This helps with preventing jdk8-specific calls being checked in,
because PR builders are running the compiler with the wrong settings.

If the JAVA_7_HOME env variable is set, assume it points at
a jdk7 and use its rt.jar when invoking javac. For zinc, just run
it with jdk7, and disable it when building jdk8-specific code.

A big note for sbt usage: adding the bootstrap options forces sbt
to fork the compiler, and that disables incremental compilation.
That means that it's really not convenient to use for normal
development, but should be ok for automated builds.

Tested with JAVA_HOME=jdk8 and JAVA_7_HOME=jdk7:
- mvn + zinc
- mvn sans zinc
- sbt

Verified that in all cases, jdk8-specific library calls fail to
compile.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #13272 from vanzin/SPARK-15451.

## What changes were proposed in this pull request?
Currently structured streaming only supports append output mode.  This PR adds the following.

- Added support for Complete output mode in the internal state store, analyzer and planner.
- Added public API in Scala and Python for users to specify output mode
- Added checks for unsupported combinations of output mode and DF operations
  - Plans with no aggregation should support only Append mode
  - Plans with aggregation should support only Update and Complete modes
  - Default output mode is Append mode (**Question: should we change this to automatically set to Complete mode when there is aggregation?**)
- Added support for Complete output mode in Memory Sink. So Memory Sink internally supports append and complete, update. But from public API only Complete and Append output modes are supported.

## How was this patch tested?
Unit tests in various test suites
- StreamingAggregationSuite: tests for complete mode
- MemorySinkSuite: tests for checking behavior in Append and Complete modes.
- UnsupportedOperationSuite: tests for checking unsupported combinations of DF ops and output modes
- DataFrameReaderWriterSuite: tests for checking that output mode cannot be called on static DFs
- Python doc test and existing unit tests modified to call write.outputMode.

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

Closes #13286 from tdas/complete-mode.

In this case, the result type of the expression becomes DECIMAL(38, 36) as we promote the individual string literals to DECIMAL(38, 18) when we handle string promotions for `BinaryArthmaticExpression`.

I think we need to cast the string literals to Double type instead. I looked at the history and found that  this was changed to use decimal instead of double to avoid potential loss of precision when we cast decimal to double.

To double check i ran the query against hive, mysql. This query returns non NULL result for both the databases and both promote the expression to use double.
Here is the output.

- Hive
```SQL
hive> create table l2 as select (cast(99 as decimal(19,6)) + '2') from l1;
OK
hive> describe l2;
OK
_c0                 	double
```
- MySQL
```SQL
mysql> create table foo2 as select (cast(99 as decimal(19,6)) + '2') from test;
Query OK, 1 row affected (0.01 sec)
Records: 1  Duplicates: 0  Warnings: 0

mysql> describe foo2;
+-----------------------------------+--------+------+-----+---------+-------+
| Field                             | Type   | Null | Key | Default | Extra |
+-----------------------------------+--------+------+-----+---------+-------+
| (cast(99 as decimal(19,6)) + '2') | double | NO   |     | 0       |       |
+-----------------------------------+--------+------+-----+---------+-------+
```

## How was this patch tested?
Added a new test in SQLQuerySuite

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

Closes #13368 from dilipbiswal/spark-15557.

## What changes were proposed in this pull request?

Right now, we will split the code for expressions into multiple functions when it exceed 64k, which requires that the the expressions are using Row object, but this is not true for whole-state codegen, it will fail to compile after splitted.

This PR will not split the code in whole-stage codegen.

## How was this patch tested?

Added regression tests.

Author: Davies Liu <davies@databricks.com>

Closes #13235 from davies/fix_nested_codegen.

## What changes were proposed in this pull request?
Since we done Scala API audit for ml.clustering at #13148, we should also fix and update the corresponding Python API docs to keep them in sync.

## How was this patch tested?
Docs change, no tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #13291 from yanboliang/spark-15361-followup.

## What changes were proposed in this pull request?

This reverts commit c24b6b67. Sent a PR to run Jenkins tests due to the revert conflicts of `dev/deps/spark-deps-hadoop*`.

## How was this patch tested?

Jenkins unit tests, integration tests, manual tests)

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #13417 from zsxwing/revert-SPARK-11753.

