Follow up to https://github.com/apache/spark/commit/ae853e8f3bdbd16427e6f1ffade4f63abaf74abb as `mv` throws an error on the Jenkins machines if source and destinations are the same.

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #16302 from shivaram/sparkr-no-mv-fix.

## What changes were proposed in this pull request?

For release builds the R_PACKAGE_VERSION and VERSION are the same (e.g., 2.1.0). Thus `cp` throws an error which causes the build to fail.

## How was this patch tested?

Manually by executing the following script
```
set -o pipefail
set -e
set -x

touch a

R_PACKAGE_VERSION=2.1.0
VERSION=2.1.0

if [ "$R_PACKAGE_VERSION" != "$VERSION" ]; then
  cp a a
fi
```

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #16299 from shivaram/sparkr-cp-fix.

## What changes were proposed in this pull request?

Use `recentProgress` instead of `lastProgress` and filter out last non-zero value. Also add eventually to the latest assertQuery similar to first `assertQuery`

## How was this patch tested?

Ran test 1000 times

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #16287 from brkyvz/SPARK-18868.

## What changes were proposed in this pull request?

https://github.com/apache/spark/commit/93cdb8a7d0f124b4db069fd8242207c82e263c52 Introduced a compile error under scala 2.10, this fixes that error.

## How was this patch tested?

locally ran
```
dev/change-version-to-2.10.sh
build/sbt -Pyarn -Phadoop-2.4 -Dhadoop.version=2.6.0 -Dscala-2.10 "project yarn" "test-only *YarnAllocatorSuite"
```
(which failed at test compilation before this change)

Author: Imran Rashid <irashid@cloudera.com>

Closes #16298 from squito/blacklist-2.10.

## What changes were proposed in this pull request?

`_to_seq` wasn't imported.

## How was this patch tested?

Added partitionBy to existing write path unit test

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #16297 from brkyvz/SPARK-18888.

## What changes were proposed in this pull request?

When starting a stream with a lot of backfill and maxFilesPerTrigger, the user could often want to start with most recent files first. This would let you keep low latency for recent data and slowly backfill historical data.

This PR adds a new option `latestFirst` to control this behavior. When it's true, `FileStreamSource` will sort the files by the modified time from latest to oldest, and take the first `maxFilesPerTrigger` files as a new batch.

## How was this patch tested?

The added test.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #16251 from zsxwing/newest-first.

## What changes were proposed in this pull request?

Check whether Aggregation operators on a streaming subplan have aggregate expressions with isDistinct = true.

## How was this patch tested?

Added unit test

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

Closes #16289 from tdas/SPARK-18870.

## What changes were proposed in this pull request?

Right now, once a user set the comment of a column with create table command, he/she cannot update the comment. It will be useful to provide a public interface (e.g. SQL) to do that.

This PR implements the following SQL statement:
```
ALTER TABLE table [PARTITION partition_spec]
CHANGE [COLUMN] column_old_name column_new_name column_dataType
[COMMENT column_comment]
[FIRST | AFTER column_name];
```

For further expansion, we could support alter `name`/`dataType`/`index` of a column too.

## How was this patch tested?

Add new test cases in `ExternalCatalogSuite` and `SessionCatalogSuite`.
Add sql file test for `ALTER TABLE CHANGE COLUMN` statement.

Author: jiangxingbo <jiangxb1987@gmail.com>

Closes #15717 from jiangxb1987/change-column.

## What changes were proposed in this pull request?

This builds upon the blacklisting introduced in SPARK-17675 to add blacklisting of executors and nodes for an entire Spark application.  Resources are blacklisted based on tasks that fail, in tasksets that eventually complete successfully; they are automatically returned to the pool of active resources based on a timeout.  Full details are available in a design doc attached to the jira.
## How was this patch tested?

Added unit tests, ran them via Jenkins, also ran a handful of them in a loop to check for flakiness.

The added tests include:
- verifying BlacklistTracker works correctly
- verifying TaskSchedulerImpl interacts with BlacklistTracker correctly (via a mock BlacklistTracker)
- an integration test for the entire scheduler with blacklisting in a few different scenarios

Author: Imran Rashid <irashid@cloudera.com>
Author: mwws <wei.mao@intel.com>

Closes #14079 from squito/blacklist-SPARK-8425.

