## What changes were proposed in this pull request?

As adaptive query execution may change the number of partitions in different batches, it may break streaming queries. Hence, we should disallow this feature in Structured Streaming.

## How was this patch tested?

`test("SPARK-19268: Adaptive query execution should be disallowed")`.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #16683 from zsxwing/SPARK-19268.

[SPARK-9435][SQL] Reuse function in Java UDF to correctly support expressions that require equality comparison between ScalaUDF

## What changes were proposed in this pull request?

Currently, running the codes in Java

```java
spark.udf().register("inc", new UDF1<Long, Long>() {
  Override
  public Long call(Long i) {
    return i + 1;
  }
}, DataTypes.LongType);

spark.range(10).toDF("x").createOrReplaceTempView("tmp");
Row result = spark.sql("SELECT inc(x) FROM tmp GROUP BY inc(x)").head();
Assert.assertEquals(7, result.getLong(0));
```

fails as below:

```
org.apache.spark.sql.AnalysisException: expression 'tmp.`x`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;;
Aggregate [UDF(x#19L)], [UDF(x#19L) AS UDF(x)#23L]
+- SubqueryAlias tmp, `tmp`
   +- Project [id#16L AS x#19L]
      +- Range (0, 10, step=1, splits=Some(8))

	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:40)
	at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:57)
```

The root cause is because we were creating the function every time when it needs to build as below:

```scala
scala> def inc(i: Int) = i + 1
inc: (i: Int)Int

scala> (inc(_: Int)).hashCode
res15: Int = 1231799381

scala> (inc(_: Int)).hashCode
res16: Int = 2109839984

scala> (inc(_: Int)) == (inc(_: Int))
res17: Boolean = false
```

This seems leading to the comparison failure between `ScalaUDF`s created from Java UDF API, for example, in `Expression.semanticEquals`.

In case of Scala one, it seems already fine.

Both can be tested easily as below if any reviewer is more comfortable with Scala:

```scala
val df = Seq((1, 10), (2, 11), (3, 12)).toDF("x", "y")
val javaUDF = new UDF1[Int, Int]  {
  override def call(i: Int): Int = i + 1
}
// spark.udf.register("inc", javaUDF, IntegerType) // Uncomment this for Java API
// spark.udf.register("inc", (i: Int) => i + 1)    // Uncomment this for Scala API
df.createOrReplaceTempView("tmp")
spark.sql("SELECT inc(y) FROM tmp GROUP BY inc(y)").show()
```

## How was this patch tested?

Unit test in `JavaUDFSuite.java` and `./dev/lint-java`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16553 from HyukjinKwon/SPARK-9435.

## What changes were proposed in this pull request?

Hive will expand the view text, so it needs 2 fields: originalText and viewText. Since we don't expand the view text, but only add table properties, perhaps only a single field `viewText` is enough in CatalogTable.

This PR brought in the following changes:
1. Remove the param `viewOriginalText` from `CatalogTable`;
2. Update the output of command `DescribeTableCommand`.

## How was this patch tested?

Tested by exsiting test cases, also updated the failed test cases.

Author: jiangxingbo <jiangxb1987@gmail.com>

Closes #16679 from jiangxb1987/catalogTable.

## What changes were proposed in this pull request?

To implement DDL commands, we added several analyzer rules in sql/hive module to analyze DDL related plans. However, our `Analyzer` currently only have one extending interface: `extendedResolutionRules`, which defines extra rules that will be run together with other rules in the resolution batch, and doesn't fit DDL rules well, because:

1. DDL rules may do some checking and normalization, but we may do it many times as the resolution batch will run rules again and again, until fixed point, and it's hard to tell if a DDL rule has already done its checking and normalization. It's fine because DDL rules are idempotent, but it's bad for analysis performance
2. some DDL rules may depend on others, and it's pretty hard to write `if` conditions to guarantee the dependencies. It will be good if we have a batch which run rules in one pass, so that we can guarantee the dependencies by rules order.

This PR adds a new extending interface in `Analyzer`: `postHocResolutionRules`, which defines rules that will be run only once in a batch runs right after the resolution batch.

