## What changes were proposed in this pull request?

Put Kafka 0.8 support behind a kafka-0-8 profile.

## How was this patch tested?

Existing tests, but, until PR builder and Jenkins configs are updated the effect here is to not build or test Kafka 0.8 support at all.

Author: Sean Owen <sowen@cloudera.com>

Closes #19134 from srowen/SPARK-21893.

## What changes were proposed in this pull request?

This PR exposes Netty memory usage for Spark's `TransportClientFactory` and `TransportServer`, including the details of each direct arena and heap arena metrics, as well as aggregated metrics. The purpose of adding the Netty metrics is to better know the memory usage of Netty in Spark shuffle, rpc and others network communications, and guide us to better configure the memory size of executors.

This PR doesn't expose these metrics to any sink, to leverage this feature, still requires to connect to either MetricsSystem or collect them back to Driver to display.

## How was this patch tested?

Add Unit test to verify it, also manually verified in real cluster.

Author: jerryshao <sshao@hortonworks.com>

Closes #18935 from jerryshao/SPARK-9104.

## What changes were proposed in this pull request?

There was a bug in Univocity Parser that causes the issue in SPARK-20978. This was fixed as below:

```scala
val df = spark.read.schema("a string, b string, unparsed string").option("columnNameOfCorruptRecord", "unparsed").csv(Seq("a").toDS())
df.show()
```

**Before**

```
java.lang.NullPointerException
	at scala.collection.immutable.StringLike$class.stripLineEnd(StringLike.scala:89)
	at scala.collection.immutable.StringOps.stripLineEnd(StringOps.scala:29)
	at org.apache.spark.sql.execution.datasources.csv.UnivocityParser.org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$getCurrentInput(UnivocityParser.scala:56)
	at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:207)
	at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:207)
...
```

**After**

```
+---+----+--------+
|  a|   b|unparsed|
+---+----+--------+
|  a|null|       a|
+---+----+--------+
```

It was fixed in 2.5.0 and 2.5.4 was released. I guess it'd be safe to upgrade this.

## How was this patch tested?

Unit test added in `CSVSuite.scala`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #19113 from HyukjinKwon/bump-up-univocity.

[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.

Mesos has secrets primitives for environment and file-based secrets, this PR adds that functionality to the Spark dispatcher and the appropriate configuration flags.
Unit tested and manually tested against a DC/OS cluster with Mesos 1.4.

Author: ArtRand <arand@soe.ucsc.edu>

Closes #18837 from ArtRand/spark-20812-dispatcher-secrets-and-labels.

## 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?

Like Parquet, this PR aims to depend on the latest Apache ORC 1.4 for Apache Spark 2.3. There are key benefits for Apache ORC 1.4.

- Stability: Apache ORC 1.4.0 has many fixes and we can depend on ORC community more.
- Maintainability: Reduce the Hive dependency and can remove old legacy code later.

Later, we can get the following two key benefits by adding new ORCFileFormat in SPARK-20728 (#17980), too.
- Usability: User can use ORC data sources without hive module, i.e, -Phive.
- Speed: Use both Spark ColumnarBatch and ORC RowBatch together. This will be faster than the current implementation in Spark.

## How was this patch tested?

Pass the jenkins.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #18640 from dongjoon-hyun/SPARK-21422.

## What changes were proposed in this pull request?

Update sbt version to 0.13.16. I think this is a useful stepping stone to getting to sbt 1.0.0.

## How was this patch tested?

Existing Build.

Author: pj.fanning <pj.fanning@workday.com>

Closes #18921 from pjfanning/SPARK-21709.

## What changes were proposed in this pull request?

This is trivial, but bugged me. We should download software over HTTPS.
And we can use RAT 0.12 while at it to pick up bug fixes.

## How was this patch tested?

N/A

Author: Sean Owen <sowen@cloudera.com>

Closes #18927 from srowen/Rat012.

## What changes were proposed in this pull request?
This pr updated `lz4-java` to the latest (v1.4.0) and removed custom `LZ4BlockInputStream`. We currently use custom `LZ4BlockInputStream` to read concatenated byte stream in shuffle. But, this functionality has been implemented in the latest lz4-java (https://github.com/lz4/lz4-java/pull/105). So, we might update the latest to remove the custom `LZ4BlockInputStream`.