## What changes were proposed in this pull request?

doc cleanup

## How was this patch tested?

~~vignettes is not building for me. I'm going to kick off a full clean build and try again and attach output here for review.~~
Output html here: https://felixcheung.github.io/sparkr-vignettes.html

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #16286 from felixcheung/rvignettespass.

## What changes were proposed in this pull request?

Since Apache Spark 1.4.0, R API document page has a broken link on `DESCRIPTION file` because Jekyll plugin script doesn't copy the file. This PR aims to fix that.

- Official Latest Website: http://spark.apache.org/docs/latest/api/R/index.html
- Apache Spark 2.1.0-rc2: http://people.apache.org/~pwendell/spark-releases/spark-2.1.0-rc2-docs/api/R/index.html

## How was this patch tested?

Manual.

```bash
cd docs
SKIP_SCALADOC=1 jekyll build
```

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #16292 from dongjoon-hyun/SPARK-18875.

## What changes were proposed in this pull request?
After the bug fix in SPARK-18854, TreeNode.apply now returns TreeNode[_] rather than a more specific type. It would be easier for interactive debugging to introduce a function that returns the BaseType.

## How was this patch tested?
N/A - this is a developer only feature used for interactive debugging. As long as it compiles, it should be good to go. I tested this in spark-shell.

Author: Reynold Xin <rxin@databricks.com>

Closes #16288 from rxin/SPARK-18869.

## What changes were proposed in this pull request?

In `DataSource`, if the table is not analyzed, we will use 0 as the default value for table size. This is dangerous, we may broadcast a large table and cause OOM. We should use `defaultSizeInBytes` instead.

## How was this patch tested?

new regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #16280 from cloud-fan/bug.

[SPARK-18703][SQL] Drop Staging Directories and Data Files After each Insertion/CTAS of Hive serde Tables

### What changes were proposed in this pull request?
Below are the files/directories generated for three inserts againsts a Hive table:
```
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1/-ext-10000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1/-ext-10000/._SUCCESS.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1/-ext-10000/.part-00000.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1/-ext-10000/_SUCCESS
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1/-ext-10000/part-00000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1/-ext-10000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1/-ext-10000/._SUCCESS.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1/-ext-10000/.part-00000.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1/-ext-10000/_SUCCESS
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1/-ext-10000/part-00000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1/-ext-10000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1/-ext-10000/._SUCCESS.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1/-ext-10000/.part-00000.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1/-ext-10000/_SUCCESS
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1/-ext-10000/part-00000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.part-00000.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/part-00000
```

The first 18 files are temporary. We do not drop it until the end of JVM termination. If JVM does not appropriately terminate, these temporary files/directories will not be dropped.

Only the last two files are needed, as shown below.
```
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.part-00000.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/part-00000
```
The temporary files/directories could accumulate a lot when we issue many inserts, since each insert generats at least six files. This could eat a lot of spaces and slow down the JVM termination. When the JVM does not terminates approprately, the files might not be dropped.

This PR is to drop the created staging files and temporary data files after each insert/CTAS.

### How was this patch tested?
Added a test case

Author: gatorsmile <gatorsmile@gmail.com>

Closes #16134 from gatorsmile/deleteFiles.

## What changes were proposed in this pull request?

When do the QA work, I found that the following issues:

1). `spark.mlp` doesn't include an example;
2). `spark.mlp` and `spark.lda` have redundant parameter explanations;
3). `spark.lda` document misses default values for some parameters.

I also changed the `spark.logit` regParam in the examples, as we discussed in #16222.

## How was this patch tested?

Manual test

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

Closes #16284 from wangmiao1981/ks.

## What changes were proposed in this pull request?
This is a bug introduced by subquery handling. numberedTreeString (which uses generateTreeString under the hood) numbers trees including innerChildren (used to print subqueries), but apply (which uses getNodeNumbered) ignores innerChildren. As a result, apply(i) would return the wrong plan node if there are subqueries.

This patch fixes the bug.

## How was this patch tested?
Added a test case in SubquerySuite.scala to test both the depth-first traversal of numbering as well as making sure the two methods are consistent.

Author: Reynold Xin <rxin@databricks.com>

Closes #16277 from rxin/SPARK-18854.