## How was this patch tested?

existing tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #16645 from cloud-fan/analyzer.

## What changes were proposed in this pull request?

1, add test for `WeightCol` in `MLTestingUtils.checkNumericTypes`
2, move datatype cast to `Predict.fit`, and supply algos' `train()` with casted dataframe
## How was this patch tested?

local tests in spark-shell and unit tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #15314 from zhengruifeng/weightCol_support_int.

## What changes were proposed in this pull request?

In `DiskBlockObjectWriter`, when some errors happened during writing, it will call `revertPartialWritesAndClose`, if this method again failed due to some issues like out of disk, it will throw exception without resetting the state of this writer, also skipping the revert. So here propose to fix this issue to offer user a chance to recover from such issue.

## How was this patch tested?

Existing test.

Author: jerryshao <sshao@hortonworks.com>

Closes #16657 from jerryshao/SPARK-19306.

[SPARK-16473][MLLIB] Fix BisectingKMeans Algorithm failing in edge case where no children exist in updateAssignments

## What changes were proposed in this pull request?

Fix a bug in which BisectingKMeans fails with error:
java.util.NoSuchElementException: key not found: 166
        at scala.collection.MapLike$class.default(MapLike.scala:228)
        at scala.collection.AbstractMap.default(Map.scala:58)
        at scala.collection.MapLike$class.apply(MapLike.scala:141)
        at scala.collection.AbstractMap.apply(Map.scala:58)
        at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$org$apache$spark$mllib$clustering$BisectingKMeans$$updateAssignments$1$$anonfun$2.apply$mcDJ$sp(BisectingKMeans.scala:338)
        at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$org$apache$spark$mllib$clustering$BisectingKMeans$$updateAssignments$1$$anonfun$2.apply(BisectingKMeans.scala:337)
        at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$org$apache$spark$mllib$clustering$BisectingKMeans$$updateAssignments$1$$anonfun$2.apply(BisectingKMeans.scala:337)
        at scala.collection.TraversableOnce$$anonfun$minBy$1.apply(TraversableOnce.scala:231)
        at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
        at scala.collection.immutable.List.foldLeft(List.scala:84)
        at scala.collection.LinearSeqOptimized$class.reduceLeft(LinearSeqOptimized.scala:125)
        at scala.collection.immutable.List.reduceLeft(List.scala:84)
        at scala.collection.TraversableOnce$class.minBy(TraversableOnce.scala:231)
        at scala.collection.AbstractTraversable.minBy(Traversable.scala:105)
        at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$org$apache$spark$mllib$clustering$BisectingKMeans$$updateAssignments$1.apply(BisectingKMeans.scala:337)
        at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$org$apache$spark$mllib$clustering$BisectingKMeans$$updateAssignments$1.apply(BisectingKMeans.scala:334)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
        at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:389)

## How was this patch tested?

The dataset was run against the code change to verify that the code works.  I will try to add unit tests to the code.

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: Ilya Matiach <ilmat@microsoft.com>

Closes #16355 from imatiach-msft/ilmat/fix-kmeans.

## What changes were proposed in this pull request?

New implementation of the Pool Adjacent Violators Algorithm (PAVA) in mllib.IsotonicRegression, which used under the hood by ml.regression.IsotonicRegression. The previous implementation could have factorial complexity in the worst case. This implementation, which closely follows those in scikit-learn and the R `iso` package, runs in quadratic time in the worst case.
## How was this patch tested?

Existing unit tests in both `mllib` and `ml` passed before and after this patch. Scaling properties were tested by running the `poolAdjacentViolators` method in [scala-benchmarking-template](https://github.com/sirthias/scala-benchmarking-template) with the input generated by

``` scala
val x = (1 to length).toArray.map(_.toDouble)
val y = x.reverse.zipWithIndex.map{ case (yi, i) => if (i % 2 == 1) yi - 1.5 else yi}
val w = Array.fill(length)(1d)

val input: Array[(Double, Double, Double)] = (y zip x zip w) map{ case ((y, x), w) => (y, x, w)}
```

Before this patch:

| Input Length | Time (us) |
| --: | --: |
| 100 | 1.35 |
| 200 | 3.14 |
| 400 | 116.10 |
| 800 | 2134225.90 |

After this patch:

| Input Length | Time (us) |
| --: | --: |
| 100 | 1.25 |
| 200 | 2.53 |
| 400 | 5.86 |
| 800 | 10.55 |

Benchmarking was also performed with randomly-generated y values, with similar results.

