## What changes were proposed in this pull request?
Supporting moving tables across different database in HiveClient `alterTable`

## How was this patch tested?
N/A

Author: gatorsmile <gatorsmile@gmail.com>

Closes #19104 from gatorsmile/alterTable.

[SPARK-14280][BUILD][WIP] Update change-version.sh and pom.xml to add Scala 2.12 profiles and enable 2.12 compilation

…build; fix some things that will be warnings or errors in 2.12; restore Scala 2.12 profile infrastructure

## What changes were proposed in this pull request?

This change adds back the infrastructure for a Scala 2.12 build, but does not enable it in the release or Python test scripts.

In order to make that meaningful, it also resolves compile errors that the code hits in 2.12 only, in a way that still works with 2.11.

It also updates dependencies to the earliest minor release of dependencies whose current version does not yet support Scala 2.12. This is in a sense covered by other JIRAs under the main umbrella, but implemented here. The versions below still work with 2.11, and are the _latest_ maintenance release in the _earliest_ viable minor release.

- Scalatest 2.x -> 3.0.3
- Chill 0.8.0 -> 0.8.4
- Clapper 1.0.x -> 1.1.2
- json4s 3.2.x -> 3.4.2
- Jackson 2.6.x -> 2.7.9 (required by json4s)

This change does _not_ fully enable a Scala 2.12 build:

- It will also require dropping support for Kafka before 0.10. Easy enough, just didn't do it yet here
- It will require recreating `SparkILoop` and `Main` for REPL 2.12, which is SPARK-14650. Possible to do here too.

What it does do is make changes that resolve much of the remaining gap without affecting the current 2.11 build.

## How was this patch tested?

Existing tests and build. Manually tested with `./dev/change-scala-version.sh 2.12` to verify it compiles, modulo the exceptions above.

Author: Sean Owen <sowen@cloudera.com>

Closes #18645 from srowen/SPARK-14280.

## What changes were proposed in this pull request?
As shown below, for example, When the job 5 is running, It was a mistake to think that five jobs were running, So I think it would be more appropriate to change jobs to job id.
![image](https://user-images.githubusercontent.com/21355020/29909612-4dc85064-8e59-11e7-87cd-275a869243bb.png)

## How was this patch tested?
no need

Author: he.qiao <he.qiao17@zte.com.cn>

Closes #19093 from Geek-He/08_31_sqltable.

## What changes were proposed in this pull request?

This PR make `DataFrame.sample(...)` can omit `withReplacement` defaulting `False`, consistently with equivalent Scala / Java API.

In short, the following examples are allowed:

```python
>>> df = spark.range(10)
>>> df.sample(0.5).count()
7
>>> df.sample(fraction=0.5).count()
3
>>> df.sample(0.5, seed=42).count()
5
>>> df.sample(fraction=0.5, seed=42).count()
5
```

In addition, this PR also adds some type checking logics as below:

```python
>>> df = spark.range(10)
>>> df.sample().count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [].
>>> df.sample(True).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'bool'>].
>>> df.sample(42).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'int'>].
>>> df.sample(fraction=False, seed="a").count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'bool'>, <type 'str'>].
>>> df.sample(seed=[1]).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'list'>].
>>> df.sample(withReplacement="a", fraction=0.5, seed=1)
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'str'>, <type 'float'>, <type 'int'>].
```

## How was this patch tested?

Manually tested, unit tests added in doc tests and manually checked the built documentation for Python.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18999 from HyukjinKwon/SPARK-21779.

## What changes were proposed in this pull request?

Allows `BinaryComparison` operators to work on any data type that actually supports ordering as verified by `TypeUtils.checkForOrderingExpr` instead of relying on the incomplete list `TypeCollection.Ordered` (which is removed by this PR).

## How was this patch tested?

Updated unit tests to cover structs and arrays.

Author: Andrew Ray <ray.andrew@gmail.com>

Closes #18818 from aray/SPARK-21110.

## What changes were proposed in this pull request?
Also remove useless function `partitionByDeterministic` after the changes of https://github.com/apache/spark/pull/14687

## How was this patch tested?
N/A

Author: gatorsmile <gatorsmile@gmail.com>

Closes #19097 from gatorsmile/followupSPARK-17107.

Removing a check in the ColumnarBatchSuite that depended on a Java assertion.  This assertion is being compiled out in the Maven builds causing the test to fail.  This part of the test is not specifically from to the functionality that is being tested here.