## What changes were proposed in this pull request?

Added short section for KSTest.
Also added logreg model to list of ML models in vignette.  (This will be reorganized under SPARK-18849)

![screen shot 2016-12-14 at 1 37 31 pm](https://cloud.githubusercontent.com/assets/5084283/21202140/7f24e240-c202-11e6-9362-458208bb9159.png)

## How was this patch tested?

Manually tested example locally.
Built vignettes locally.

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

Closes #16283 from jkbradley/ksTest-vignette.

## What changes were proposed in this pull request?

Right now `StreamingQuery.lastProgress` throws NoSuchElementException and it's hard to be used in Python since Python user will just see Py4jError.

This PR just makes it return null instead.

## How was this patch tested?

`test("lastProgress should be null when recentProgress is empty")`

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #16273 from zsxwing/SPARK-18852.

## What changes were proposed in this pull request?
This patch reduces the default number element estimation for arrays and maps from 100 to 1. The issue with the 100 number is that when nested (e.g. an array of map), 100 * 100 would be used as the default size. This sounds like just an overestimation which doesn't seem that bad (since it is usually better to overestimate than underestimate). However, due to the way we assume the size output for Project (new estimated column size / old estimated column size), this overestimation can become underestimation. It is actually in general in this case safer to assume 1 default element.

## How was this patch tested?
This should be covered by existing tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #16274 from rxin/SPARK-18853.

[SPARK-18753][SQL] Keep pushed-down null literal as a filter in Spark-side post-filter for FileFormat datasources

## What changes were proposed in this pull request?

Currently, `FileSourceStrategy` does not handle the case when the pushed-down filter is `Literal(null)` and removes it at the post-filter in Spark-side.

For example, the codes below:

```scala
val df = Seq(Tuple1(Some(true)), Tuple1(None), Tuple1(Some(false))).toDF()
df.filter($"_1" === "true").explain(true)
```

shows it keeps `null` properly.

```
== Parsed Logical Plan ==
'Filter ('_1 = true)
+- LocalRelation [_1#17]

== Analyzed Logical Plan ==
_1: boolean
Filter (cast(_1#17 as double) = cast(true as double))
+- LocalRelation [_1#17]

== Optimized Logical Plan ==
Filter (isnotnull(_1#17) && null)
+- LocalRelation [_1#17]

== Physical Plan ==
*Filter (isnotnull(_1#17) && null)       << Here `null` is there
+- LocalTableScan [_1#17]
```

However, when we read it back from Parquet,

```scala
val path = "/tmp/testfile"
df.write.parquet(path)
spark.read.parquet(path).filter($"_1" === "true").explain(true)
```

`null` is removed at the post-filter.

```
== Parsed Logical Plan ==
'Filter ('_1 = true)
+- Relation[_1#11] parquet

== Analyzed Logical Plan ==
_1: boolean
Filter (cast(_1#11 as double) = cast(true as double))
+- Relation[_1#11] parquet

== Optimized Logical Plan ==
Filter (isnotnull(_1#11) && null)
+- Relation[_1#11] parquet

== Physical Plan ==
*Project [_1#11]
+- *Filter isnotnull(_1#11)       << Here `null` is missing
   +- *FileScan parquet [_1#11] Batched: true, Format: ParquetFormat, Location: InMemoryFileIndex[file:/tmp/testfile], PartitionFilters: [null], PushedFilters: [IsNotNull(_1)], ReadSchema: struct<_1:boolean>
```

This PR fixes it to keep it properly. In more details,

```scala
val partitionKeyFilters =
  ExpressionSet(normalizedFilters.filter(_.references.subsetOf(partitionSet)))
```

This keeps this `null` in `partitionKeyFilters` as `Literal` always don't have `children` and `references` is being empty  which is always the subset of `partitionSet`.

And then in

```scala
val afterScanFilters = filterSet -- partitionKeyFilters
```

`null` is always removed from the post filter. So, if the referenced fields are empty, it should be applied into data columns too.

