gmail.com>

Closes #12666 from sbcd90/schedulerModeIssue.

## What changes were proposed in this pull request?

Documentation changes

## How was this patch tested?

No tests

Author: Michael Gummelt <mgummelt@mesosphere.io>

Closes #12664 from mgummelt/fix-dynamic-docs.

## What changes were proposed in this pull request?

Made BinaryClassificationEvaluator, MulticlassClassificationEvaluator and RegressionEvaluator accept all numeric types for label

## How was this patch tested?

Unit tests

Author: BenFradet <benjamin.fradet@gmail.com>

Closes #12500 from BenFradet/SPARK-13962.

## What changes were proposed in this pull request?

In Spark 2.0, `SparkSession` is the new thing. Internally we should stop using `SQLContext` everywhere since that's supposed to be not the main user-facing API anymore.

In this patch I took care to not break any public APIs. The one place that's suspect is `o.a.s.ml.source.libsvm.DefaultSource`, but according to mengxr it's not supposed to be public so it's OK to change the underlying `FileFormat` trait.

**Reviewers**: This is a big patch that may be difficult to review but the changes are actually really straightforward. If you prefer I can break it up into a few smaller patches, but it will delay the progress of this issue a little.

## How was this patch tested?

No change in functionality intended.

Author: Andrew Or <andrew@databricks.com>

Closes #12625 from andrewor14/spark-session-refactor.

## What changes were proposed in this pull request?
This is for users who can't upgrade and need to continue to use HiveContext.

## How was this patch tested?
Added some basic tests for sanity check.

This is based on #12672 and closes #12672.

Author: Andrew Or <andrew@databricks.com>
Author: Reynold Xin <rxin@databricks.com>

Closes #12682 from rxin/add-back-hive-context.

## What changes were proposed in this pull request?

Minor followup to https://github.com/apache/spark/pull/12651

## How was this patch tested?

Test-only change

Author: Sameer Agarwal <sameer@databricks.com>

Closes #12674 from sameeragarwal/tpcds-fix-2.

## What changes were proposed in this pull request?

`RuntimeConfig` is the new user-facing API in 2.0 added in #11378. Until now, however, it's been dead code. This patch uses `RuntimeConfig` in `SessionState` and exposes that through the `SparkSession`.

## How was this patch tested?

New test in `SQLContextSuite`.

Author: Andrew Or <andrew@databricks.com>

Closes #12669 from andrewor14/use-runtime-conf.

## What changes were proposed in this pull request?
This patch changes UnresolvedFunction and UnresolvedGenerator to use a FunctionIdentifier rather than just a String for function name. Also changed SessionCatalog to accept FunctionIdentifier in lookupFunction.

## How was this patch tested?
Updated related unit tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #12659 from rxin/SPARK-14888.

## What changes were proposed in this pull request?

```
Spark context available as 'sc' (master = local[*], app id = local-1461283768192).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.0.0-SNAPSHOT
      /_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_51)
Type in expressions to have them evaluated.
Type :help for more information.

scala> sql("SHOW TABLES").collect()
16/04/21 17:09:39 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0
16/04/21 17:09:39 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
res0: Array[org.apache.spark.sql.Row] = Array([src,false])

scala> sql("SHOW TABLES").collect()
res1: Array[org.apache.spark.sql.Row] = Array([src,false])

scala> spark.createDataFrame(Seq((1, 1), (2, 2), (3, 3)))
res2: org.apache.spark.sql.DataFrame = [_1: int, _2: int]
```

Hive things are loaded lazily.

## How was this patch tested?

Manual.

Author: Andrew Or <andrew@databricks.com>

Closes #12589 from andrewor14/spark-session-repl.

## What changes were proposed in this pull request?
SparkR ```NaiveBayesModel``` supports ```save/load``` by the following API:
```
df <- createDataFrame(sqlContext, infert)
model <- naiveBayes(education ~ ., df, laplace = 0)
ml.save(model, path)
model2 <- ml.load(path)
```

## How was this patch tested?
Add unit tests.

cc mengxr

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #12573 from yanboliang/spark-14312.

