## What changes were proposed in this pull request?

Before this change, with delay scheduling off, spark would effectively
ignore locality preferences for bulk scheduling.  With this change,
locality preferences are used when multiple offers are made
simultaneously.

## How was this patch tested?

Test case added which fails without this change.  All unit tests run via jenkins.

Author: Imran Rashid <irashid@cloudera.com>

Closes #16376 from squito/locality_without_delay.

## What changes were proposed in this pull request?

```
Caused by: java.lang.IllegalArgumentException: Wrong FS: s3a://**************/checkpoint/7b2231a3-d845-4740-bfa3-681850e5987f/metadata, expected: file:///
	at org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:649)
	at org.apache.hadoop.fs.RawLocalFileSystem.pathToFile(RawLocalFileSystem.java:82)
	at org.apache.hadoop.fs.RawLocalFileSystem.deprecatedGetFileStatus(RawLocalFileSystem.java:606)
	at org.apache.hadoop.fs.RawLocalFileSystem.getFileLinkStatusInternal(RawLocalFileSystem.java:824)
	at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:601)
	at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:421)
	at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1426)
	at org.apache.spark.sql.execution.streaming.StreamMetadata$.read(StreamMetadata.scala:51)
	at org.apache.spark.sql.execution.streaming.StreamExecution.<init>(StreamExecution.scala:100)
	at org.apache.spark.sql.streaming.StreamingQueryManager.createQuery(StreamingQueryManager.scala:232)
	at org.apache.spark.sql.streaming.StreamingQueryManager.startQuery(StreamingQueryManager.scala:269)
	at org.apache.spark.sql.streaming.DataStreamWriter.start(DataStreamWriter.scala:262)
```

Can easily replicate on spark standalone cluster by providing checkpoint location uri scheme anything other than "file://" and not overriding in config.

WorkAround  --conf spark.hadoop.fs.defaultFS=s3a://somebucket or set it in sparkConf or spark-default.conf

## How was this patch tested?

existing ut

Author: uncleGen <hustyugm@gmail.com>

Closes #16815 from uncleGen/SPARK-19407.

## What changes were proposed in this pull request?

Remove cyclic imports between `pyspark.ml.pipeline` and `pyspark.ml`.

## How was this patch tested?

Existing unit tests.

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

Closes #16814 from zero323/SPARK-19467.

### What changes were proposed in this pull request?
The removed codes for `IN` are not reachable, because the previous rule `InConversion` already resolves the type coercion issues.

### How was this patch tested?
N/A

Author: gatorsmile <gatorsmile@gmail.com>

Closes #16783 from gatorsmile/typeCoercionIn.

## What changes were proposed in this pull request?
The SQL parser can mistake a `WHEN (...)` used in `CASE` for a function call. This happens in cases like the following:
```sql
select case when (1) + case when 1 > 0 then 1 else 0 end = 2 then 1 else 0 end
from tb
```
This PR fixes this by re-organizing the case related parsing rules.

## How was this patch tested?
Added a regression test to the `ExpressionParserSuite`.

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

Closes #16821 from hvanhovell/SPARK-19472.

## What changes were proposed in this pull request?

Log below is misleading:

```
if (successful(index)) {
  logInfo(
    s"Task ${info.id} in stage ${taskSet.id} (TID $tid) failed, " +
    "but another instance of the task has already succeeded, " +
    "so not re-queuing the task to be re-executed.")
}
```

If fetch failed, the task is marked as successful in `TaskSetManager:: handleFailedTask`. Then log above will be printed. The `successful` just means task will not be scheduled any longer, not a real success.

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

Author: jinxing <jinxing@meituan.com>

Closes #16738 from jinxing64/SPARK-19398.

## What changes were proposed in this pull request?

The current way of resolving `InsertIntoTable` and `CreateTable` is convoluted: sometimes we replace them with concrete implementation commands during analysis, sometimes during planning phase.

And the error checking logic is also a mess: we may put it in extended analyzer rules, or extended checking rules, or `CheckAnalysis`.