Major diffs between the latest release and v1.3.0 in the master are as follows (https://github.com/lz4/lz4-java/compare/62f7547abb0819d1ca1e669645ee1a9d26cd60b0...6d4693f56253fcddfad7b441bb8d917b182efa2d);
- fixed NPE in XXHashFactory similarly
- Don't place resources in default package to support shading
- Fixes ByteBuffer methods failing to apply arrayOffset() for array-backed
- Try to load lz4-java from java.library.path, then fallback to bundled
- Add ppc64le binary
- Add s390x JNI binding
- Add basic LZ4 Frame v1.5.0 support
- enable aarch64 support for lz4-java
- Allow unsafeInstance() for ppc64le archiecture
- Add unsafeInstance support for AArch64
- Support 64-bit JNI build on Solaris
- Avoid over-allocating a buffer
- Allow EndMark to be incompressible for LZ4FrameInputStream.
- Concat byte stream

## How was this patch tested?
Existing tests.

Author: Takeshi Yamamuro <yamamuro@apache.org>

Closes #18883 from maropu/SPARK-21276.

## What changes were proposed in this pull request?

Update breeze to 0.13.1 for an emergency bugfix in strong wolfe line search
https://github.com/scalanlp/breeze/pull/651

## How was this patch tested?

N/A

Author: WeichenXu <WeichenXu123@outlook.com>

Closes #18797 from WeichenXu123/update-breeze.

## What changes were proposed in this pull request?

Taking over https://github.com/apache/spark/pull/18789 ; Closes #18789

Update Jackson to 2.6.7 uniformly, and some components to 2.6.7.1, to get some fixes and prep for Scala 2.12

## How was this patch tested?

Existing tests

Author: Sean Owen <sowen@cloudera.com>

Closes #18881 from srowen/SPARK-20433.

## What changes were proposed in this pull request?

This PR proposes to use https://rversions.r-pkg.org/r-release-win instead of https://rversions.r-pkg.org/r-release to check R's version for Windows correctly.

We met a syncing problem with Windows release (see #15709) before. To cut this short, it was ...

- 3.3.2 release was released but not for Windows for few hours.
- `https://rversions.r-pkg.org/r-release` returns the latest as 3.3.2 and the download link for 3.3.1 becomes `windows/base/old` by our script
- 3.3.2 release for WIndows yet
- 3.3.1 is still not in `windows/base/old` but `windows/base` as the latest
- Failed to download with `windows/base/old` link and builds were broken

I believe this problem is not only what we met. Please see https://github.com/krlmlr/r-appveyor/commit/01ce943929993bbf27facd2cdc20ae2e03808eb4 and also this `r-release-win` API came out between 3.3.1 and 3.3.2 (assuming to deal with this issue), please see `https://github.com/metacran/rversions.app/issues/2`.

Using this API will prevent the problem although it looks quite rare assuming from the commit logs in https://github.com/metacran/rversions.app/commits/master. After 3.3.2, both  `r-release-win` and `r-release` are being updated together.

## How was this patch tested?

AppVeyor tests.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18859 from HyukjinKwon/use-reliable-link.

## What changes were proposed in this pull request?

R version update

## How was this patch tested?

AppVeyor

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #18856 from felixcheung/rappveyorver.

## What changes were proposed in this pull request?

This PR proposes to fix few rather typos in `merge_spark_pr.py`.

- `#   usage: ./apache-pr-merge.py    (see config env vars below)`
  -> `#   usage: ./merge_spark_pr.py    (see config env vars below)`

- `... have local a Spark ...` -> `... have a local Spark ...`

- `... to Apache.` -> `... to Apache Spark.`

I skimmed this file and these look all I could find.

## How was this patch tested?

pep8 check (`./dev/lint-python`).

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18776 from HyukjinKwon/minor-merge-script.

[SPARK-15526][ML][FOLLOWUP] Make JPMML provided scope to avoid including unshaded JARs, and repromote to compile in MLlib

Following the comment at https://issues.apache.org/jira/browse/SPARK-15526?focusedCommentId=16086106&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-16086106 -- this change actually needed a little more work to be complete.

This also marks JPMML as `provided` to make sure its JARs aren't included in the `jars` output, but then scopes to `compile` in `mllib`. This is how Guava is handled.