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #19098 from BryanCutler/hotfix-ColumnarBatchSuite-assertion.

… Dataset with LogicalRDD logical operator

## What changes were proposed in this pull request?

Reusing `SparkSession.internalCreateDataFrame` wherever possible (to cut dups)

## How was this patch tested?

Local build and waiting for Jenkins

Author: Jacek Laskowski <jacek@japila.pl>

Closes #19095 from jaceklaskowski/SPARK-21886-internalCreateDataFrame.

## What changes were proposed in this pull request?
Creates `SQLMetricsTestUtils` for the utility functions of both Hive-specific and the other SQLMetrics test cases.

Also, move two SQLMetrics test cases from sql/hive to sql/core.

## How was this patch tested?
N/A

Author: gatorsmile <gatorsmile@gmail.com>

Closes #19092 from gatorsmile/rewriteSQLMetrics.

## What changes were proposed in this pull request?

This PR allows the creation of a `ColumnarBatch` from `ReadOnlyColumnVectors` where previously a columnar batch could only allocate vectors internally.  This is useful for using `ArrowColumnVectors` in a batch form to do row-based iteration.  Also added `ArrowConverter.fromPayloadIterator` which converts `ArrowPayload` iterator to `InternalRow` iterator and uses a `ColumnarBatch` internally.

## How was this patch tested?

Added a new unit test for creating a `ColumnarBatch` with `ReadOnlyColumnVectors` and a test to verify the roundtrip of rows -> ArrowPayload -> rows, using `toPayloadIterator` and `fromPayloadIterator`.

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #18787 from BryanCutler/arrow-ColumnarBatch-support-SPARK-21583.

## What changes were proposed in this pull request?

Fix Java code style so `./dev/lint-java` succeeds

## How was this patch tested?

Run `./dev/lint-java`

Author: Andrew Ash <andrew@andrewash.com>

Closes #19088 from ash211/spark-21875-lint-java.

## What changes were proposed in this pull request?

This PR aims to support `spark.sql.orc.compression.codec` like Parquet's `spark.sql.parquet.compression.codec`. Users can use SQLConf to control ORC compression, too.

## How was this patch tested?

Pass the Jenkins with new and updated test cases.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #19055 from dongjoon-hyun/SPARK-21839.

## What changes were proposed in this pull request?

igore("shuffle hash join") is to shuffle hash join to test _case class ShuffledHashJoinExec_.
But when you 'ignore' -> 'test', the test is _case class BroadcastHashJoinExec_.

Before modified,  as a result of：canBroadcast is true.
Print information in _canBroadcast(plan: LogicalPlan)_
```
canBroadcast plan.stats.sizeInBytes:6710880
canBroadcast conf.autoBroadcastJoinThreshold:10000000
```

After modified, plan.stats.sizeInBytes is 11184808.
Print information in _canBuildLocalHashMap(plan: LogicalPlan)_
and _muchSmaller(a: LogicalPlan, b: LogicalPlan)_ :

```
canBuildLocalHashMap plan.stats.sizeInBytes:11184808
canBuildLocalHashMap conf.autoBroadcastJoinThreshold:10000000
canBuildLocalHashMap conf.numShufflePartitions:2
```
```
muchSmaller a.stats.sizeInBytes * 3:33554424
muchSmaller b.stats.sizeInBytes:33554432
```
## How was this patch tested?

existing test case.

Author: caoxuewen <cao.xuewen@zte.com.cn>

Closes #19069 from heary-cao/shuffle_hash_join.

This reverts commit 3d0e1742.

## What changes were proposed in this pull request?

`org.apache.spark.deploy.RPackageUtilsSuite`

```
 - jars without manifest return false *** FAILED *** (109 milliseconds)
   java.io.IOException: Unable to delete file: C:\projects\spark\target\tmp\1500266936418-0\dep1-c.jar
```

`org.apache.spark.deploy.SparkSubmitSuite`

```
 - download one file to local *** FAILED *** (16 milliseconds)
   java.net.URISyntaxException: Illegal character in authority at index 6: s3a://C:\projects\spark\target\tmp\test2630198944759847458.jar

 - download list of files to local *** FAILED *** (0 milliseconds)
   java.net.URISyntaxException: Illegal character in authority at index 6: s3a://C:\projects\spark\target\tmp\test2783551769392880031.jar
```