This PR simplifies the data source analysis:

1.  `InsertIntoTable` and `CreateTable` are always unresolved and need to be replaced by concrete implementation commands during analysis.
2. The error checking logic is mainly in 2 rules: `PreprocessTableCreation` and `PreprocessTableInsertion`.

## How was this patch tested?

existing test.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #16269 from cloud-fan/ddl.

## What changes were proposed in this pull request?

This PR proposes to enable the tests for Parquet filter pushdown with binary and string.

This was disabled in https://github.com/apache/spark/pull/16106 due to Parquet's issue but it is now revived in https://github.com/apache/spark/pull/16791 after upgrading Parquet to 1.8.2.

## How was this patch tested?

Manually tested `ParquetFilterSuite` via IDE.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16817 from HyukjinKwon/SPARK-17213.

[SPARK-17663][CORE] SchedulableBuilder should handle invalid data access via scheduler.allocation.file

## What changes were proposed in this pull request?

If `spark.scheduler.allocation.file` has invalid `minShare` or/and `weight` values, these cause :
- `NumberFormatException` due to `toInt` function
- `SparkContext` can not be initialized.
- It does not show meaningful error message to user.

In a nutshell, this functionality can be more robust by selecting one of the following flows :

**1-** Currently, if `schedulingMode` has an invalid value, a warning message is logged and default value is set as `FIFO`. Same pattern can be used for `minShare`(default: 0) and `weight`(default: 1) as well
**2-** Meaningful error message can be shown to the user for all invalid cases.

PR offers :
- `schedulingMode` handles just empty values. It also needs to be supported for **whitespace**, **non-uppercase**(fair, FaIr etc...) or `SchedulingMode.NONE` cases by setting default value(`FIFO`)
- `minShare` and `weight` handle just empty values. They also need to be supported for **non-integer** cases by setting default values.
- Some refactoring of `PoolSuite`.

**Code to Reproduce :**

```
val conf = new SparkConf().setAppName("spark-fairscheduler").setMaster("local")
conf.set("spark.scheduler.mode", "FAIR")
conf.set("spark.scheduler.allocation.file", "src/main/resources/fairscheduler-invalid-data.xml")
val sc = new SparkContext(conf)
```

**fairscheduler-invalid-data.xml :**

```
<allocations>
    <pool name="production">
        <schedulingMode>FIFO</schedulingMode>
        <weight>invalid_weight</weight>
        <minShare>2</minShare>
    </pool>
</allocations>
```

**Stacktrace :**

```
Exception in thread "main" java.lang.NumberFormatException: For input string: "invalid_weight"
    at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
    at java.lang.Integer.parseInt(Integer.java:580)
    at java.lang.Integer.parseInt(Integer.java:615)
    at scala.collection.immutable.StringLike$class.toInt(StringLike.scala:272)
    at scala.collection.immutable.StringOps.toInt(StringOps.scala:29)
    at org.apache.spark.scheduler.FairSchedulableBuilder$$anonfun$org$apache$spark$scheduler$FairSchedulableBuilder$$buildFairSchedulerPool$1.apply(SchedulableBuilder.scala:127)
    at org.apache.spark.scheduler.FairSchedulableBuilder$$anonfun$org$apache$spark$scheduler$FairSchedulableBuilder$$buildFairSchedulerPool$1.apply(SchedulableBuilder.scala:102)
```
## How was this patch tested?

Added Unit Test Case.

Author: erenavsarogullari <erenavsarogullari@gmail.com>

Closes #15237 from erenavsarogullari/SPARK-17663.

## What changes were proposed in this pull request?

We've already upgraded parquet-mr to 1.8.2. This PR does some further cleanup by removing a workaround of PARQUET-686 and a hack due to PARQUET-363 and PARQUET-278. All three Parquet issues are fixed in parquet-mr 1.8.2.

## How was this patch tested?

Existing unit tests.

Author: Cheng Lian <lian@databricks.com>

Closes #16791 from liancheng/parquet-1.8.2-cleanup.