Checked result in `assembly/target/scala-2.11/jars` to verify there are no JPMML jars. Maven and SBT builds still work.

Author: Sean Owen <sowen@cloudera.com>

Closes #18637 from srowen/SPARK-15526.2.

## What changes were proposed in this pull request?

- Remove Scala 2.10 build profiles and support
- Replace some 2.10 support in scripts with commented placeholders for 2.12 later
- Remove deprecated API calls from 2.10 support
- Remove usages of deprecated context bounds where possible
- Remove Scala 2.10 workarounds like ScalaReflectionLock
- Other minor Scala warning fixes

## How was this patch tested?

Existing tests

Author: Sean Owen <sowen@cloudera.com>

Closes #17150 from srowen/SPARK-19810.

## What changes were proposed in this pull request?
Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`.  This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process.  The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame.  Data types except complex, date, timestamp, and decimal  are currently supported, otherwise an `UnsupportedOperation` exception is thrown.

Additions to Spark include a Scala package private method `Dataset.toArrowPayload` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served.  A package private class/object `ArrowConverters` that provide data type mappings and conversion routines.  In Python, a private method `DataFrame._collectAsArrow` is added to collect Arrow payloads and a SQLConf "spark.sql.execution.arrow.enable" can be used in `toPandas()` to enable using Arrow (uses the old conversion by default).

## How was this patch tested?
Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types.  The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data.  This will ensure that the schema and data has been converted correctly.

Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow.  A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas.

Author: Bryan Cutler <cutlerb@gmail.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: Li Jin <li.jin@twosigma.com>
Author: Wes McKinney <wes.mckinney@twosigma.com>

Closes #18459 from BryanCutler/toPandas_with_arrow-SPARK-13534.

## What changes were proposed in this pull request?

This PR aims to bump Py4J in order to fix the following float/double bug.
Py4J 0.10.5 fixes this (https://github.com/bartdag/py4j/issues/272) and the latest Py4J is 0.10.6.

**BEFORE**
```
>>> df = spark.range(1)
>>> df.select(df['id'] + 17.133574204226083).show()
+--------------------+
|(id + 17.1335742042)|
+--------------------+
|       17.1335742042|
+--------------------+
```

**AFTER**
```
>>> df = spark.range(1)
>>> df.select(df['id'] + 17.133574204226083).show()
+-------------------------+
|(id + 17.133574204226083)|
+-------------------------+
|       17.133574204226083|
+-------------------------+
```

## How was this patch tested?

Manual.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #18546 from dongjoon-hyun/SPARK-21278.

This reverts commit e4469760.

[SPARK-21189][INFRA] Handle unknown error codes in Jenkins rather then leaving incomplete comment in PRs

## What changes were proposed in this pull request?

Recently, Jenkins tests were unstable due to unknown reasons as below:

```
 /home/jenkins/workspace/SparkPullRequestBuilder/dev/lint-r ; process was terminated by signal 9
    test_result_code, test_result_note = run_tests(tests_timeout)
  File "./dev/run-tests-jenkins.py", line 140, in run_tests
    test_result_note = ' * This patch **fails %s**.' % failure_note_by_errcode[test_result_code]
KeyError: -9
```

```
Traceback (most recent call last):
  File "./dev/run-tests-jenkins.py", line 226, in <module>
    main()
  File "./dev/run-tests-jenkins.py", line 213, in main
    test_result_code, test_result_note = run_tests(tests_timeout)
  File "./dev/run-tests-jenkins.py", line 140, in run_tests
    test_result_note = ' * This patch **fails %s**.' % failure_note_by_errcode[test_result_code]
KeyError: -10
```

This exception looks causing failing to update the comments in the PR. For example:

![2017-06-23 4 19 41](https://user-images.githubusercontent.com/6477701/27470626-d035ecd8-582f-11e7-883e-0ae6941659b7.png)

![2017-06-23 4 19 50](https://user-images.githubusercontent.com/6477701/27470629-d11ba782-582f-11e7-97e0-64d28cbc19aa.png)

these comment just remain.

This always requires, for both reviewers and the author, a overhead to click and check the logs, which I believe are not really useful.

This PR proposes to leave the code in the PR comment messages and let update the comments.