## What changes were proposed in this pull request?
At https://github.com/aunkrig/janino/blob/janino_2.7.8/janino/src/org/codehaus/janino/ClassLoaderIClassLoader.java#L80-L85, Janino's classloader throws the exception when its parent throws a ClassNotFoundException with a cause set. However, it does not throw the exception when there is no cause set. Seems we need to use a special ClassLoader to wrap the actual parent classloader set to Janino handle this behavior.

## How was this patch tested?
I have reverted the workaround made by https://issues.apache.org/jira/browse/SPARK-11636 ( https://github.com/apache/spark/compare/master...yhuai:SPARK-15622?expand=1#diff-bb538fda94224dd0af01d0fd7e1b4ea0R81) and `test-only *ReplSuite -- -z "SPARK-2576 importing implicits"` still passes the test (without the change in `CodeGenerator`, this test does not pass with the change in `ExecutorClassLoader `).

Author: Yin Huai <yhuai@databricks.com>

Closes #13366 from yhuai/SPARK-15622.

## What changes were proposed in this pull request?

When we build serializer for UDT object, we should declare its data type as udt instead of udt.sqlType, or if we deserialize it again, we lose the information that it's a udt object and throw analysis exception.

## How was this patch tested?

new test in `UserDefiendTypeSuite`

Author: Wenchen Fan <wenchen@databricks.com>

Closes #13402 from cloud-fan/udt.

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

The following condition in the Optimizer rule `OptimizeCodegen` is not right.
```Scala
branches.size < conf.maxCaseBranchesForCodegen
```

- The number of branches in case when clause should be `branches.size + elseBranch.size`.
- `maxCaseBranchesForCodegen` is the maximum boundary for enabling codegen. Thus, we should use `<=` instead of `<`.

This PR is to fix this boundary case and also add missing test cases for verifying the conf `MAX_CASES_BRANCHES`.

#### How was this patch tested?
Added test cases in `SQLConfSuite`

Author: gatorsmile <gatorsmile@gmail.com>

Closes #13392 from gatorsmile/maxCaseWhen.

## What changes were proposed in this pull request?
in HiveTableScanExec, schema is lazy and is related with relation.attributeMap. So it needs to serialize MetastoreRelation when serializing task binary bytes.It can avoid to serialize MetastoreRelation.

## How was this patch tested?

Author: Lianhui Wang <lianhuiwang09@gmail.com>

Closes #13397 from lianhuiwang/avoid-serialize.

## What changes were proposed in this pull request?
A local variable in NumberConverter is wrongly shared between threads.
This pr fixes the race condition.

## How was this patch tested?
Manually checked.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #13391 from maropu/SPARK-15528.

## What changes were proposed in this pull request?
For incomplete applications in HistoryServer, the complete column will show "-" instead of incorrect date.

## How was this patch tested?
manually tested.

Author: catapan <cedarpan86@gmail.com>
Author: Ziying Pan <cedarpan@Ziyings-MacBook.local>

Closes #13396 from catapan/SPARK-15641_fix_completed_column.

## What changes were proposed in this pull request?
This patch contains a list of changes as a result of my auditing Dataset, SparkSession, and SQLContext. The patch audits the categorization of experimental APIs, function groups, and deprecations. For the detailed list of changes, please see the diff.

## How was this patch tested?
N/A

Author: Reynold Xin <rxin@databricks.com>

Closes #13370 from rxin/SPARK-15638.

[SPARK-10530][CORE] Kill other task attempts when one taskattempt belonging the same task is succeeded in speculation

## What changes were proposed in this pull request?

With this patch, TaskSetManager kills other running attempts when any one of the attempt succeeds for the same task. Also killed tasks will not be considered as failed tasks and they get listed separately in the UI and also shows the task state as KILLED instead of FAILED.

## How was this patch tested?

core\src\test\scala\org\apache\spark\ui\jobs\JobProgressListenerSuite.scala
core\src\test\scala\org\apache\spark\util\JsonProtocolSuite.scala

I have verified this patch manually by enabling spark.speculation as true, when any attempt gets succeeded then other running attempts are getting killed for the same task and other pending tasks are getting assigned in those. And also when any attempt gets killed then they are considered as KILLED tasks and not considered as FAILED tasks. Please find the attached screen shots for the reference.