[SPARK-19279][SQL] Infer Schema for Hive Serde Tables and Block Creating a Hive Table With an Empty Schema

### What changes were proposed in this pull request?
So far, we allow users to create a table with an empty schema: `CREATE TABLE tab1`. This could break many code paths if we enable it. Thus, we should follow Hive to block it.

For Hive serde tables, some serde libraries require the specified schema and record it in the metastore. To get the list, we need to check `hive.serdes.using.metastore.for.schema,` which contains a list of serdes that require user-specified schema. The default values are

- org.apache.hadoop.hive.ql.io.orc.OrcSerde
- org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
- org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe
- org.apache.hadoop.hive.serde2.dynamic_type.DynamicSerDe
- org.apache.hadoop.hive.serde2.MetadataTypedColumnsetSerDe
- org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe
- org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe
- org.apache.hadoop.hive.serde2.lazybinary.LazyBinarySerDe

### How was this patch tested?
Added test cases for both Hive and data source tables

Author: gatorsmile <gatorsmile@gmail.com>

Closes #16636 from gatorsmile/fixEmptyTableSchema.

## What changes were proposed in this pull request?
Methods `numClasses` and `numFeatures` in LinearSVCModel are already usable by inheriting `JavaClassificationModel`
we should not explicitly add them.

## How was this patch tested?
existing tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #16727 from zhengruifeng/nits_in_linearSVC.

## What changes were proposed in this pull request?

* save word2vec models as distributed files rather than as one large datum. Backwards compatibility with the previous save format is maintained by checking for the "wordIndex" column
* migrate the fix for loading large models (SPARK-11994) to ml word2vec

## How was this patch tested?

Tested loading the new and old formats locally

srowen yanboliang MLnick

Author: Asher Krim <akrim@hubspot.com>

Closes #16607 from Krimit/saveLargeModels.

## What changes were proposed in this pull request?
The names method fails to check for validity of the assignment values. This can be fixed by calling colnames within names.

## How was this patch tested?
new tests.

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #16794 from actuaryzhang/sparkRNames.

## What changes were proposed in this pull request?

DataFrame.except doesn't work for UDT columns. It is because `ExtractEquiJoinKeys` will run `Literal.default` against UDT. However, we don't handle UDT in `Literal.default` and an exception will throw like:

    java.lang.RuntimeException: no default for type
    org.apache.spark.ml.linalg.VectorUDT3bfc3ba7
      at org.apache.spark.sql.catalyst.expressions.Literal$.default(literals.scala:179)
      at org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys$$anonfun$4.apply(patterns.scala:117)
      at org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys$$anonfun$4.apply(patterns.scala:110)

More simple fix is just let `Literal.default` handle UDT by its sql type. So we can use more efficient join type on UDT.

Besides `except`, this also fixes other similar scenarios, so in summary this fixes:

* `except` on two Datasets with UDT
* `intersect` on two Datasets with UDT
* `Join` with the join conditions using `<=>` on UDT columns

## How was this patch tested?

Jenkins tests.

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

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

Closes #16765 from viirya/df-except-for-udt.

## What changes were proposed in this pull request?

This PR proposes to

- remove unused `findTightestCommonType` in `TypeCoercion` as suggested in https://github.com/apache/spark/pull/16777#discussion_r99283834
- rename `findTightestCommonTypeOfTwo ` to `findTightestCommonType`.
- fix comments accordingly

The usage was removed while refactoring/fixing in several JIRAs such as SPARK-16714, SPARK-16735 and SPARK-16646

## How was this patch tested?

Existing tests.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16786 from HyukjinKwon/SPARK-19446.

## What changes were proposed in this pull request?
DirectParquetOutputCommitter was removed from Spark as it was deemed unsafe to use. We however still have some code to generate warning. This patch removes those code as well.

## How was this patch tested?
N/A

Author: Reynold Xin <rxin@databricks.com>

Closes #16796 from rxin/remove-direct.