#### What changes were proposed in this pull request?

For performance, predicates can be pushed through Window if and only if the following conditions are satisfied:
 1. All the expressions are part of window partitioning key. The expressions can be compound.
 2. Deterministic

#### How was this patch tested?

TODO:
- [X]  DSL needs to be modified for window
- [X] more tests will be added.

Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>

Closes #11635 from gatorsmile/pushPredicateThroughWindow.

## What changes were proposed in this pull request?

This removes the class `HiveContext` itself along with all code usages associated with it. The bulk of the work was already done in #12485. This is mainly just code cleanup and actually removing the class.

Note: A couple of things will break after this patch. These will be fixed separately.
- the python HiveContext
- all the documentation / comments referencing HiveContext
- there will be no more HiveContext in the REPL (fixed by #12589)

## How was this patch tested?

No change in functionality.

Author: Andrew Or <andrew@databricks.com>

Closes #12585 from andrewor14/delete-hive-context.

## What changes were proposed in this pull request?
SPARK-12130 make 2.0 shuffle service incompatible with 1.x. So from discussion: [http://apache-spark-developers-list.1001551.n3.nabble.com/YARN-Shuffle-service-and-its-compatibility-td17222.html](url) we should maintain compatibility between Spark 1.x and Spark 2.x's shuffle service.
I put string comparison into executor's register at first avoid string comparison in getBlockData every time.

## How was this patch tested?
N/A

Author: Lianhui Wang <lianhuiwang09@gmail.com>

Closes #12568 from lianhuiwang/SPARK-14731.

## What changes were proposed in this pull request?
As the discussion at [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574), ```HashingTF``` should support MurmurHash3 and make it as the default hash algorithm. We should also expose set/get API for ```hashAlgorithm```, then users can choose the hash method.

Note: The problem that ```mllib.feature.HashingTF``` behaves differently between Scala/Java and Python will be resolved in the followup work.

## How was this patch tested?
unit tests.

cc jkbradley MLnick

Author: Yanbo Liang <ybliang8@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #12498 from yanboliang/spark-10574.

[SPARK-14892][SQL][TEST] Disable the HiveCompatibilitySuite test case for INPUTDRIVER and OUTPUTDRIVER.

#### What changes were proposed in this pull request?
Disable the test case involving INPUTDRIVER and OUTPUTDRIVER, which are not supported

#### How was this patch tested?
N/A

Author: gatorsmile <gatorsmile@gmail.com>

Closes #12662 from gatorsmile/disableInOutDriver.

## What changes were proposed in this pull request?

Removed instances of JavaMLWriter, JavaMLReader appearing in public Python API docs

## How was this patch tested?

n/a

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #12542 from jkbradley/javamlwriter-doc.

## What changes were proposed in this pull request?

Add Python API in ML for GaussianMixture

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)

Add doctest and test cases are the same as mllib Python tests
./dev/lint-python
PEP8 checks passed.
rm -rf _build/*
pydoc checks passed.

./python/run-tests --python-executables=python2.7 --modules=pyspark-ml
Running PySpark tests. Output is in /Users/mwang/spark_ws_0904/python/unit-tests.log
Will test against the following Python executables: ['python2.7']
Will test the following Python modules: ['pyspark-ml']
Finished test(python2.7): pyspark.ml.evaluation (18s)
Finished test(python2.7): pyspark.ml.clustering (40s)
Finished test(python2.7): pyspark.ml.classification (49s)
Finished test(python2.7): pyspark.ml.recommendation (44s)
Finished test(python2.7): pyspark.ml.feature (64s)
Finished test(python2.7): pyspark.ml.regression (45s)
Finished test(python2.7): pyspark.ml.tuning (30s)
Finished test(python2.7): pyspark.ml.tests (56s)
Tests passed in 106 seconds

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes #12402 from wangmiao1981/gmm.

First, make all dependencies in the examples module provided, and explicitly
list a couple of ones that somehow are promoted to compile by maven. This
means that to run streaming examples, the streaming connector package needs
to be provided to run-examples using --packages or --jars, just like regular
apps.