## How was this patch tested?

Jenkins tests below, I manually gave the error code to test this.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18399 from HyukjinKwon/jenkins-print-errors.

## What changes were proposed in this pull request?
Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`.  This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process.  The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame.  All non-complex data types are currently supported, otherwise an `UnsupportedOperation` exception is thrown.

Additions to Spark include a Scala package private method `Dataset.toArrowPayloadBytes` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served.  A package private class/object `ArrowConverters` that provide data type mappings and conversion routines.  In Python, a public method `DataFrame.collectAsArrow` is added to collect Arrow payloads and an optional flag in `toPandas(useArrow=False)` to enable using Arrow (uses the old conversion by default).

## How was this patch tested?
Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types.  The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data.  This will ensure that the schema and data has been converted correctly.

Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow.  A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas.

Author: Bryan Cutler <cutlerb@gmail.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: Li Jin <li.jin@twosigma.com>
Author: Wes McKinney <wes.mckinney@twosigma.com>

Closes #15821 from BryanCutler/wip-toPandas_with_arrow-SPARK-13534.

## What changes were proposed in this pull request?

Fix some typo of the document.

## How was this patch tested?

Existing tests.

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

Author: Xianyang Liu <xianyang.liu@intel.com>

Closes #18350 from ConeyLiu/fixtypo.

## What changes were proposed in this pull request?

Move Hadoop delegation token code from `spark-yarn` to `spark-core`, so that other schedulers (such as Mesos), may use it.  In order to avoid exposing Hadoop interfaces in spark-core, the new Hadoop delegation token classes are kept private.  In order to provider backward compatiblity, and to allow YARN users to continue to load their own delegation token providers via Java service loading, the old YARN interfaces, as well as the client code that uses them, have been retained.

Summary:
- Move registered `yarn.security.ServiceCredentialProvider` classes from `spark-yarn` to `spark-core`.  Moved them into a new, private hierarchy under `HadoopDelegationTokenProvider`.  Client code in `HadoopDelegationTokenManager` now loads credentials from a whitelist of three providers (`HadoopFSDelegationTokenProvider`, `HiveDelegationTokenProvider`, `HBaseDelegationTokenProvider`), instead of service loading, which means that users are not able to implement their own delegation token providers, as they are in the `spark-yarn` module.

- The `yarn.security.ServiceCredentialProvider` interface has been kept for backwards compatibility, and to continue to allow YARN users to implement their own delegation token provider implementations.  Client code in YARN now fetches tokens via the new `YARNHadoopDelegationTokenManager` class, which fetches tokens from the core providers through `HadoopDelegationTokenManager`, as well as service loads them from `yarn.security.ServiceCredentialProvider`.

Old Hierarchy:

```
yarn.security.ServiceCredentialProvider (service loaded)
  HadoopFSCredentialProvider
  HiveCredentialProvider
  HBaseCredentialProvider
yarn.security.ConfigurableCredentialManager
```

New Hierarchy:

```
HadoopDelegationTokenManager
HadoopDelegationTokenProvider (not service loaded)
  HadoopFSDelegationTokenProvider
  HiveDelegationTokenProvider
  HBaseDelegationTokenProvider

yarn.security.ServiceCredentialProvider (service loaded)
yarn.security.YARNHadoopDelegationTokenManager
```
## How was this patch tested?

unit tests

Author: Michael Gummelt <mgummelt@mesosphere.io>
Author: Dr. Stefan Schimanski <sttts@mesosphere.io>

Closes #17723 from mgummelt/SPARK-20434-refactor-kerberos.

## What changes were proposed in this pull request?

Update hadoop-2.7 profile's curator version to 2.7.1, more see [SPARK-13933](https://issues.apache.org/jira/browse/SPARK-13933).

## How was this patch tested?

manual tests

Author: Yuming Wang <wgyumg@gmail.com>

Closes #18247 from wangyum/SPARK-13933.

## What changes were proposed in this pull request?

REPL module depends on SQL module, so we should run REPL tests if SQL module has code changes.

## How was this patch tested?

N/A

Author: Wenchen Fan <wenchen@databricks.com>

Closes #18191 from cloud-fan/test.