After this PR, it becomes as below:

```
== Parsed Logical Plan ==
'Filter ('_1 = true)
+- Relation[_1#276] parquet

== Analyzed Logical Plan ==
_1: boolean
Filter (cast(_1#276 as double) = cast(true as double))
+- Relation[_1#276] parquet

== Optimized Logical Plan ==
Filter (isnotnull(_1#276) && null)
+- Relation[_1#276] parquet

== Physical Plan ==
*Project [_1#276]
+- *Filter (isnotnull(_1#276) && null)
   +- *FileScan parquet [_1#276] Batched: true, Format: ParquetFormat, Location: InMemoryFileIndex[file:/private/var/folders/9j/gf_c342d7d150mwrxvkqnc180000gn/T/spark-a5d59bdb-5b..., PartitionFilters: [null], PushedFilters: [IsNotNull(_1)], ReadSchema: struct<_1:boolean>
```

## How was this patch tested?

Unit test in `FileSourceStrategySuite`

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16184 from HyukjinKwon/SPARK-18753.

## What changes were proposed in this pull request?

This PR proposes to fix the tests failed on Windows as below:

```
[info] - pipe with empty partition *** FAILED *** (672 milliseconds)
[info]   Set(0, 4, 5) did not equal Set(0, 5, 6) (PipedRDDSuite.scala:145)
[info]   org.scalatest.exceptions.TestFailedException:
...
```

In this case, `wc -c` counts the characters on both Windows and Linux but the newlines characters on Windows are `\r\n` which are two. So, the counts ends up one more for each.

```
[info] - test pipe exports map_input_file *** FAILED *** (62 milliseconds)
[info]   java.lang.IllegalStateException: Subprocess exited with status 1. Command ran: printenv map_input_file
[info]   at org.apache.spark.rdd.PipedRDD$$anon$1.hasNext(PipedRDD.scala:178)
...
```

```
[info] - test pipe exports mapreduce_map_input_file *** FAILED *** (172 milliseconds)
[info]   java.lang.IllegalStateException: Subprocess exited with status 1. Command ran: printenv mapreduce_map_input_file
[info]   at org.apache.spark.rdd.PipedRDD$$anon$1.hasNext(PipedRDD.scala:178)
...
```

`printenv` command prints the environment variables; however, when environment variables are set to `ProcessBuilder` as lower-cased keys, `printenv` in Windows ignores and does not print this although it is actually set and accessible. (this was tested in [here](https://ci.appveyor.com/project/spark-test/spark/build/208-PipedRDDSuite) for upper-cases with this [diff](https://github.com/apache/spark/compare/master...spark-test:74d39da) and [here](https://ci.appveyor.com/project/spark-test/spark/build/203-PipedRDDSuite) for lower-cases with this [diff](https://github.com/apache/spark/compare/master...spark-test:fde5e37f28032c15a8d8693ba033a8a779a26317). It seems a bug in `printenv`.
(BTW, note that environment variables on Windows are case-insensitive).

This is (I believe) a thirdparty tool on Windows that resembles `printenv` on Linux (installed in AppVeyor environment or Windows Server 2012 R2). This command does not exist, at least, for Windows 7 and 10 (manually tested).

On Windows, we can use `cmd.exe /C set [varname]` officially for this purpose. We could fix the tests with this in order to test if the environment variable is set.

## How was this patch tested?

Manually tested via AppVeyor.

**Before**
https://ci.appveyor.com/project/spark-test/spark/build/194-PipedRDDSuite

**After**
https://ci.appveyor.com/project/spark-test/spark/build/226-PipedRDDSuite

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16254 from HyukjinKwon/pipe-errors.

[SPARK-18842][TESTS][LAUNCHER] De-duplicate paths in classpaths in commands for local-cluster mode to work around the path length limitation on Windows

## What changes were proposed in this pull request?

Currently, some tests are being failed and hanging on Windows due to this problem. For the reason in SPARK-18718, some tests using `local-cluster` mode were disabled on Windows due to the length limitation by paths given to classpaths.

The limitation seems roughly 32K (see the [blog in MS](https://blogs.msdn.microsoft.com/oldnewthing/20031210-00/?p=41553/) and [another reference](https://support.thoughtworks.com/hc/en-us/articles/213248526-Getting-around-maximum-command-line-length-is-32767-characters-on-Windows)) but in `local-cluster` mode, executors were being launched as processes with the command such as [here](https://gist.github.com/HyukjinKwon/5bc81061c250d4af5a180869b59d42ea) in (only) tests.