Also, remove a couple of outdated examples. HBase has had Spark bindings for
a while and is even including them in the HBase distribution in the next
version, making the examples obsolete. The same applies to Cassandra, which
seems to have a proper Spark binding library already.

I just tested the build, which passes, and ran SparkPi. The examples jars
directory now has only two jars:

```
$ ls -1 examples/target/scala-2.11/jars/
scopt_2.11-3.3.0.jar
spark-examples_2.11-2.0.0-SNAPSHOT.jar
```

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #12544 from vanzin/SPARK-14744.

## What changes were proposed in this pull request?
Removed expectedType arg from PySpark Param __init__, as suggested by the JIRA.

## How was this patch tested?
Manually looked through all places that use Param. Compiled and ran all ML PySpark test cases before and after the fix.

Author: Jason Lee <cjlee@us.ibm.com>

Closes #12581 from jasoncl/SPARK-14768.

## What changes were proposed in this pull request?

This method was accidentally made `private[sql]` in Spark 2.0. This PR makes it public again, since 3rd party data sources like spark-avro depend on it.

## How was this patch tested?

N/A

Author: Cheng Lian <lian@databricks.com>

Closes #12652 from liancheng/spark-14875.

## What changes were proposed in this pull request?

Implement the same memory size validations for the StaticMemoryManager (Legacy) as the UnifiedMemoryManager has.

## How was this patch tested?

Manual tests were done in CDH cluster.

Test with small executor memory:
`
spark-submit --class org.apache.spark.examples.SparkPi --deploy-mode client --master yarn --executor-memory 15m --conf spark.memory.useLegacyMode=true /opt/cloudera/parcels/CDH/lib/spark/examples/lib/spark-examples*.jar 10
`

Exception thrown:
```
ERROR spark.SparkContext: Error initializing SparkContext.
java.lang.IllegalArgumentException: Executor memory 15728640 must be at least 471859200. Please increase executor memory using the --executor-memory option or spark.executor.memory in Spark configuration.
	at org.apache.spark.memory.StaticMemoryManager$.org$apache$spark$memory$StaticMemoryManager$$getMaxExecutionMemory(StaticMemoryManager.scala:127)
	at org.apache.spark.memory.StaticMemoryManager.<init>(StaticMemoryManager.scala:46)
	at org.apache.spark.SparkEnv$.create(SparkEnv.scala:352)
	at org.apache.spark.SparkEnv$.createDriverEnv(SparkEnv.scala:193)
	at org.apache.spark.SparkContext.createSparkEnv(SparkContext.scala:289)
	at org.apache.spark.SparkContext.<init>(SparkContext.scala:462)
	at org.apache.spark.examples.SparkPi$.main(SparkPi.scala:29)
	at org.apache.spark.examples.SparkPi.main(SparkPi.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
	at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
	at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
	at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
	at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
```

Author: Peter Ableda <peter.ableda@cloudera.com>

Closes #12395 from peterableda/SPARK-14636.

## What changes were proposed in this pull request?
add the checking for StepSize and Tol in sharedParams

## How was this patch tested?
Unit tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12530 from zhengruifeng/ml_args_checking.

## What changes were proposed in this pull request?

Sbt compile and test should also run scalastyle. This makes it less likely you forget to run scalastyle and fail in jenkins. Scalastyle results are cached for efficiency.

This patch was originally written by ahirreddy; I just fixed it up to work with scalastyle 0.8.0.

## How was this patch tested?

Tested manually with `build/sbt package`.

Author: Eric Liang <ekl@databricks.com>

Closes #12555 from ericl/scalastyle.

## What changes were proposed in this pull request?

This PR fixes a bug in `TungstenAggregate` that manifests while aggregating by keys over nullable `BigDecimal` columns. This causes a null pointer exception while executing TPCDS q14a.

## How was this patch tested?

1. Added regression test in `DataFrameAggregateSuite`.
2. Verified that TPCDS q14a works

Author: Sameer Agarwal <sameer@databricks.com>

Closes #12651 from sameeragarwal/tpcds-fix.