## What changes were proposed in this pull request?

Current version has error in vignettes:
```
model <- spark.bisectingKmeans(df, Sepal_Length ~ Sepal_Width, k = 4)
summary(kmeansModel)
```

`kmeansModel` does not exist...

felixcheung wangmiao1981

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #16799 from actuaryzhang/sparkRVignettes.

## What changes were proposed in this pull request?
Update programming guide, example and vignette with Bisecting k-means.

Author: krishnakalyan3 <krishnakalyan3@gmail.com>

Closes #16767 from krishnakalyan3/bisecting-kmeans.

## What changes were proposed in this pull request?

In `TaskMemoryManager `, when we acquire memory by calling `acquireExecutionMemory` and we can't acquire required memory, we will try to spill other memory consumers.

Currently, we simply iterates the memory consumers in a hash set. Normally each time the consumer will be iterated in the same order.

The first issue is that we might spill additional consumers. For example, if consumer 1 uses 10MB, consumer 2 uses 50MB, then consumer 3 acquires 100MB but we can only get 60MB and spilling is needed. We might spill both consumer 1 and consumer 2. But we actually just need to spill consumer 2 and get the required 100MB.

The second issue is that if we spill consumer 1 in first time spilling. After a while, consumer 1 now uses 5MB. Then consumer 4 may acquire some memory and spilling is needed again. Because we iterate the memory consumers in the same order, we will spill consumer 1 again. So for consumer 1, we will produce many small spilling files.

This patch modifies the way iterating the memory consumers. It sorts the memory consumers by their memory usage. So the consumer using more memory will spill first. Once it is spilled, even it acquires few memory again, in next time spilling happens it will not be the consumers to spill again if there are other consumers using more memory than it.

## How was this patch tested?

Jenkins tests.

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

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

Closes #16603 from viirya/sort-memoryconsumer-when-spill.

## What changes were proposed in this pull request?

In `ExpressionEncoder.toRow` and `fromRow`, we catch the exception and output `treeString` of serializer/deserializer expressions in the error message. However, encoder can be very complex and the serializer/deserializer expressions can be very large trees and blow up the log files(e.g. generate over 500mb logs for this single error message.) As a first attempt, this PR try to use `simpleString` instead.

**BEFORE**

```scala
scala> :paste
// Entering paste mode (ctrl-D to finish)

case class TestCaseClass(value: Int)
import spark.implicits._
Seq(TestCaseClass(1)).toDS().collect()

// Exiting paste mode, now interpreting.

java.lang.RuntimeException: Error while decoding: java.lang.NullPointerException
newInstance(class TestCaseClass)
+- assertnotnull(input[0, int, false], - field (class: "scala.Int", name: "value"), - root class: "TestCaseClass")
   +- input[0, int, false]

  at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.fromRow(ExpressionEncoder.scala:303)
...
```

**AFTER**

```scala
...
// Exiting paste mode, now interpreting.

java.lang.RuntimeException: Error while decoding: java.lang.NullPointerException
newInstance(class TestCaseClass)
  at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.fromRow(ExpressionEncoder.scala:303)
...
```

## How was this patch tested?

Manual.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #16701 from dongjoon-hyun/SPARK-18909-EXPR-ERROR.

Closes #15736
Closes #16309
Closes #16485
Closes #16502
Closes #16196
Closes #16498
Closes #12380
Closes #16764

Closes #14394
Closes #14204
Closes #14027
Closes #13690
Closes #16279

Author: Sean Owen <sowen@cloudera.com>

Closes #16778 from srowen/CloseStalePRs.

[SPARK-19411][SQL] Remove the metadata used to mark optional columns in merged Parquet schema for filter predicate pushdown

## What changes were proposed in this pull request?

There is a metadata introduced before to mark the optional columns in merged Parquet schema for filter predicate pushdown. As we upgrade to Parquet 1.8.2 which includes the fix for the pushdown of optional columns, we don't need this metadata now.

## How was this patch tested?

Jenkins tests.