Author: z001qdp <Nicholas.Eggert@target.com>

Closes #15018 from neggert/SPARK-17455-isoreg-algo.

## What changes were proposed in this pull request?

jira: https://issues.apache.org/jira/browse/SPARK-14709

Provide API for SVM algorithm for DataFrames. As discussed in jira, the initial implementation uses OWL-QN with Hinge loss function.
The API should mimic existing spark.ml.classification APIs.
Currently only Binary Classification is supported. Multinomial support can be added in this or following release.
## How was this patch tested?

new unit tests and simple manual test

Author: Yuhao <yuhao.yang@intel.com>
Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #15211 from hhbyyh/mlsvm.

## What changes were proposed in this pull request?

when we append data to a existed partitioned datasource table, the InsertIntoHadoopFsRelationCommand.getCustomPartitionLocations currently
return the same location with Hive default, it should return None.

## How was this patch tested?

Author: windpiger <songjun@outlook.com>

Closes #16642 from windpiger/appendSchema.

## What changes were proposed in this pull request?

Drop more elements when `stageData.taskData.size > retainedTasks` to reduce the number of times on call drop function.

## How was this patch tested?

Jenkins

Author: Yuming Wang <wgyumg@gmail.com>

Closes #16527 from wangyum/SPARK-19146.

## What changes were proposed in this pull request?

This PR aims to fix the following two things.

1. `sql("SET -v").collect()` or `sql("SET -v").show()` raises the following exceptions for String configuration with default value, `null`. For the test, please see [Jenkins result](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/71539/testReport/) and https://github.com/apache/spark/commit/60953bf1f1ba144e709fdae3903a390ff9479fd0 in #16624 .

```
sbt.ForkMain$ForkError: java.lang.RuntimeException: Error while decoding: java.lang.NullPointerException
createexternalrow(input[0, string, false].toString, input[1, string, false].toString, input[2, string, false].toString, StructField(key,StringType,false), StructField(value,StringType,false), StructField(meaning,StringType,false))
:- input[0, string, false].toString
:  +- input[0, string, false]
:- input[1, string, false].toString
:  +- input[1, string, false]
+- input[2, string, false].toString
   +- input[2, string, false]
```

2. Currently, `SET` and `SET -v` commands show unsorted result.
    We had better show a sorted result for UX. Also, this is compatible with Hive.

**BEFORE**
```
scala> sql("set").show(false)
...
|spark.driver.host              |10.22.16.140                                                                                                                                 |
|spark.driver.port              |63893                                                                                                                                        |
|spark.repl.class.uri           |spark://10.22.16.140:63893/classes                                                                                                           |
...
|spark.app.name                 |Spark shell                                                                                                                                  |
|spark.driver.memory            |4G                                                                                                                                           |
|spark.executor.id              |driver                                                                                                                                       |
|spark.submit.deployMode        |client                                                                                                                                       |
|spark.master                   |local[*]                                                                                                                                     |
|spark.home                     |/Users/dhyun/spark                                                                                                                           |
|spark.sql.catalogImplementation|hive                                                                                                                                         |
|spark.app.id                   |local-1484333618945                                                                                                                          |
```

**AFTER**

```
scala> sql("set").show(false)
...
|spark.app.id                   |local-1484333925649                                                                                                                          |
|spark.app.name                 |Spark shell                                                                                                                                  |
|spark.driver.host              |10.22.16.140                                                                                                                                 |
|spark.driver.memory            |4G                                                                                                                                           |
|spark.driver.port              |64994                                                                                                                                        |
|spark.executor.id              |driver                                                                                                                                       |
|spark.jars                     |                                                                                                                                             |
|spark.master                   |local[*]                                                                                                                                     |
|spark.repl.class.uri           |spark://10.22.16.140:64994/classes                                                                                                           |
|spark.sql.catalogImplementation|hive                                                                                                                                         |
|spark.submit.deployMode        |client                                                                                                                                       |
```

## How was this patch tested?

Jenkins with a new test case.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #16579 from dongjoon-hyun/SPARK-19218.