This length is roughly 40K due to the classpaths given to `java` command. However, it seems duplicates are almost half of them. So, if we deduplicate the paths, it seems reduced to roughly 20K with the command, [here](https://gist.github.com/HyukjinKwon/dad0c8db897e5e094684a2dc6a417790).

Maybe, we should consider as some more paths are added in the future but it seems better than disabling all the tests for now with minimised changes.

Therefore, this PR proposes to deduplicate the paths in classpaths in case of launching executors as processes in `local-cluster` mode.

## How was this patch tested?

Existing tests in `ShuffleSuite` and `BroadcastJoinSuite` manually via AppVeyor

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16266 from HyukjinKwon/disable-local-cluster-tests.

## What changes were proposed in this pull request?

Currently, the full console output page of a Spark Jenkins PR build can be as large as several megabytes. It takes a relatively long time to load and may even freeze the browser for quite a while.

This PR makes the build script to post the test report page link to GitHub instead. The test report page is way more concise and is usually the first page I'd like to check when investigating a Jenkins build failure.

Note that for builds that a test report is not available (ongoing builds and builds that fail before test execution), the test report link automatically redirects to the build page.

## How was this patch tested?

N/A.

Author: Cheng Lian <lian@databricks.com>

Closes #16163 from liancheng/jenkins-test-report.

There is a small race in SchedulerIntegrationSuite.
The test assumes that the taskscheduler thread
processing that last task will finish before the DAGScheduler processes
the task event and notifies the job waiter, but that is not 100%
guaranteed.

ran the test locally a bunch of times, never failed, though admittedly
it never failed locally for me before either.  However I am nearly 100%
certain this is what caused the failure of one jenkins build
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/68694/consoleFull
(which is long gone now, sorry -- I fixed it as part of
https://github.com/apache/spark/pull/14079 initially)

Author: Imran Rashid <irashid@cloudera.com>

Closes #16270 from squito/sched_integ_flakiness.

## What changes were proposed in this pull request?
Move the checking of GROUP BY column in correlated scalar subquery from CheckAnalysis
to Analysis to fix a regression caused by SPARK-18504.

This problem can be reproduced with a simple script now.

Seq((1,1)).toDF("pk","pv").createOrReplaceTempView("p")
Seq((1,1)).toDF("ck","cv").createOrReplaceTempView("c")
sql("select * from p,c where p.pk=c.ck and c.cv = (select avg(c1.cv) from c c1 where c1.ck = p.pk)").show

The requirements are:
1. We need to reference the same table twice in both the parent and the subquery. Here is the table c.
2. We need to have a correlated predicate but to a different table. Here is from c (as c1) in the subquery to p in the parent.
3. We will then "deduplicate" c1.ck in the subquery to `ck#<n1>#<n2>` at `Project` above `Aggregate` of `avg`. Then when we compare `ck#<n1>#<n2>` and the original group by column `ck#<n1>` by their canonicalized form, which is #<n2> != #<n1>. That's how we trigger the exception added in SPARK-18504.

## How was this patch tested?

SubquerySuite and a simplified version of TPCDS-Q32

Author: Nattavut Sutyanyong <nsy.can@gmail.com>

Closes #16246 from nsyca/18814.

## What changes were proposed in this pull request?

`OverwriteOptions` was introduced in https://github.com/apache/spark/pull/15705, to carry the information of static partitions. However, after further refactor, this information becomes duplicated and we can remove `OverwriteOptions`.

## How was this patch tested?

N/A

Author: Wenchen Fan <wenchen@databricks.com>

Closes #15995 from cloud-fan/overwrite.

## What changes were proposed in this pull request?

While adding vignettes for kstest, I found some errors in the example:
1. There is a typo of kstest;
2. print.summary.KStest doesn't work with the example;

Fix the example errors;
Add a new unit test for print.summary.KStest;

## How was this patch tested?
Manual test;
Add new unit test;

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

Closes #16259 from wangmiao1981/ks.

## What changes were proposed in this pull request?

Disable KafkaSourceStressForDontFailOnDataLossSuite for now.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #16275 from zsxwing/ignore-flaky-test.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #16257 from vanzin/SPARK-18752.2.