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

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

Closes #16756 from viirya/remove-optional-metadata.

## What changes were proposed in this pull request?

The current code in `HeartbeatReceiverSuite`, executorId is set as below:
```
  private val executorId1 = "executor-1"
  private val executorId2 = "executor-2"
```

The executorId is sent to driver when register as below:

```
test("expire dead hosts should kill executors with replacement (SPARK-8119)")  {
  ...
  fakeSchedulerBackend.driverEndpoint.askSync[Boolean](
      RegisterExecutor(executorId1, dummyExecutorEndpointRef1, "1.2.3.4", 0, Map.empty))
  ...
}
```

Receiving `RegisterExecutor` in `CoarseGrainedSchedulerBackend`, the executorId will be compared with `currentExecutorIdCounter` as below:
```
case RegisterExecutor(executorId, executorRef, hostname, cores, logUrls)  =>
  if (executorDataMap.contains(executorId)) {
    executorRef.send(RegisterExecutorFailed("Duplicate executor ID: " + executorId))
    context.reply(true)
  } else {
  ...
  executorDataMap.put(executorId, data)
  if (currentExecutorIdCounter < executorId.toInt) {
    currentExecutorIdCounter = executorId.toInt
  }
  ...
```

`executorId.toInt` will cause NumberformatException.

This unit test can pass currently because of `askWithRetry`, when catching exception, RPC will call again, thus it will go `if` branch and return true.

**To fix**
Rectify executorId and replace `askWithRetry` with `askSync`, refer to https://github.com/apache/spark/pull/16690
## How was this patch tested?
This fix is for unit test and no need to add another one.(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Author: jinxing <jinxing@meituan.com>

Closes #16779 from jinxing64/SPARK-19437.

## What changes were proposed in this pull request?

* Removed Since tags in Python Params since they are inherited by other classes
* Fixed doc links for LinearSVC

## How was this patch tested?

* doc tests
* generating docs locally and checking manually

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

Closes #16723 from jkbradley/pyparam-fix-doc.

## What changes were proposed in this pull request?

When connecting timeout, `ask` may fail with a confusing message:

```
17/02/01 23:15:19 INFO Worker: Connecting to master ...
java.lang.IllegalArgumentException: requirement failed: TransportClient has not yet been set.
        at scala.Predef$.require(Predef.scala:224)
        at org.apache.spark.rpc.netty.RpcOutboxMessage.onTimeout(Outbox.scala:70)
        at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$ask$1.applyOrElse(NettyRpcEnv.scala:232)
        at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$ask$1.applyOrElse(NettyRpcEnv.scala:231)
        at scala.concurrent.Future$$anonfun$onFailure$1.apply(Future.scala:138)
        at scala.concurrent.Future$$anonfun$onFailure$1.apply(Future.scala:136)
        at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
```

It's better to provide a meaningful message.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #16773 from zsxwing/connect-timeout.

## What changes were proposed in this pull request?

1, add the multi-cols support based on current private api
2, add the multi-cols support to pyspark
## How was this patch tested?

unit tests

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

Closes #12135 from zhengruifeng/quantile4multicols.

[SPARK-19347] ReceiverSupervisorImpl can add block to ReceiverTracker multiple times because of askWithRetry.

## What changes were proposed in this pull request?

`ReceiverSupervisorImpl` on executor side reports block's meta back to `ReceiverTracker` on driver side. In current code, `askWithRetry` is used. However, for `AddBlock`, `ReceiverTracker` is not idempotent, which may result in messages are processed multiple times.