## What changes were proposed in this pull request?
This is a supplement to PR #16516 which did not make the value from `getFamily` case insensitive. Current tests of poisson/binomial glm with weight fail when specifying 'Poisson' or 'Binomial', because the calculation of `dispersion` and `pValue` checks the value of family retrieved from `getFamily`
```
model.getFamily == Binomial.name || model.getFamily == Poisson.name
```

## How was this patch tested?
Update existing tests for 'Poisson' and 'Binomial'.

yanboliang felixcheung imatiach-msft

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #16675 from actuaryzhang/family.

## What changes were proposed in this pull request?

As I pointed out in https://github.com/apache/spark/pull/15807#issuecomment-259143655 , the current subexpression elimination framework has a problem, it always evaluates all common subexpressions at the beginning, even they are inside conditional expressions and may not be accessed.

Ideally we should implement it like scala lazy val, so we only evaluate it when it gets accessed at lease once. https://github.com/apache/spark/issues/15837 tries this approach, but it seems too complicated and may introduce performance regression.

This PR simply stops common subexpression elimination for conditional expressions, with some cleanup.

## How was this patch tested?

regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #16659 from cloud-fan/codegen.

### What changes were proposed in this pull request?
It is weird to create Hive source tables when using InMemoryCatalog. We are unable to operate it. This PR is to block users to create Hive source tables.

### How was this patch tested?
Fixed the test cases

Author: gatorsmile <gatorsmile@gmail.com>

Closes #16587 from gatorsmile/blockHiveTable.

## What changes were proposed in this pull request?

This PR refactors CSV read path to be consistent with JSON data source. It makes the methods in classes have consistent arguments with JSON ones.

`UnivocityParser` and `JacksonParser`

``` scala
private[csv] class UnivocityParser(
    schema: StructType,
    requiredSchema: StructType,
    options: CSVOptions) extends Logging {
  ...

def parse(input: String): Seq[InternalRow] = {
  ...
```

``` scala
class JacksonParser(
    schema: StructType,
    columnNameOfCorruptRecord: String,
    options: JSONOptions) extends Logging {
  ...

def parse(input: String): Option[InternalRow] = {
  ...
```

These allow parsing an iterator (`String` to `InternalRow`) as below for both JSON and CSV:

```scala
iter.flatMap(parser.parse)
```

## How was this patch tested?

Existing tests should cover this.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16669 from HyukjinKwon/SPARK-16101-read.

## What changes were proposed in this pull request?
```spark.gaussianMixture``` supports output total log-likelihood for the model like R ```mvnormalmixEM```.

## How was this patch tested?
R unit test.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #16646 from yanboliang/spark-19291.

## What changes were proposed in this pull request?
MLlib ```GeneralizedLinearRegression``` ```family``` and ```link``` should be case insensitive. This is consistent with some other MLlib params such as [```featureSubsetStrategy```](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala#L415).

## How was this patch tested?
Update corresponding tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #16516 from yanboliang/spark-19133.

## What changes were proposed in this pull request?

After [SPARK-19107](https://issues.apache.org/jira/browse/SPARK-19153), we now can treat hive as a data source and create hive tables with DataFrameWriter and Catalog. However, the support is not completed, there are still some cases we do not support.

this PR provide DataFrameWriter.saveAsTable work with hive format to create partitioned table.

## How was this patch tested?
unit test added

Author: windpiger <songjun@outlook.com>

Closes #16593 from windpiger/saveAsTableWithPartitionedTable.

[SPARK-19117][SPARK-18922][TESTS] Fix the rest of flaky, newly introduced and missed test failures on Windows

## What changes were proposed in this pull request?

**Failed tests**