## What changes were proposed in this pull request?
Add implicit encoders for BigDecimal, timestamp and date.

## How was this patch tested?
Add an unit test.  Pass build, unit tests, and some tests below .
Before:
```
scala> spark.createDataset(Seq(new java.math.BigDecimal(10)))
<console>:24: error: Unable to find encoder for type stored in a Dataset.  Primitive types (Int, String, etc) and Product types (case classes) are supported by importing spark.implicits._  Support for serializing other types will be added in future releases.
       spark.createDataset(Seq(new java.math.BigDecimal(10)))
                          ^

scala>
```
After:
```
scala> spark.createDataset(Seq(new java.math.BigDecimal(10)))
res0: org.apache.spark.sql.Dataset[java.math.BigDecimal] = [value: decimal(38,18)]
```

Author: Weiqing Yang <yangweiqing001@gmail.com>

Closes #16176 from weiqingy/SPARK-18746.

## What changes were proposed in this pull request?

Mention `spark.randomForest` and `spark.gbt` in vignettes. Keep the content minimal since users can type `?spark.randomForest` to see the full doc.

cc: jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #16264 from mengxr/SPARK-18793.

## What changes were proposed in this pull request?

- Changed `StreamingQueryProgress.watermark` to `StreamingQueryProgress.queryTimestamps` which is a `Map[String, String]` containing the following keys: "eventTime.max", "eventTime.min", "eventTime.avg", "processingTime", "watermark". All of them UTC formatted strings.

- Renamed `StreamingQuery.timestamp` to `StreamingQueryProgress.triggerTimestamp` to differentiate from `queryTimestamps`. It has the timestamp of when the trigger was started.

## How was this patch tested?

Updated tests

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

Closes #16258 from tdas/SPARK-18834.

## What changes were proposed in this pull request?

When I added a visibility check for the logs column on the executors page in #14382 the method I used only ran the check on the initial DataTable creation and not subsequent page loads. I moved the check out of the table definition and instead it runs on each page load. The jQuery DataTable functionality used is the same.

## How was this patch tested?

Tested Manually

No visible UI changes to screenshot.

Author: Alex Bozarth <ajbozart@us.ibm.com>

Closes #16256 from ajbozarth/spark18816.

## What changes were proposed in this pull request?

CostFun used to send a dense vector of zeroes as a closure in a
treeAggregate call. To avoid that, we replace treeAggregate by
mapPartition + treeReduce, creating a zero vector inside the mapPartition
block in-place.

## How was this patch tested?

Unit test for module mllib run locally for correctness.

As for performance we run an heavy optimization on our production data (50 iterations on 128 MB weight vectors) and have seen significant decrease in terms both of runtime and container being killed by lack of off-heap memory.

Author: Anthony Truchet <a.truchet@criteo.com>
Author: sethah <seth.hendrickson16@gmail.com>
Author: Anthony Truchet <AnthonyTruchet@users.noreply.github.com>

Closes #16037 from AnthonyTruchet/ENG-17719-lbfgs-only.

The AIC calculation in Binomial GLM seems to be off when the response or weight is non-integer: the result is different from that in R. This issue arises when one models rates, i.e, num of successes normalized over num of trials, and uses num of trials as weights. In this case, the effective likelihood is  weight * label ~ binomial(weight, mu), where weight = number of trials, and weight * label = number of successes and mu = is the success rate.