**To reproduce**:

1. Check if it is the first time receiving `AddBlock` in `ReceiverTracker`, if so sleep long enough(say 200 seconds), thus the first RPC call will be timeout in `askWithRetry`, then `AddBlock` will be resent.
2. Rebuild Spark and run following job:
```
  def streamProcessing(): Unit = {
    val conf = new SparkConf()
      .setAppName("StreamingTest")
      .setMaster(masterUrl)
    val ssc = new StreamingContext(conf, Seconds(200))
    val stream = ssc.socketTextStream("localhost", 1234)
    stream.print()
    ssc.start()
    ssc.awaitTermination()
  }
```
**To fix**:

It makes sense to provide a blocking version `ask` in RpcEndpointRef, as mentioned in SPARK-18113 (https://github.com/apache/spark/pull/16503#event-927953218). Because Netty RPC layer will not drop messages. `askWithRetry` is a leftover from akka days. It imposes restrictions on the caller(e.g. idempotency) and other things that people generally don't pay that much attention to when using it.

## How was this patch tested?
Test manually. The scenario described above doesn't happen with this patch.

Author: jinxing <jinxing@meituan.com>

Closes #16690 from jinxing64/SPARK-19347.

## What changes were proposed in this pull request?

Copying of the killed status was missing while getting the newTaskInfo object by dropping the unnecessary details to reduce the memory usage. This patch adds the copying of the killed status to newTaskInfo object, this will correct the display of the status from wrong status to KILLED status in Web UI.

## How was this patch tested?

Current behaviour of displaying tasks in stage UI page,

| Index | ID | Attempt | Status | Locality Level | Executor ID / Host | Launch Time | Duration | GC Time | Input Size / Records | Write Time | Shuffle Write Size / Records | Errors |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|143	|10	|0	|SUCCESS	|NODE_LOCAL	|6 / x.xx.x.x stdout stderr|2017/01/25 07:49:27	|0 ms |		|0.0 B / 0		| |0.0 B / 0	|TaskKilled (killed intentionally)|
|156	|11	|0	|SUCCESS	|NODE_LOCAL	|5 / x.xx.x.x stdout stderr|2017/01/25 07:49:27	|0 ms |		|0.0 B / 0		| |0.0 B / 0	|TaskKilled (killed intentionally)|

Web UI display after applying the patch,

| Index | ID | Attempt | Status | Locality Level | Executor ID / Host | Launch Time | Duration | GC Time | Input Size / Records | Write Time | Shuffle Write Size / Records | Errors |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|143	|10	|0	|KILLED	|NODE_LOCAL	|6 / x.xx.x.x stdout stderr|2017/01/25 07:49:27	|0 ms |		|0.0 B / 0		|  | 0.0 B / 0	| TaskKilled (killed intentionally)|
|156	|11	|0	|KILLED	|NODE_LOCAL	|5 / x.xx.x.x stdout stderr|2017/01/25 07:49:27	|0 ms |		|0.0 B / 0		|  |0.0 B / 0	| TaskKilled (killed intentionally)|

Author: Devaraj K <devaraj@apache.org>

Closes #16725 from devaraj-kavali/SPARK-19377.

## What changes were proposed in this pull request?

This PR deduplicates arguments, `url` and `table` in `JdbcUtils` with `JDBCOptions`.

It avoids to use duplicated arguments, for example, as below:

from

```scala
val jdbcOptions = new JDBCOptions(url, table, map)
JdbcUtils.saveTable(ds, url, table, jdbcOptions)
```

to

```scala
val jdbcOptions = new JDBCOptions(url, table, map)
JdbcUtils.saveTable(ds, jdbcOptions)
```

## How was this patch tested?

Running unit test in `JdbcSuite`/`JDBCWriteSuite`

Building with Scala 2.10 as below:

```
./dev/change-scala-version.sh 2.10
./build/mvn -Pyarn -Phadoop-2.4 -Dscala-2.10 -DskipTests clean package
```

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16753 from HyukjinKwon/SPARK-19296.

## What changes were proposed in this pull request?
Fix brokens links in ml-pipeline and ml-tuning
`<div data-lang="scala">`  ->   `<div data-lang="scala" markdown="1">`

## How was this patch tested?
manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #16754 from zhengruifeng/doc_api_fix.