`org.apache.spark.scheduler.ReplayListenerSuite`

```
 - Replay compressed inprogress log file succeeding on partial read (156 milliseconds)
   Exception encountered when attempting to run a suite with class name:
   org.apache.spark.scheduler.ReplayListenerSuite *** ABORTED *** (1 second, 391 milliseconds)
   java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-8f3cacd6-faad-4121-b901-ba1bba8025a0

 - End-to-end replay *** FAILED *** (62 milliseconds)
   java.io.IOException: No FileSystem for scheme: C

 - End-to-end replay with compression *** FAILED *** (110 milliseconds)
   java.io.IOException: No FileSystem for scheme: C
```

`org.apache.spark.sql.hive.StatisticsSuite`

```
 - SPARK-21079 - analyze table with location different than that of individual partitions *** FAILED *** (875 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);

 - SPARK-21079 - analyze partitioned table with only a subset of partitions visible *** FAILED *** (47 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```

**Note:** this PR does not fix:

`org.apache.spark.deploy.SparkSubmitSuite`

```
 - launch simple application with spark-submit with redaction *** FAILED *** (172 milliseconds)
   java.util.NoSuchElementException: next on empty iterator
```

I can't reproduce this on my Windows machine but looks appearntly consistently failed on AppVeyor. This one is unclear to me yet and hard to debug so I did not include this one for now.

**Note:** it looks there are more instances but it is hard to identify them partly due to flakiness and partly due to swarming logs and errors. Will probably go one more time if it is fine.

## How was this patch tested?

Manually via AppVeyor:

**Before**

- `org.apache.spark.deploy.RPackageUtilsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/8t8ra3lrljuir7q4
- `org.apache.spark.deploy.SparkSubmitSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/taquy84yudjjen64
- `org.apache.spark.scheduler.ReplayListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/24omrfn2k0xfa9xq
- `org.apache.spark.sql.hive.StatisticsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/2079y1plgj76dc9l

**After**

- `org.apache.spark.deploy.RPackageUtilsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/3803dbfn89ne1164
- `org.apache.spark.deploy.SparkSubmitSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/m5l350dp7u9a4xjr
- `org.apache.spark.scheduler.ReplayListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/565vf74pp6bfdk18
- `org.apache.spark.sql.hive.StatisticsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/qm78tsk8c37jb6s4

Jenkins tests are required and AppVeyor tests will be triggered.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18971 from HyukjinKwon/windows-fixes.

## What changes were proposed in this pull request?
We should make codegen fallback of expressions configurable. So far, it is always on. We might hide it when our codegen have compilation bugs. Thus, we should also disable the codegen fallback when running test cases.

## How was this patch tested?
Added test cases

Author: gatorsmile <gatorsmile@gmail.com>

Closes #19062 from gatorsmile/fallbackCodegen.

## What changes were proposed in this pull request?

This is a follow-up for https://github.com/apache/spark/pull/18488, to simplify the code.

The major change is, we should map java enum to string type, instead of a struct type with a single string field.

## How was this patch tested?

existing tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #19066 from cloud-fan/fix.

## What changes were proposed in this pull request?

Add trait UserDefinedExpression to identify user-defined functions.
UDF can be expensive. In optimizer we may need to avoid executing UDF multiple times.
E.g.
```scala
table.select(UDF as 'a).select('a, ('a + 1) as 'b)
```
If UDF is expensive in this case, optimizer should not collapse the project to
```scala
table.select(UDF as 'a, (UDF+1) as 'b)
```

Currently UDF classes like PythonUDF, HiveGenericUDF are not defined in catalyst.
This PR is to add a new trait to make it easier to identify user-defined functions.

## How was this patch tested?

Unit test

Author: Wang Gengliang <ltnwgl@gmail.com>

Closes #19064 from gengliangwang/UDFType.

## What changes were proposed in this pull request?

As mentioned at https://github.com/apache/spark/pull/18680#issuecomment-316820409, when we have more `ColumnVector` implementations, it might (or might not) have huge performance implications because it might disable inlining, or force virtual dispatches.

As for read path, one of the major paths is the one generated by `ColumnBatchScan`. Currently it refers `ColumnVector` so the penalty will be bigger as we have more classes, but we can know the concrete type from its usage, e.g. vectorized Parquet reader uses `OnHeapColumnVector`. We can use the concrete type in the generated code directly to avoid the penalty.

## How was this patch tested?

Existing tests.

Author: Takuya UESHIN <ueshin@databricks.com>

Closes #18989 from ueshin/issues/SPARK-21781.

Signed-off-by: iamhumanbeing <iamhumanbeinggmail.com>

## What changes were proposed in this pull request?

testNameNote = "(minNumPostShufflePartitions: 3) is not correct.
it should be "(minNumPostShufflePartitions: " + numPartitions + ")" in ExchangeCoordinatorSuite

## How was this patch tested?

unit tests

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

Author: iamhumanbeing <iamhumanbeing@gmail.com>

Closes #19058 from iamhumanbeing/testnote.

## What changes were proposed in this pull request?

This PR proposes both:

- Add information about Javadoc, SQL docs and few more information in `docs/README.md` and a comment in `docs/_plugins/copy_api_dirs.rb` related with Javadoc.

- Adds some commands so that the script always runs the SQL docs build under `./sql` directory (for directly running `./sql/create-docs.sh` in the root directory).

## How was this patch tested?

Manual tests with `jekyll build` and `./sql/create-docs.sh` in the root directory.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #19019 from HyukjinKwon/minor-doc-build.

## What changes were proposed in this pull request?

[SPARK-19025](https://github.com/apache/spark/pull/16869) removes SQLBuilder, so we don't need the following in HiveCompatibilitySuite.