## What changes were proposed in this pull request?

Currently, if we run `./python/run-tests.py` and they are aborted without cleaning up this directory, it fails pep8 check due to some Python scripts generated. For example, https://github.com/apache/spark/blob/7387126f83dc0489eb1df734bfeba705709b7861/python/pyspark/tests.py#L1955-L1968

```
PEP8 checks failed.
./work/app-20170531190857-0000/0/test.py:5:55: W292 no newline at end of file
./work/app-20170531190909-0000/0/test.py:5:55: W292 no newline at end of file
./work/app-20170531190924-0000/0/test.py:3:1: E302 expected 2 blank lines, found 1
./work/app-20170531190924-0000/0/test.py:7:52: W292 no newline at end of file
./work/app-20170531191016-0000/0/test.py:5:55: W292 no newline at end of file
./work/app-20170531191030-0000/0/test.py:5:55: W292 no newline at end of file
./work/app-20170531191045-0000/0/test.py:3:1: E302 expected 2 blank lines, found 1
./work/app-20170531191045-0000/0/test.py:7:52: W292 no newline at end of file
```

For me, it is sometimes a bit annoying. This PR proposes to exclude these (assuming we want to skip per https://github.com/apache/spark/blob/master/.gitignore#L73).

Also, it moves other pep8 configurations in the script into ini configuration file in pep8.

## How was this patch tested?

Manually tested via `./dev/lint-python`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18161 from HyukjinKwon/work-exclude-pep8.

## What changes were proposed in this pull request?

This PR proposes to fix the lint-breaks as below:
```
[ERROR] src/main/java/org/apache/spark/unsafe/Platform.java:[51] (regexp) RegexpSingleline: No trailing whitespace allowed.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[45,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[62,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[78,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[92,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[102,25] (naming) MethodName: Method name 'Once' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisInputDStreamBuilderSuite.java:[28,8] (imports) UnusedImports: Unused import - org.apache.spark.streaming.api.java.JavaDStream.
```

after:
```
dev/lint-java
Checkstyle checks passed.
```
[Test Result](https://travis-ci.org/ConeyLiu/spark/jobs/229666169)

## How was this patch tested?

Travis CI

Author: Xianyang Liu <xianyang.liu@intel.com>

Closes #17890 from ConeyLiu/codestyle.

## What changes were proposed in this pull request?

Drop the hadoop distirbution name from the Python version (PEP440 - https://www.python.org/dev/peps/pep-0440/). We've been using the local version string to disambiguate between different hadoop versions packaged with PySpark, but PEP0440 states that local versions should not be used when publishing up-stream. Since we no longer make PySpark pip packages for different hadoop versions, we can simply drop the hadoop information. If at a later point we need to start publishing different hadoop versions we can look at make different packages or similar.

## How was this patch tested?

Ran `make-distribution` locally

Author: Holden Karau <holden@us.ibm.com>

Closes #17885 from holdenk/SPARK-20627-remove-pip-local-version-string.

## What changes were proposed in this pull request?

Fix build warnings primarily related to Breeze 0.13 operator changes, Java style problems

## How was this patch tested?

Existing tests

Author: Sean Owen <sowen@cloudera.com>

Closes #17803 from srowen/SPARK-20523.

## What changes were proposed in this pull request?
Upgrade breeze version to 0.13.1, which fixed some critical bugs of L-BFGS-B.

## How was this patch tested?
Existing unit tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #17746 from yanboliang/spark-20449.

## What changes were proposed in this pull request?

This PR proposes two things as below:

- Avoid Unidoc build only if Hadoop 2.6 is explicitly set in SBT build

  Due to a different dependency resolution in SBT & Unidoc by an unknown reason, the documentation build fails on a specific machine & environment in Jenkins but it was unable to reproduce.