[SPARK-19402][DOCS] Support LaTex inline formula correctly and fix warnings in Scala/Java APIs generation

## What changes were proposed in this pull request?

This PR proposes three things as below:

- Support LaTex inline-formula, `\( ... \)` in Scala API documentation
  It seems currently,

  ```
  \( ... \)
  ```

  are rendered as they are, for example,

  <img width="345" alt="2017-01-30 10 01 13" src="https://cloud.githubusercontent.com/assets/6477701/22423960/ab37d54a-e737-11e6-9196-4f6229c0189c.png">

  It seems mistakenly more backslashes were added.

- Fix warnings Scaladoc/Javadoc generation
  This PR fixes t two types of warnings as below:

  ```
  [warn] .../spark/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala:335: Could not find any member to link for "UnsupportedOperationException".
  [warn]   /**
  [warn]   ^
  ```

  ```
  [warn] .../spark/sql/core/src/main/scala/org/apache/spark/sql/internal/VariableSubstitution.scala:24: Variable var undefined in comment for class VariableSubstitution in class VariableSubstitution
  [warn]  * `${var}`, `${system:var}` and `${env:var}`.
  [warn]      ^
  ```

- Fix Javadoc8 break
  ```
  [error] .../spark/mllib/target/java/org/apache/spark/ml/PredictionModel.java:7: error: reference not found
  [error]  *                       E.g., {link VectorUDT} for vector features.
  [error]                                       ^
  [error] .../spark/mllib/target/java/org/apache/spark/ml/PredictorParams.java:12: error: reference not found
  [error]    *                          E.g., {link VectorUDT} for vector features.
  [error]                                            ^
  [error] .../spark/mllib/target/java/org/apache/spark/ml/Predictor.java:10: error: reference not found
  [error]  *                       E.g., {link VectorUDT} for vector features.
  [error]                                       ^
  [error] .../spark/sql/hive/target/java/org/apache/spark/sql/hive/HiveAnalysis.java:5: error: reference not found
  [error]  * Note that, this rule must be run after {link PreprocessTableInsertion}.
  [error]                                                  ^
  ```

## How was this patch tested?

Manually via `sbt unidoc` and `jeykil build`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16741 from HyukjinKwon/warn-and-break.

## What changes were proposed in this pull request

When Kmeans using initMode = "random" and some random seed, it is possible the actual cluster size doesn't equal to the configured `k`.

In this case, summary(model) returns error due to the number of cols of coefficient matrix doesn't equal to k.

Example:
>  col1 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
>   col2 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
>   col3 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
>   cols <- as.data.frame(cbind(col1, col2, col3))
>   df <- createDataFrame(cols)
>
>   model2 <- spark.kmeans(data = df, ~ ., k = 5, maxIter = 10,  initMode = "random", seed = 22222, tol = 1E-5)
>
> summary(model2)
Error in `colnames<-`(`*tmp*`, value = c("col1", "col2", "col3")) :
  length of 'dimnames' [2] not equal to array extent
In addition: Warning message:
In matrix(coefficients, ncol = k) :
  data length [9] is not a sub-multiple or multiple of the number of rows [2]

Fix: Get the actual cluster size in the summary and use it to build the coefficient matrix.
## How was this patch tested?

Add unit tests.

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

Closes #16666 from wangmiao1981/kmeans.

## What changes were proposed in this pull request?

Defer `UserDefinedFunction._judf` initialization to the first call. This prevents unintended `SparkSession` initialization.  This allows users to define and import UDF without creating a context / session as a side effect.

[SPARK-19163](https://issues.apache.org/jira/browse/SPARK-19163)

## How was this patch tested?

Unit tests.

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

Closes #16536 from zero323/SPARK-19163.

[SPARK-19378][SS] Ensure continuity of stateOperator and eventTime metrics even if there is no new data in trigger

## What changes were proposed in this pull request?

In StructuredStreaming, if a new trigger was skipped because no new data arrived, we suddenly report nothing for the metrics `stateOperator`. We could however easily report the metrics from `lastExecution` to ensure continuity of metrics.

## How was this patch tested?

Regression test in `StreamingQueryStatusAndProgressSuite`

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #16716 from brkyvz/state-agg.