srowen sethah yanboliang HyukjinKwon zhengruifeng

## What changes were proposed in this pull request?
I suggest changing the current aic calculation for the Binomial family from
```
-2.0 * predictions.map { case (y: Double, mu: Double, weight: Double) =>
        weight * dist.Binomial(1, mu).logProbabilityOf(math.round(y).toInt)
      }.sum()
```
to the following which generalizes to the case of real-valued response and weights.
```
      -2.0 * predictions.map { case (y: Double, mu: Double, weight: Double) =>
        val wt = math.round(weight).toInt
        if (wt == 0){
          0.0
        } else {
          dist.Binomial(wt, mu).logProbabilityOf(math.round(y * weight).toInt)
        }
      }.sum()
```
## How was this patch tested?
I will write the unit test once the community wants to include the proposed change. For now, the following modifies existing tests in weighted Binomial GLM to illustrate the issue. The second label is changed from 0 to 0.5.

```
val datasetWithWeight = Seq(
    (1.0, 1.0, 0.0, 5.0),
    (0.5, 2.0, 1.0, 2.0),
    (1.0, 3.0, 2.0, 1.0),
    (0.0, 4.0, 3.0, 3.0)
  ).toDF("y", "w", "x1", "x2")

val formula = (new RFormula()
  .setFormula("y ~ x1 + x2")
  .setFeaturesCol("features")
  .setLabelCol("label"))
val output = formula.fit(datasetWithWeight).transform(datasetWithWeight).select("features", "label", "w")

val glr = new GeneralizedLinearRegression()
    .setFamily("binomial")
    .setWeightCol("w")
    .setFitIntercept(false)
    .setRegParam(0)

val model = glr.fit(output)
model.summary.aic
```
The AIC from Spark is 17.3227, and the AIC from R is 15.66454.

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #16149 from actuaryzhang/aic.

[SPARK-18840][YARN] Avoid throw exception when getting token renewal interval in non HDFS security environment

## What changes were proposed in this pull request?

Fix `java.util.NoSuchElementException` when running Spark in non-hdfs security environment.

In the current code, we assume `HDFS_DELEGATION_KIND` token will be found in Credentials. But in some cloud environments, HDFS is not required, so we should avoid this exception.

## How was this patch tested?

Manually verified in local environment.

Author: jerryshao <sshao@hortonworks.com>

Closes #16265 from jerryshao/SPARK-18840.

## What changes were proposed in this pull request?

Change the statement `SHOW TABLES [EXTENDED] [(IN|FROM) database_name] [[LIKE] 'identifier_with_wildcards'] [PARTITION(partition_spec)]` to the following statements:

- SHOW TABLES [(IN|FROM) database_name] [[LIKE] 'identifier_with_wildcards']
- SHOW TABLE EXTENDED [(IN|FROM) database_name] LIKE 'identifier_with_wildcards' [PARTITION(partition_spec)]

After this change, the statements `SHOW TABLE/SHOW TABLES` have the same syntax with that HIVE has.

## How was this patch tested?
Modified the test sql file `show-tables.sql`;
Modified the test suite `DDLSuite`.

Author: jiangxingbo <jiangxb1987@gmail.com>

Closes #16262 from jiangxb1987/show-table-extended.

This avoids issues during maven tests because of shading.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #16260 from vanzin/SPARK-18835.

## What changes were proposed in this pull request?

Some places in SQL may call `RpcEndpointRef.askWithRetry` (e.g., ParquetFileFormat.buildReader -> SparkContext.broadcast -> ... -> BlockManagerMaster.updateBlockInfo -> RpcEndpointRef.askWithRetry), which will finally call `Await.result`. It may cause `java.lang.IllegalArgumentException: spark.sql.execution.id is already set` when running in Scala ForkJoinPool.

This PR includes the following changes to fix this issue:

- Remove `ThreadUtils.awaitResult`
- Rename `ThreadUtils. awaitResultInForkJoinSafely` to `ThreadUtils.awaitResult`
- Replace `Await.result` in RpcTimeout with `ThreadUtils.awaitResult`.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #16230 from zsxwing/fix-SPARK-13747.

## What changes were proposed in this pull request?

Before hive 1.1, when inserting into a table, hive will create the staging directory under a common scratch directory. After the writing is finished, hive will simply empty the table directory and move the staging directory to it.

After hive 1.1, hive will create the staging directory under the table directory, and when moving staging directory to table directory, hive will still empty the table directory, but will exclude the staging directory there.

In `InsertIntoHiveTable`, we simply copy the code from hive 1.2, which means we will always create the staging directory under the table directory, no matter what the hive version is. This causes problems if the hive version is prior to 1.1, because the staging directory will be removed by hive when hive is trying to empty the table directory.

This PR copies the code from hive 0.13, so that we have 2 branches to create staging directory. If hive version is prior to 1.1, we'll go to the old style branch(i.e. create the staging directory under a common scratch directory), else, go to the new style branch(i.e. create the staging directory under the table directory)

## How was this patch tested?

new test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #16104 from cloud-fan/hive-0.13.