![stage-tasks-table](https://cloud.githubusercontent.com/assets/3174804/14075132/394c6a12-f4f4-11e5-8638-20ff7b8cc9bc.png)
![stages-table](https://cloud.githubusercontent.com/assets/3174804/14075134/3b60f412-f4f4-11e5-9ea6-dd0dcc86eb03.png)

Ref : https://github.com/apache/spark/pull/11916

Author: Devaraj K <devaraj@apache.org>

Closes #11996 from devaraj-kavali/SPARK-10530.

## What changes were proposed in this pull request?

Fixed broken java code examples in streaming documentation

Attn: tdas

Author: Matthew Wise <matthew.rs.wise@gmail.com>

Closes #13388 from mawise/fix_docs_java_streaming_example.

## What changes were proposed in this pull request?

No code change, just some typo fixing.

## How was this patch tested?

Manually run project build with testing, and build is successful.

Author: Xin Ren <iamshrek@126.com>

Closes #13385 from keypointt/codeWalkThroughStreaming.

## What changes were proposed in this pull request?

`EmbedSerializerInFilter` implicitly assumes that the plan fragment being optimized doesn't change plan schema, which is reasonable because `Dataset.filter` should never change the schema.

However, due to another issue involving `DeserializeToObject` and `SerializeFromObject`, typed filter *does* change plan schema (see [SPARK-15632][1]). This breaks `EmbedSerializerInFilter` and causes corrupted data.

This PR disables `EmbedSerializerInFilter` when there's a schema change to avoid data corruption. The schema change issue should be addressed in follow-up PRs.

## How was this patch tested?

New test case added in `DatasetSuite`.

[1]: https://issues.apache.org/jira/browse/SPARK-15632

Author: Cheng Lian <lian@databricks.com>

Closes #13362 from liancheng/spark-15112-corrupted-filter.

## What changes were proposed in this pull request?

This change resolves a number of build warnings that have accumulated, before 2.x. It does not address a large number of deprecation warnings, especially related to the Accumulator API. That will happen separately.

## How was this patch tested?

Jenkins

Author: Sean Owen <sowen@cloudera.com>

Closes #13377 from srowen/BuildWarnings.

## What changes were proposed in this pull request?
This patch reduces the verbosity of aggregate expressions in explain (but does not actually remove any information). As an example, for the following command:
```
spark.range(10).selectExpr("sum(id) + 1", "count(distinct id)").explain(true)
```

Output before this patch:
```
== Physical Plan ==
*TungstenAggregate(key=[], functions=[(sum(id#0L),mode=Final,isDistinct=false),(count(id#0L),mode=Final,isDistinct=true)], output=[(sum(id) + 1)#3L,count(DISTINCT id)#16L])
+- Exchange SinglePartition, None
   +- *TungstenAggregate(key=[], functions=[(sum(id#0L),mode=PartialMerge,isDistinct=false),(count(id#0L),mode=Partial,isDistinct=true)], output=[sum#18L,count#21L])
      +- *TungstenAggregate(key=[id#0L], functions=[(sum(id#0L),mode=PartialMerge,isDistinct=false)], output=[id#0L,sum#18L])
         +- Exchange hashpartitioning(id#0L, 5), None
            +- *TungstenAggregate(key=[id#0L], functions=[(sum(id#0L),mode=Partial,isDistinct=false)], output=[id#0L,sum#18L])
               +- *Range (0, 10, splits=2)
```

Output after this patch:
```
== Physical Plan ==
*TungstenAggregate(key=[], functions=[sum(id#0L),count(distinct id#0L)], output=[(sum(id) + 1)#3L,count(DISTINCT id)#16L])
+- Exchange SinglePartition, None
   +- *TungstenAggregate(key=[], functions=[merge_sum(id#0L),partial_count(distinct id#0L)], output=[sum#18L,count#21L])
      +- *TungstenAggregate(key=[id#0L], functions=[merge_sum(id#0L)], output=[id#0L,sum#18L])
         +- Exchange hashpartitioning(id#0L, 5), None
            +- *TungstenAggregate(key=[id#0L], functions=[partial_sum(id#0L)], output=[id#0L,sum#18L])
               +- *Range (0, 10, splits=2)
```

Note the change from `(sum(id#0L),mode=PartialMerge,isDistinct=false)` to `merge_sum(id#0L)`.

In general aggregate explain is still very verbose, but further work will be done as follow-up pull requests.