```
org.apache.spark.sql.hive.execution.HiveQuerySuite:
 - transform with SerDe3 *** FAILED ***
 - transform with SerDe4 *** FAILED ***
```

```
org.apache.spark.sql.hive.execution.HiveDDLSuite:
 - create hive serde table with new syntax *** FAILED ***
 - add/drop partition with location - managed table *** FAILED ***
```

```
org.apache.spark.sql.hive.ParquetMetastoreSuite:
 - Explicitly added partitions should be readable after load *** FAILED ***
 - Non-partitioned table readable after load *** FAILED ***
```

**Aborted tests**

```
Exception encountered when attempting to run a suite with class name: org.apache.spark.sql.hive.execution.HiveSerDeSuite *** ABORTED *** (157 milliseconds)
   org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: C:projectssparksqlhive   argetscala-2.11   est-classesdatafilessales.txt;
```

**Flaky tests(failed 9ish out of 10)**

```
org.apache.spark.scheduler.SparkListenerSuite:
 - local metrics *** FAILED ***
```

## How was this patch tested?

Manually tested via AppVeyor.

**Failed tests**

```
org.apache.spark.sql.hive.execution.HiveQuerySuite:
 - transform with SerDe3 !!! CANCELED !!! (0 milliseconds)
 - transform with SerDe4 !!! CANCELED !!! (0 milliseconds)
```

```
org.apache.spark.sql.hive.execution.HiveDDLSuite:
 - create hive serde table with new syntax (1 second, 672 milliseconds)
 - add/drop partition with location - managed table (2 seconds, 391 milliseconds)
```

```
org.apache.spark.sql.hive.ParquetMetastoreSuite:
 - Explicitly added partitions should be readable after load (609 milliseconds)
 - Non-partitioned table readable after load (344 milliseconds)
```

**Aborted tests**

```
spark.sql.hive.execution.HiveSerDeSuite:
 - Read with RegexSerDe (2 seconds, 142 milliseconds)
 - Read and write with LazySimpleSerDe (tab separated) (2 seconds)
 - Read with AvroSerDe (1 second, 47 milliseconds)
 - Read Partitioned with AvroSerDe (1 second, 422 milliseconds)
```

**Flaky tests (failed 9ish out of 10)**

