## What changes were proposed in this pull request?

[[SPARK-13210][SQL] catch OOM when allocate memory and expand array](https://github.com/apache/spark/commit/37bc203c8dd5022cb11d53b697c28a737ee85bcc) introduced an `acquiredButNotUsed` field, but it might not be correctly synchronized:
- the write `acquiredButNotUsed += acquired` is guarded by `this` lock (see [here](https://github.com/apache/spark/blame/master/core/src/main/java/org/apache/spark/memory/TaskMemoryManager.java#L271));
- the read `memoryManager.releaseExecutionMemory(acquiredButNotUsed, taskAttemptId, tungstenMemoryMode)` (see [here](https://github.com/apache/spark/blame/master/core/src/main/java/org/apache/spark/memory/TaskMemoryManager.java#L400)) might not be correctly synchronized, and thus might not see `acquiredButNotUsed`'s most recent value.

This patch makes `acquiredButNotUsed` volatile to fix this.

## How was this patch tested?

This should be covered by existing suits.

Author: Liwei Lin <lwlin7@gmail.com>

Closes #12681 from lw-lin/fix-acquiredButNotUsed.

## What changes were proposed in this pull request?
We currently expose both Hadoop configuration and Spark SQL configuration in RuntimeConfig. I think we can remove the Hadoop configuration part, and simply generate Hadoop Configuration on the fly by passing all the SQL configurations into it. This way, there is a single interface (in Java/Scala/Python/SQL) for end-users.

As part of this patch, I also removed some config options deprecated in Spark 1.x.

## How was this patch tested?
Updated relevant tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #12689 from rxin/SPARK-14913.

## What changes were proposed in this pull request?

#12625 exposed a new user-facing conf interface in `SparkSession`. This patch adds a catalog interface.

## How was this patch tested?

See `CatalogSuite`.

Author: Andrew Or <andrew@databricks.com>

Closes #12713 from andrewor14/user-facing-catalog.

## What changes were proposed in this pull request?
This PR adds Native execution of SHOW COLUMNS and SHOW PARTITION commands.

Command Syntax:
``` SQL
SHOW COLUMNS (FROM | IN) table_identifier [(FROM | IN) database]
```
``` SQL
SHOW PARTITIONS [db_name.]table_name [PARTITION(partition_spec)]
```

## How was this patch tested?

Added test cases in HiveCommandSuite to verify execution and DDLCommandSuite
to verify plans.

Author: Dilip Biswal <dbiswal@us.ibm.com>

Closes #12222 from dilipbiswal/dkb_show_columns.

## What changes were proposed in this pull request?

Before, spark.ml GaussianMixtureModel used the spark.mllib MultivariateGaussian in its public API.  This was added after 1.6, so we can modify this API without breaking APIs.

This PR copies MultivariateGaussian to mllib-local in spark.ml, with a few changes:
* Renamed fields to match numpy, scipy: mu => mean, sigma => cov

This PR then uses the spark.ml MultivariateGaussian in the spark.ml GaussianMixtureModel, which involves:
* Modifying the constructor
* Adding a computeProbabilities method

Also:
* Added EPSILON to mllib-local for use in MultivariateGaussian

## How was this patch tested?

Existing unit tests

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

Closes #12593 from jkbradley/sparkml-gmm-fix.

## What changes were proposed in this pull request?

Added method histogram() to compute the histogram of a Column

Usage:

```
## Create a DataFrame from the Iris dataset
irisDF <- createDataFrame(sqlContext, iris)

## Render a histogram for the Sepal_Length column
histogram(irisDF, "Sepal_Length", nbins=12)

```
![histogram](https://cloud.githubusercontent.com/assets/13985649/13588486/e1e751c6-e484-11e5-85db-2fc2115c4bb2.png)

Note: Usage will change once SPARK-9325 is figured out so that histogram() only takes a Column as a parameter, as opposed to a DataFrame and a name

## How was this patch tested?

All unit tests pass. I added specific unit cases for different scenarios.

Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.usca.ibm.com>
Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.attlocal.net>

Closes #11569 from olarayej/SPARK-13734.

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.