## How was this patch tested?
Tested manually.

Author: Reynold Xin <rxin@databricks.com>

Closes #13367 from rxin/SPARK-15636.

## What changes were proposed in this pull request?

Change version check in R tests

## How was this patch tested?

R tests
shivaram

Author: felixcheung <felixcheung_m@hotmail.com>

Closes #13369 from felixcheung/rversioncheck.

## What changes were proposed in this pull request?
I create a bucketed table bucketed_table with bucket column i,
```scala
case class Data(i: Int, j: Int, k: Int)
sc.makeRDD(Array((1, 2, 3))).map(x => Data(x._1, x._2, x._3)).toDF.write.bucketBy(2, "i").saveAsTable("bucketed_table")
```

and I run the following SQLs:
```sql
SELECT j FROM bucketed_table;
Error in query: bucket column i not found in existing columns (j);

SELECT j, MAX(k) FROM bucketed_table GROUP BY j;
Error in query: bucket column i not found in existing columns (j, k);
```

I think we should add a check that, we only enable bucketing when it satisfies all conditions below:
1. the conf is enabled
2. the relation is bucketed
3. the output contains all bucketing columns

## How was this patch tested?
Updated test cases to reflect the changes.

Author: Yadong Qi <qiyadong2010@gmail.com>

Closes #13321 from watermen/SPARK-15549.

## What changes were proposed in this pull request?

Let `Dataset.createTempView` and `Dataset.createOrReplaceTempView` use `CreateViewCommand`, rather than calling `SparkSession.createTempView`. Besides, this patch also removes `SparkSession.createTempView`.

## How was this patch tested?
Existing tests.

Author: Liang-Chi Hsieh <simonh@tw.ibm.com>

Closes #13327 from viirya/dataset-createtempview.

## What changes were proposed in this pull request?
This is a simple patch that makes package names for Java 8 test suites consistent. I moved everything to test.org.apache.spark to we can test package private APIs properly. Also added "java8" as the package name so we can easily run all the tests related to Java 8.

## How was this patch tested?
This is a test only change.

Author: Reynold Xin <rxin@databricks.com>

Closes #13364 from rxin/SPARK-15633.

## What changes were proposed in this pull request?
Fix the wrong bound of `k` in `PCA`
`require(k <= sources.first().size, ...`  ->  `require(k < sources.first().size`

BTW, remove unused import in `ml.ElementwiseProduct`

## How was this patch tested?

manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #13356 from zhengruifeng/fix_pca.

## What changes were proposed in this pull request?
Temp directory used to save records is not deleted after program exit in DataFrameExample. Although it called deleteOnExit, it doesn't work as the directory is not empty. Similar things happend in ContextCleanerSuite. Update the code to make sure temp directory is deleted after program exit.

## How was this patch tested?

unit tests and local build.

Author: dding3 <ding.ding@intel.com>

Closes #13328 from dding3/master.

## What changes were proposed in this pull request?

(Please fill in changes proposed in this fix)
In the MLLib naivebayes example, scala and python example doesn't use libsvm data, but Java does.

I make changes in scala and python example to use the libsvm data as the same as Java example.

## How was this patch tested?

Manual tests

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

Closes #13301 from wangmiao1981/example.

## What changes were proposed in this pull request?

These commands ignore the partition spec and change the storage properties of the table itself:
```
ALTER TABLE table_name PARTITION (a=1, b=2) SET SERDE 'my_serde'
ALTER TABLE table_name PARTITION (a=1, b=2) SET SERDEPROPERTIES ('key1'='val1')
```
Now they change the storage properties of the specified partition.

## How was this patch tested?

DDLSuite

Author: Andrew Or <andrew@databricks.com>

Closes #13343 from andrewor14/alter-table-serdeproperties.

## What changes were proposed in this pull request?

This includes minimal changes to get Spark using the current release of Parquet, 1.8.1.

## How was this patch tested?

This uses the existing Parquet tests.

Author: Ryan Blue <blue@apache.org>

Closes #13280 from rdblue/SPARK-9876-update-parquet.

## What changes were proposed in this pull request?
This PR reworks on the CliSuite test cases for `LIST FILES/JARS` commands.

CC yhuai Thanks!

Author: Xin Wu <xinwu@us.ibm.com>

Closes #13361 from xwu0226/SPARK-15431-clisuite-new.