  So, this PR just checks an environment variable `AMPLAB_JENKINS_BUILD_PROFILE` that is set in Hadoop 2.6 SBT build against branches on Jenkins, and then disables Unidoc build. **Note that PR builder will still build it with Hadoop 2.6 & SBT.**

  ```
  ========================================================================
  Building Unidoc API Documentation
  ========================================================================
  [info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments:  -Phadoop-2.6 -Pmesos -Pkinesis-asl -Pyarn -Phive-thriftserver -Phive unidoc
  Using /usr/java/jdk1.8.0_60 as default JAVA_HOME.
  ...
  ```

  I checked the environment variables from the logs (first bit) as below:

  - **spark-master-test-sbt-hadoop-2.6** (this one is being failed) - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.6/lastBuild/consoleFull

  ```
  JAVA_HOME=/usr/java/jdk1.8.0_60
  JAVA_7_HOME=/usr/java/jdk1.7.0_79
  SPARK_BRANCH=master
  AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.6   <- I use this variable
  AMPLAB_JENKINS="true"
  ```
  - spark-master-test-sbt-hadoop-2.7 - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.7/lastBuild/consoleFull

  ```
  JAVA_HOME=/usr/java/jdk1.8.0_60
  JAVA_7_HOME=/usr/java/jdk1.7.0_79
  SPARK_BRANCH=master
  AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.7
  AMPLAB_JENKINS="true"
  ```

  - spark-master-test-maven-hadoop-2.6 - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.6/lastBuild/consoleFull

  ```
  JAVA_HOME=/usr/java/jdk1.8.0_60
  JAVA_7_HOME=/usr/java/jdk1.7.0_79
  HADOOP_PROFILE=hadoop-2.6
  HADOOP_VERSION=
  SPARK_BRANCH=master
  AMPLAB_JENKINS="true"
  ```

  - spark-master-test-maven-hadoop-2.7 - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7/lastBuild/consoleFull

  ```
  JAVA_HOME=/usr/java/jdk1.8.0_60
  JAVA_7_HOME=/usr/java/jdk1.7.0_79
  HADOOP_PROFILE=hadoop-2.7
  HADOOP_VERSION=
  SPARK_BRANCH=master
  AMPLAB_JENKINS="true"
  ```

  - PR builder - https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/75843/consoleFull

  ```
  JENKINS_MASTER_HOSTNAME=amp-jenkins-master
  JAVA_HOME=/usr/java/jdk1.8.0_60
  JAVA_7_HOME=/usr/java/jdk1.7.0_79
  ```

  Assuming from other logs in branch-2.1

    - SBT & Hadoop 2.6 against branch-2.1 https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-branch-2.1-test-sbt-hadoop-2.6/lastBuild/consoleFull

      ```
      JAVA_HOME=/usr/java/jdk1.8.0_60
      JAVA_7_HOME=/usr/java/jdk1.7.0_79
      SPARK_BRANCH=branch-2.1
      AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.6
      AMPLAB_JENKINS="true"
      ```

    - Maven & Hadoop 2.6 against branch-2.1 https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-branch-2.1-test-maven-hadoop-2.6/lastBuild/consoleFull

      ```
      JAVA_HOME=/usr/java/jdk1.8.0_60
      JAVA_7_HOME=/usr/java/jdk1.7.0_79
      HADOOP_PROFILE=hadoop-2.6
      HADOOP_VERSION=
      SPARK_BRANCH=branch-2.1
      AMPLAB_JENKINS="true"
      ```

  We have been using the same convention for those variables. These are actually being used in `run-tests.py` script - here https://github.com/apache/spark/blob/master/dev/run-tests.py#L519-L520

- Revert the previous try

  After https://github.com/apache/spark/pull/17651, it seems the build still fails on SBT Hadoop 2.6 master.

  I am unable to reproduce this - https://github.com/apache/spark/pull/17477#issuecomment-294094092 and the reviewer was too. So, this got merged as it looks the only way to verify this is to merge it currently (as no one seems able to reproduce this).

## How was this patch tested?

I only checked `is_hadoop_version_2_6 = os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6"` is working fine as expected as below:

```python
>>> import collections
>>> os = collections.namedtuple('os', 'environ')(environ={"AMPLAB_JENKINS_BUILD_PROFILE": "hadoop2.6"})
>>> print(not os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6")
False
>>> os = collections.namedtuple('os', 'environ')(environ={"AMPLAB_JENKINS_BUILD_PROFILE": "hadoop2.7"})
>>> print(not os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6")
True
>>> os = collections.namedtuple('os', 'environ')(environ={})
>>> print(not os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6")
True
```

I tried many ways but I was unable to reproduce this in my local. Sean also tried the way I did but he was also unable to reproduce this.