```
org.apache.spark.scheduler.SparkListenerSuite:
 - local metrics (4 seconds, 562 milliseconds)
```

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16586 from HyukjinKwon/set-path-appveyor.

https://issues.apache.org/jira/browse/SPARK-17724

## What changes were proposed in this pull request?
For unevaluated `\n`, evaluate it and enable line break, for Streaming WebUI `stages` page and `job` page.
(I didn't change Scala source file, since Jetty server has to somehow indicate line break and js to code display it.)
(This PR is a continue from previous PR https://github.com/apache/spark/pull/15353 for the same issue, sorry being so long time)

Two changes:

1. RDD Node tooltipText is actually showing the `<circle>`  `title` property, so I set extra attribute in `spark-dag-viz.js`: `.attr("data-html", "true")`

`<circle x="-5" y="-5" r="5" data-toggle="tooltip" data-placement="bottom" title="" data-original-title="ParallelCollectionRDD [9]\nmakeRDD at QueueStream.scala:49"></circle>`

2. Static `<tspan>` text of each stage, split by `/n`, and append an extra `<tspan>` element to its parentNode

`<text><tspan xml:space="preserve" dy="1em" x="1">reduceByKey</tspan><tspan xml:space="preserve" dy="1em" x="1">reduceByKey/n 23:34:49</tspan></text>
`

## UI changes
Screenshot **before fix**, `\n` is not evaluated in both circle tooltipText and static text:
![screen shot 2017-01-19 at 12 21 54 am](https://cloud.githubusercontent.com/assets/3925641/22098829/53c7f49c-dddd-11e6-9daa-b3ddb6044114.png)

Screenshot **after fix**:
![screen shot 2017-01-19 at 12 20 30 am](https://cloud.githubusercontent.com/assets/3925641/22098806/294910d4-dddd-11e6-9948-d942e09f545e.png)

## How was this patch tested?
Tested locally. For Streaming WebUI `stages` page and `job` page, on multiple browsers:
- Chrome
- Firefox
- Safari

Author: Xin Ren <renxin.ubc@gmail.com>

Closes #16643 from keypointt/SPARK-17724-2nd.

## What changes were proposed in this pull request?

For data source tables, we will always reorder the specified table schema, or the query in CTAS, to put partition columns at the end. e.g. `CREATE TABLE t(a int, b int, c int, d int) USING parquet PARTITIONED BY (d, b)` will create a table with schema `<a, c, d, b>`

Hive serde tables don't have this problem before, because its CREATE TABLE syntax specifies data schema and partition schema individually.

However, after we unifed the CREATE TABLE syntax, Hive serde table also need to do the reorder. This PR puts the reorder logic in a analyzer rule,  which works with both data source tables and Hive serde tables.

## How was this patch tested?

new regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #16655 from cloud-fan/schema.

## What changes were proposed in this pull request?

JDBC  read  is failing with  NPE due to missing null value check for array data type if the source table has null values in the array type column.  For null values Resultset.getArray()  returns null.
This PR adds null safe check to the Resultset.getArray() value before invoking method on the Array object.
## How was this patch tested?

Updated the PostgresIntegration test suite to test null values. Ran docker integration tests on my laptop.

Author: sureshthalamati <suresh.thalamati@gmail.com>

Closes #15192 from sureshthalamati/jdbc_array_null_fix-SPARK-14536.

## What changes were proposed in this pull request?

This PR refactors CSV write path to be consistent with JSON data source.

This PR makes the methods in classes have consistent arguments with JSON ones.
  - `UnivocityGenerator` and `JacksonGenerator`

    ``` scala
    private[csv] class UnivocityGenerator(
        schema: StructType,
        writer: Writer,
        options: CSVOptions = new CSVOptions(Map.empty[String, String])) {
    ...

    def write ...
    def close ...
    def flush ...
    ```

    ``` scala
    private[sql] class JacksonGenerator(
       schema: StructType,
       writer: Writer,
       options: JSONOptions = new JSONOptions(Map.empty[String, String])) {
    ...

    def write ...
    def close ...
    def flush ...
    ```

- This PR also makes the classes put in together in a consistent manner with JSON.
  - `CsvFileFormat`

    ``` scala
    CsvFileFormat
    CsvOutputWriter
    ```

  - `JsonFileFormat`

    ``` scala
    JsonFileFormat
    JsonOutputWriter
    ```

## How was this patch tested?

Existing tests should cover this.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16496 from HyukjinKwon/SPARK-16101-write.

## What changes were proposed in this pull request?

There is a race condition when stopping StateStore which makes `StateStoreSuite.maintenance` flaky. `StateStore.stop` doesn't wait for the running task to finish, and an out-of-date task may fail `doMaintenance` and cancel the new task. Here is a reproducer: https://github.com/zsxwing/spark/commit/dde1b5b106ba034861cf19e16883cfe181faa6f3

This PR adds MaintenanceTask to eliminate the race condition.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #16627 from zsxwing/SPARK-19267.

## What changes were proposed in this pull request?

PythonUDF is unevaluable, which can not be used inside a join condition, currently the optimizer will push a PythonUDF which accessing both side of join into the join condition, then the query will fail to plan.

This PR fix this issue by checking the expression is evaluable  or not before pushing it into Join.

## How was this patch tested?

Add a regression test.

Author: Davies Liu <davies@databricks.com>

Closes #16581 from davies/pyudf_join.

## What changes were proposed in this pull request?

Sort in a streaming plan should be allowed only after a aggregation in complete mode. Currently it is incorrectly allowed when present anywhere in the plan. It gives unpredictable potentially incorrect results.

## How was this patch tested?
New test

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #16662 from tdas/SPARK-19314.

## What changes were proposed in this pull request?

Although Spark history server UI shows task ‘status’ and ‘duration’ fields, it does not expose these fields in the REST API response. For the Spark history server API users, it is not possible to determine task status and duration. Spark history server has access to task status and duration from event log, but it is not exposing these in API. This patch is proposed to expose task ‘status’ and ‘duration’ fields in Spark history server REST API.

## How was this patch tested?

Modified existing test cases in org.apache.spark.deploy.history.HistoryServerSuite.

Author: Parag Chaudhari <paragpc@amazon.com>

Closes #16473 from paragpc/expose_task_status.

## What changes were proposed in this pull request?

In docs/security.md, there is a description as follows.