## What changes were proposed in this pull request?

We're using `asML` to convert the mllib vector/matrix to ml vector/matrix now. Using `as` is more correct given that this conversion actually shares the same underline data structure. As a result, in this PR, `toBreeze` will be changed to `asBreeze`. This is a private API, as a result, it will not affect any user's application.

## How was this patch tested?

unit tests

Author: DB Tsai <dbt@netflix.com>

Closes #13198 from dbtsai/minor.

## What changes were proposed in this pull request?

1. Add `_transfer_param_map_to/from_java` for OneVsRest;

2. Add `_compare_params` in ml/tests.py to help compare params.

3. Add `test_onevsrest` as the integration test for OneVsRest.

## How was this patch tested?

Python unit test.

Author: yinxusen <yinxusen@gmail.com>

Closes #12875 from yinxusen/SPARK-15008.

## What changes were proposed in this pull request?
* Document ```WeightedLeastSquares```(normal equation) and ```IterativelyReweightedLeastSquares```.
* Copy ```L-BFGS``` documents from ```spark.mllib``` to ```spark.ml```.

Due to the session ```Optimization of linear methods``` is used for developers, I think we should provide the brief introduction of the optimization method, necessary references and how it implements in Spark. It's not necessary to paste all mathematical formula and derivation here. If developers/users want to learn more, they can track reference.

## How was this patch tested?
Document update, no tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #13262 from yanboliang/spark-15484.

## What changes were proposed in this pull request?

This patch adds a user guide section for generalized linear regression and includes the examples from [#12754](https://github.com/apache/spark/pull/12754).

## How was this patch tested?

Documentation only, no tests required.

## Approach

In general, it is a bit unclear what level of detail ought to be included in the user guide since there is a lot of variability within the current user guide. I tried to give a fairly brief mathematical introduction to GLMs, and cover what types of problems they could be used for. Additionally, I included a brief blurb on the IRLS solver. The input/output columns are given in a table as is found elsewhere in the docs (though, again, these appear rather intermittently in the current docs), as well as a table providing the supported families and their link functions.

Author: sethah <seth.hendrickson16@gmail.com>

Closes #13139 from sethah/SPARK-15186.

## What changes were proposed in this pull request?

- Refer to the Jira for the problem: jira : https://issues.apache.org/jira/browse/SPARK-14400
- The fix is to check if the process has exited with a non-zero exit code in `hasNext()`. I have moved this and checking of writer thread exception to a separate method.

## How was this patch tested?

- Ran a job which had incorrect transform script command and saw that the job fails
- Existing unit tests for `ScriptTransformationSuite`. Added a new unit test

Author: Tejas Patil <tejasp@fb.com>

Closes #12194 from tejasapatil/script_transform.

## What changes were proposed in this pull request?

Remove several obsolete env variables not supported for Spark on YARN now, also updates the docs to include several changes with 2.0.

## How was this patch tested?

N/A

CC vanzin tgravescs

Author: jerryshao <sshao@hortonworks.com>

Closes #13296 from jerryshao/yarn-doc.

## What changes were proposed in this pull request?

Explicitly limit launcher JVM memory to modest 128m

## How was this patch tested?

Jenkins tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #13360 from srowen/SPARK-15531.

## What changes were proposed in this pull request?

Profiling a Spark job spilling large amount of intermediate data we found that significant portion of time is being spent in DiskObjectWriter.updateBytesWritten function. Looking at the code, we see that the function is being called too frequently to update the number of bytes written to disk. We should reduce the frequency to avoid this.

## How was this patch tested?

Tested by running the job on cluster and saw 20% CPU gain  by this change.

Author: Sital Kedia <skedia@fb.com>

Closes #13332 from sitalkedia/DiskObjectWriter.

## What changes were proposed in this pull request?

Minor typo fixes in Dataset scaladoc
* Corrected context type as SparkSession, not SQLContext.
liancheng rxin andrewor14

## How was this patch tested?

Compiled locally

Author: Xinh Huynh <xinh_huynh@yahoo.com>

Closes #13330 from xinhhuynh/fix-dataset-typos.

## What changes were proposed in this pull request?
This patch adds a new function emptyDataset to SparkSession, for creating an empty dataset.

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

Author: Reynold Xin <rxin@databricks.com>

Closes #13344 from rxin/SPARK-15597.