Please refer the comments in https://github.com/apache/spark/pull/17477#issuecomment-294094092

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17669 from HyukjinKwon/revert-SPARK-20343.

## What changes were proposed in this pull request?

This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable.

There are several problems with it:

- It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?".

- > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up.

  (see  joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627))

To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above.

There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013

Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings.

## How was this patch tested?

Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`.

This was tested via manually adding `time.time()` as below:

```diff
     profiles_and_goals = build_profiles + sbt_goals

     print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ",
           " ".join(profiles_and_goals))

+    import time
+    st = time.time()
     exec_sbt(profiles_and_goals)
+    print("Elapsed :[%s]" % str(time.time() - st))
```

produces

```
...
========================================================================
Building Unidoc API Documentation
========================================================================
...
[info] Main Java API documentation successful.
...
Elapsed :[94.8746569157]
...

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17477 from HyukjinKwon/SPARK-18692.

## What changes were proposed in this pull request?

Added `util._message_exception` helper to use `str(e)` when `e.message` is unavailable (Python3).  Grepped for all occurrences of `.message` in `pyspark/` and these were the only occurrences.

## How was this patch tested?

- Doctests for helper function

## Legal

This is my original work and I license the work to the project under the project’s open source license.

Author: David Gingrich <david@textio.com>

Closes #16845 from dgingrich/topic-spark-19505-py3-exceptions.

JIRA Issue: https://issues.apache.org/jira/browse/SPARK-20123

## What changes were proposed in this pull request?

If $SPARK_HOME or $FWDIR variable contains spaces, then use "./dev/make-distribution.sh --name custom-spark --tgz -Psparkr -Phadoop-2.7 -Phive -Phive-thriftserver -Pmesos -Pyarn" build spark will failed.

## How was this patch tested?

manual tests

Author: zuotingbing <zuo.tingbing9@zte.com.cn>

Closes #17452 from zuotingbing/spark-bulid.

## What changes were proposed in this pull request?

Allow Jenkins Python tests to use the installed conda to test Python 2.7 support & test pip installability.

## How was this patch tested?

Updated shell scripts, ran tests locally with installed conda, ran tests in Jenkins.

Author: Holden Karau <holden@us.ibm.com>

Closes #17355 from holdenk/SPARK-19955-support-python-tests-with-conda.

## What changes were proposed in this pull request?

A pyspark wrapper for spark.ml.stat.ChiSquareTest

## How was this patch tested?

unit tests
doctests

Author: Bago Amirbekian <bago@databricks.com>

Closes #17421 from MrBago/chiSquareTestWrapper.

## What changes were proposed in this pull request?

The master snapshot publisher builds are currently broken due to two minor build issues:

1. For unknown reasons, the LFTP `mkdir -p` command began throwing errors when the remote directory already exists. This change of behavior might have been caused by configuration changes in the ASF's SFTP server, but I'm not entirely sure of that. To work around this problem, this patch updates the script to ignore errors from the `lftp mkdir -p` commands.
2. The PySpark `setup.py` file references a non-existent `pyspark.ml.stat` module, causing Python packaging to fail by complaining about a missing directory. The fix is to simply drop that line from the setup script.

## How was this patch tested?

The LFTP fix was tested by manually running the failing commands on AMPLab Jenkins against the ASF SFTP server. The PySpark fix was tested locally.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #17437 from JoshRosen/spark-20102.

## What changes were proposed in this pull request?

- Add `HasSupport` and `HasConfidence` `Params`.
- Add new module `pyspark.ml.fpm`.
- Add `FPGrowth` / `FPGrowthModel` wrappers.
- Provide tests for new features.

## How was this patch tested?

Unit tests.

Author: zero323 <zero323@users.noreply.github.com>

Closes #17218 from zero323/SPARK-19281.

## What changes were proposed in this pull request?

Fixed a typo in `dev/make-distribution.sh` script that sets the MAVEN_OPTS variable, introduced [here](https://github.com/apache/spark/commit/0e24054#diff-ba2c046d92a1d2b5b417788bfb5cb5f8R149).

## How was this patch tested?

Run `dev/make-distribution.sh` manually.

Author: Shuai Lin <linshuai2012@gmail.com>

Closes #16984 from lins05/fix-spark-make-distribution-after-removing-java7.