```
 steps to configure the key-stores and the trust-store for the standalone deployment mode is as
 follows:
 * Generate a keys pair for each node
 * Export the public key of the key pair to a file on each node
 * Import all exported public keys into a single trust-store
```

According to markdown format, the first item should follow a blank line.

## How was this patch tested?

Manually tested.

Following captures are rendered web page before and after fix.

* before
![before](https://cloud.githubusercontent.com/assets/4736016/22136731/b358115c-df19-11e6-8f6c-2f7b65766265.png)

* after
![after](https://cloud.githubusercontent.com/assets/4736016/22136745/c6366ff8-df19-11e6-840d-e7e894218f9c.png)

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

Closes #16653 from sarutak/SPARK-19302.

## What changes were proposed in this pull request?

Change non-cbo estimation behavior of aggregate:
- If groupExpression is empty, we can know row count (=1) and the corresponding size;
- otherwise, estimation falls back to UnaryNode's computeStats method, which should not propagate rowCount and attributeStats in Statistics because they are not estimated in that method.

## How was this patch tested?

Added test case

Author: wangzhenhua <wangzhenhua@huawei.com>

Closes #16631 from wzhfy/aggNoCbo.

## What changes were proposed in this pull request?

When we query a table with a filter on partitioned columns, we will push the partition filter to the metastore to get matched partitions directly.

In `HiveExternalCatalog.listPartitionsByFilter`, we assume the column names in partition filter are already normalized and we don't need to consider case sensitivity. However, `HiveTableScanExec` doesn't follow this assumption. This PR fixes it.

## How was this patch tested?

new regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #16647 from cloud-fan/bug.

## What changes were proposed in this pull request?

This PR refactors the code generation part to get data from `ColumnarVector` and `ColumnarBatch` by using a trait `ColumnarBatchScan` for ease of reuse. This is because this part will be reused by several components (e.g. parquet reader, Dataset.cache, and others) since `ColumnarBatch` will be first citizen.

This PR is a part of https://github.com/apache/spark/pull/15219. In advance, this PR makes the code generation for  `ColumnarVector` and `ColumnarBatch` reuseable as a trait. In general, this is very useful for other components from the reuseability view, too.
## How was this patch tested?

tested existing test suites

Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>

Closes #15467 from kiszk/columnarrefactor.

[SPARK-19295][SQL] IsolatedClientLoader's downloadVersion should log the location of downloaded metastore client jars

## What changes were proposed in this pull request?
This will help the users to know the location of those downloaded jars when `spark.sql.hive.metastore.jars` is set to `maven`.

## How was this patch tested?
jenkins

Author: Yin Huai <yhuai@databricks.com>

Closes #16649 from yhuai/SPARK-19295.

Builds on top of work in SPARK-8425 to update Application Level Blacklisting in the scheduler.

## What changes were proposed in this pull request?

Adds a UI to these patches by:
- defining new listener events for blacklisting and unblacklisting, nodes and executors;
- sending said events at the relevant points in BlacklistTracker;
- adding JSON (de)serialization code for these events;
- augmenting the Executors UI page to show which, and how many, executors are blacklisted;
- adding a unit test to make sure events are being fired;
- adding HistoryServerSuite coverage to verify that the SHS reads these events correctly.
- updates the Executor UI to show Blacklisted/Active/Dead as a tri-state in Executors Status

Updates .rat-excludes to pass tests.

username squito

## How was this patch tested?

./dev/run-tests
testOnly org.apache.spark.util.JsonProtocolSuite
testOnly org.apache.spark.scheduler.BlacklistTrackerSuite