```scala
// Ensures that the plans generation use metastore relation and not OrcRelation
// Was done because SqlBuilder does not work with plans having logical relation
TestHive.setConf(HiveUtils.CONVERT_METASTORE_ORC, false)
```

## How was this patch tested?

Pass the existing Jenkins tests.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #19043 from dongjoon-hyun/SPARK-21831.

## What changes were proposed in this pull request?

Adjust Local UDTs test to assert about results, and fix index of vector column. See JIRA for details.

## How was this patch tested?

Existing tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #19053 from srowen/SPARK-21837.

## What changes were proposed in this pull request?

This patch adds allowUnquotedControlChars option in JSON data source to allow JSON Strings to contain unquoted control characters (ASCII characters with value less than 32, including tab and line feed characters)

## How was this patch tested?
Add new test cases

Author: vinodkc <vinod.kc.in@gmail.com>

Closes #19008 from vinodkc/br_fix_SPARK-21756.

## What changes were proposed in this pull request?

After [SPARK-19025](https://github.com/apache/spark/pull/16869), there is no need to keep SQLBuilderTest.
ExpressionSQLBuilderSuite is the only place to use it.
This PR aims to remove SQLBuilderTest.

## How was this patch tested?

Pass the updated `ExpressionSQLBuilderSuite`.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #19044 from dongjoon-hyun/SPARK-21832.

## What changes were proposed in this pull request?

Fix build warnings and Java lint errors. This just helps a bit in evaluating (new) warnings in another PR I have open.

## How was this patch tested?

Existing tests

Author: Sean Owen <sowen@cloudera.com>

Closes #19051 from srowen/JavaWarnings.

## What changes were proposed in this pull request?

Fixed NPE when creating encoder for enum.

When you try to create an encoder for Enum type (or bean with enum property) via Encoders.bean(...), it fails with NullPointerException at TypeToken:495.
I did a little research and it turns out, that in JavaTypeInference following code
```
  def getJavaBeanReadableProperties(beanClass: Class[_]): Array[PropertyDescriptor] = {
    val beanInfo = Introspector.getBeanInfo(beanClass)
    beanInfo.getPropertyDescriptors.filterNot(_.getName == "class")
      .filter(_.getReadMethod != null)
  }
```
filters out properties named "class", because we wouldn't want to serialize that. But enum types have another property of type Class named "declaringClass", which we are trying to inspect recursively. Eventually we try to inspect ClassLoader class, which has property "defaultAssertionStatus" with no read method, which leads to NPE at TypeToken:495.

I added property name "declaringClass" to filtering to resolve this.

## How was this patch tested?
Unit test in JavaDatasetSuite which creates an encoder for enum

Author: mike <mike0sv@gmail.com>
Author: Mikhail Sveshnikov <mike0sv@gmail.com>

Closes #18488 from mike0sv/enum-support.

## What changes were proposed in this pull request?
This PR bumps the ANTLR version to 4.7, and fixes a number of small parser related issues uncovered by the bump.

The main reason for upgrading is that in some cases the current version of ANTLR (4.5) can exhibit exponential slowdowns if it needs to parse boolean predicates. For example the following query will take forever to parse:
```sql
SELECT *
FROM RANGE(1000)
WHERE
TRUE
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
```

This is caused by a know bug in ANTLR (https://github.com/antlr/antlr4/issues/994), which was fixed in version 4.6.

## How was this patch tested?
Existing tests.

Author: Herman van Hovell <hvanhovell@databricks.com>

Closes #19042 from hvanhovell/SPARK-21830.

## What changes were proposed in this pull request?

Add more cases we should view as a normal query stop rather than a failure.

## How was this patch tested?

The new unit tests.

Author: Shixiong Zhu <zsxwing@gmail.com>

Closes #18997 from zsxwing/SPARK-21788.

## What changes were proposed in this pull request?

This is a bug introduced by https://github.com/apache/spark/pull/11274/files#diff-7adb688cbfa583b5711801f196a074bbL274 .

Non-equal join condition should only be applied when the equal-join condition matches.

## How was this patch tested?

regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #19036 from cloud-fan/bug.

[SPARK-21759][SQL] In.checkInputDataTypes should not wrongly report unresolved plans for IN correlated subquery

## What changes were proposed in this pull request?

With the check for structural integrity proposed in SPARK-21726, it is found that the optimization rule `PullupCorrelatedPredicates` can produce unresolved plans.

For a correlated IN query looks like:

    SELECT t1.a FROM t1
    WHERE
    t1.a IN (SELECT t2.c
            FROM t2
            WHERE t1.b < t2.d);

The query plan might look like:

    Project [a#0]
    +- Filter a#0 IN (list#4 [b#1])
       :  +- Project [c#2]
       :     +- Filter (outer(b#1) < d#3)
       :        +- LocalRelation <empty>, [c#2, d#3]
       +- LocalRelation <empty>, [a#0, b#1]

After `PullupCorrelatedPredicates`, it produces query plan like:

    'Project [a#0]
    +- 'Filter a#0 IN (list#4 [(b#1 < d#3)])
       :  +- Project [c#2, d#3]
       :     +- LocalRelation <empty>, [c#2, d#3]
       +- LocalRelation <empty>, [a#0, b#1]

Because the correlated predicate involves another attribute `d#3` in subquery, it has been pulled out and added into the `Project` on the top of the subquery.

When `list` in `In` contains just one `ListQuery`, `In.checkInputDataTypes` checks if the size of `value` expressions matches the output size of subquery. In the above example, there is only `value` expression and the subquery output has two attributes `c#2, d#3`, so it fails the check and `In.resolved` returns `false`.

We should not let `In.checkInputDataTypes` wrongly report unresolved plans to fail the structural integrity check.

## How was this patch tested?

Added test.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #18968 from viirya/SPARK-21759.

[SPARK-21745][SQL] Refactor ColumnVector hierarchy to make ColumnVector read-only and to introduce WritableColumnVector.

## What changes were proposed in this pull request?

This is a refactoring of `ColumnVector` hierarchy and related classes.

1. make `ColumnVector` read-only
2. introduce `WritableColumnVector` with write interface
3. remove `ReadOnlyColumnVector`

## How was this patch tested?

Existing tests.

Author: Takuya UESHIN <ueshin@databricks.com>

Closes #18958 from ueshin/issues/SPARK-21745.

## What changes were proposed in this pull request?

When json_tuple in extracting values from JSON it returns null values within repeated columns except the first one as below:

``` scala
scala> spark.sql("""SELECT json_tuple('{"a":1, "b":2}', 'a', 'b', 'a')""").show()
+---+---+----+
| c0| c1|  c2|
+---+---+----+
|  1|  2|null|
+---+---+----+
```

I think this should be consistent with Hive's implementation:
```
hive> SELECT json_tuple('{"a": 1, "b": 2}', 'a', 'a');
...
1    1
```

In this PR, we located all the matched indices in `fieldNames` instead of returning the first matched index, i.e., indexOf.

## How was this patch tested?

Added test in JsonExpressionsSuite.

Author: Jen-Ming Chung <jenmingisme@gmail.com>

Closes #19017 from jmchung/SPARK-21804.

## What changes were proposed in this pull request?

The given example in the comment of Class ExchangeCoordinator is exist four post-shuffle partitions,but the current comment is “three”.

## How was this patch tested?

Author: lufei <lu.fei80@zte.com.cn>

Closes #19028 from figo77/SPARK-21816.

## What changes were proposed in this pull request?
The getAliasedConstraints  fuction in LogicalPlan.scala will clone the expression set when an element added,
and it will take a long time. This PR add a function to add multiple elements at once to reduce the clone time.

Before modified, the cost of getAliasedConstraints is:
100 expressions:  41 seconds
150 expressions:  466 seconds

After modified, the cost of getAliasedConstraints is:
100 expressions:  1.8 seconds
150 expressions:  6.5 seconds

The test is like this:
test("getAliasedConstraints") {
    val expressionNum = 150
    val aggExpression = (1 to expressionNum).map(i => Alias(Count(Literal(1)), s"cnt$i")())
    val aggPlan = Aggregate(Nil, aggExpression, LocalRelation())

    val beginTime = System.currentTimeMillis()
    val expressions = aggPlan.validConstraints
    println(s"validConstraints cost: ${System.currentTimeMillis() - beginTime}ms")
    // The size of Aliased expression is n * (n - 1) / 2 + n
    assert( expressions.size === expressionNum * (expressionNum - 1) / 2 + expressionNum)
  }

(Please fill in changes proposed in this fix)

## How was this patch tested?

(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)

Run new added test.

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

Author: 10129659 <chen.yanshan@zte.com.cn>

Closes #19022 from eatoncys/getAliasedConstraints.

## What changes were proposed in this pull request?
This pr changed the default value of `maxLinesPerFunction` into `4000`. In #18810, we had this new option to disable code generation for too long functions and I found this option only affected `Q17` and `Q66` in TPC-DS. But, `Q66` had some performance regression:

```
Q17 w/o #18810, 3224ms --> q17 w/#18810, 2627ms (improvement)
Q66 w/o #18810, 1712ms --> q66 w/#18810, 3032ms (regression)
```

To keep the previous performance in TPC-DS, we better set higher value at `maxLinesPerFunction` by default.

## How was this patch tested?
Existing tests.

Author: Takeshi Yamamuro <yamamuro@apache.org>

Closes #19021 from maropu/SPARK-21603-FOLLOWUP-1.

## What changes were proposed in this pull request?
All streaming logical plans will now have isStreaming set. This involved adding isStreaming as a case class arg in a few cases, since a node might be logically streaming depending on where it came from.

## How was this patch tested?

Existing unit tests - no functional change is intended in this PR.

Author: Jose Torres <joseph-torres@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #18973 from joseph-torres/SPARK-21765.

[SPARK-21769][SQL] Add a table-specific option for always respecting schemas inferred/controlled by Spark SQL

## What changes were proposed in this pull request?
For Hive-serde tables, we always respect the schema stored in Hive metastore, because the schema could be altered by the other engines that share the same metastore. Thus, we always trust the metastore-controlled schema for Hive-serde tables when the schemas are different (without considering the nullability and cases). However, in some scenarios, Hive metastore also could INCORRECTLY overwrite the schemas when the serde and Hive metastore built-in serde are different.

The proposed solution is to introduce a table-specific option for such scenarios. For a specific table, users can make Spark always respect Spark-inferred/controlled schema instead of trusting metastore-controlled schema. By default, we trust Hive metastore-controlled schema.

## How was this patch tested?
Added a cross-version test case

Author: gatorsmile <gatorsmile@gmail.com>

Closes #19003 from gatorsmile/respectSparkSchema.

[SPARK-21499][SQL] Support creating persistent function for Spark UDAF(UserDefinedAggregateFunction)

## What changes were proposed in this pull request?
This PR is to enable users to create persistent Scala UDAF (that extends UserDefinedAggregateFunction).

```SQL
CREATE FUNCTION myDoubleAvg AS 'test.org.apache.spark.sql.MyDoubleAvg'
```

Before this PR, Spark UDAF only can be registered through the API `spark.udf.register(...)`

## How was this patch tested?
Added test cases

Author: gatorsmile <gatorsmile@gmail.com>

Closes #18700 from gatorsmile/javaUDFinScala.

## What changes were proposed in this pull request?
We do not have any Hive-specific parser. It does not make sense to keep a parser-specific test suite `HiveDDLCommandSuite.scala` in the Hive package. This PR is to remove it.

## How was this patch tested?
N/A

Author: gatorsmile <gatorsmile@gmail.com>

Closes #19015 from gatorsmile/combineDDL.