[SPARK-17161][PYSPARK][ML] Add PySpark-ML JavaWrapper convenience function to create Py4J JavaArrays

## What changes were proposed in this pull request?

Adding convenience function to Python `JavaWrapper` so that it is easy to create a Py4J JavaArray that is compatible with current class constructors that have a Scala `Array` as input so that it is not necessary to have a Java/Python friendly constructor.  The function takes a Java class as input that is used by Py4J to create the Java array of the given class.  As an example, `OneVsRest` has been updated to use this and the alternate constructor is removed.

## How was this patch tested?

Added unit tests for the new convenience function and updated `OneVsRest` doctests which use this to persist the model.

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #14725 from BryanCutler/pyspark-new_java_array-CountVectorizer-SPARK-17161.

## What changes were proposed in this pull request?
The `coefficients` component in model summary should be 'matrix' but the underlying structure is indeed list. This affects several models except for 'AFTSurvivalRegressionModel' which has the correct implementation. The fix is to first `unlist` the coefficients returned from the `callJMethod` before converting to matrix. An example illustrates the issues:

```
data(iris)
df <- createDataFrame(iris)
model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family = "gaussian")
s <- summary(model)

> str(s$coefficients)
List of 8
 $ : num 6.53
 $ : num -0.223
 $ : num 0.479
 $ : num 0.155
 $ : num 13.6
 $ : num -1.44
 $ : num 0
 $ : num 0.152
 - attr(*, "dim")= int [1:2] 2 4
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:2] "(Intercept)" "Sepal_Width"
  ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
> s$coefficients[, 2]
$`(Intercept)`
[1] 0.4788963

$Sepal_Width
[1] 0.1550809
```

This  shows that the underlying structure of coefficients is still `list`.

felixcheung wangmiao1981

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #16730 from actuaryzhang/sparkRCoef.

## What changes were proposed in this pull request?

According to the discussion on #16281 which tried to upgrade toward Apache Parquet 1.9.0, Apache Spark community prefer to upgrade to 1.8.2 instead of 1.9.0. Now, Apache Parquet 1.8.2 is released officially last week on 26 Jan. We can use 1.8.2 now.

https://lists.apache.org/thread.html/af0c813f1419899289a336d96ec02b3bbeecaea23aa6ef69f435c142%3Cdev.parquet.apache.org%3E

This PR only aims to bump Parquet version to 1.8.2. It didn't touch any other codes.

## How was this patch tested?

Pass the existing tests and also manually by doing `./dev/test-dependencies.sh`.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #16751 from dongjoon-hyun/SPARK-19409.

## What changes were proposed in this pull request?

With extract `[[` or replace `[[<-`, the parameter `i` is a column index, that needs to be corrected in doc. Also a few minor updates: examples, links.

## How was this patch tested?

manual

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #16721 from felixcheung/rsubsetdoc.

### What changes were proposed in this pull request?
Currently, the function `to_json` allows users to provide options for generating JSON. However, it does not pass it to `JacksonGenerator`. Thus, it ignores the user-provided options. This PR is to fix it. Below is an example.

```Scala
val df = Seq(Tuple1(Tuple1(java.sql.Timestamp.valueOf("2015-08-26 18:00:00.0")))).toDF("a")
val options = Map("timestampFormat" -> "dd/MM/yyyy HH:mm")
df.select(to_json($"a", options)).show(false)
```
The current output is like
```
+--------------------------------------+
|structtojson(a)                       |
+--------------------------------------+
|{"_1":"2015-08-26T18:00:00.000-07:00"}|
+--------------------------------------+
```

After the fix, the output is like
```
+-------------------------+
|structtojson(a)          |
+-------------------------+
|{"_1":"26/08/2015 18:00"}|
+-------------------------+
```
### How was this patch tested?
Added test cases for both `from_json` and `to_json`

Author: gatorsmile <gatorsmile@gmail.com>

Closes #16745 from gatorsmile/toJson.