testOnly org.apache.spark.deploy.history.HistoryServerSuite
https://github.com/jsoltren/jose-utils/blob/master/blacklist/test-blacklist.sh
![blacklist-20161219](https://cloud.githubusercontent.com/assets/1208477/21335321/9eda320a-c623-11e6-8b8c-9c912a73c276.jpg)

Author: José Hiram Soltren <jose@cloudera.com>

Closes #16346 from jsoltren/SPARK-16654-submit.

## What changes were proposed in this pull request?
The initial shouldFilterOut() method invocation filter the root path name(table name in the intial call) and remove if it contains _. I moved the check one level below, so it first list files/directories in the given root path and then apply filter.
(Please fill in changes proposed in this fix)

## How was this patch tested?
Added new test case for this scenario
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: jayadevanmurali <jayadevan.m@tcs.com>
Author: jayadevan <jayadevan.m@tcs.com>

Closes #16635 from jayadevanmurali/branch-0.1-SPARK-19059.

## What changes were proposed in this pull request?

add loglikelihood in GMM.summary

## How was this patch tested?

added tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>
Author: Ruifeng Zheng <ruifengz@foxmail.com>

Closes #12064 from zhengruifeng/gmm_metric.

## What changes were proposed in this pull request?

We have a table relation plan cache in `HiveMetastoreCatalog`, which caches a lot of things: file status, resolved data source, inferred schema, etc.

However, it doesn't make sense to limit this cache with hive support, we should move it to SQL core module so that users can use this cache without hive support.

It can also reduce the size of `HiveMetastoreCatalog`, so that it's easier to remove it eventually.

main changes:
1. move the table relation cache to `SessionCatalog`
2. `SessionCatalog.lookupRelation` will return `SimpleCatalogRelation` and the analyzer will convert it to `LogicalRelation` or `MetastoreRelation` later, then `HiveSessionCatalog` doesn't need to override `lookupRelation` anymore
3. `FindDataSourceTable` will read/write the table relation cache.

## How was this patch tested?

existing tests.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #16621 from cloud-fan/plan-cache.

## What changes were proposed in this pull request?
Update known_translations per https://github.com/apache/spark/pull/16423#issuecomment-269739634

Author: Yin Huai <yhuai@databricks.com>

Closes #16628 from yhuai/known_translations.

[SPARK-14975][ML] Fixed GBTClassifier to predict probability per training instance and fixed interfaces

## What changes were proposed in this pull request?

For all of the classifiers in MLLib we can predict probabilities except for GBTClassifier.
Also, all classifiers inherit from ProbabilisticClassifier but GBTClassifier strangely inherits from Predictor, which is a bug.
This change corrects the interface and adds the ability for the classifier to give a probabilities vector.

## How was this patch tested?

The basic ML tests were run after making the changes.  I've marked this as WIP as I need to add more tests.

Author: Ilya Matiach <ilmat@microsoft.com>

Closes #16441 from imatiach-msft/ilmat/fix-GBT.

[SPARK-19182][DSTREAM] Optimize the lock in StreamingJobProgressListener to not block UI when generating Streaming jobs

## What changes were proposed in this pull request?

When DStreamGraph is generating a job, it will hold a lock and block other APIs. Because StreamingJobProgressListener (numInactiveReceivers, streamName(streamId: Int), streamIds) needs to call DStreamGraph's methods to access some information, the UI may hang if generating a job is very slow (e.g., talking to the slow Kafka cluster to fetch metadata).
It's better to optimize the locks in DStreamGraph and StreamingJobProgressListener to make the UI not block by job generation.

## How was this patch tested?
existing ut

cc zsxwing

Author: uncleGen <hustyugm@gmail.com>

Closes #16601 from uncleGen/SPARK-19182.