Some very rare JVM errors are printed to stdout, and that confuses
the code in spark-class. So add a check so that those cases are
detected and the proper error message is shown to the user.

Tested by running spark-submit after setting "ulimit -v 32000".

Closes #14231

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #14508 from vanzin/SPARK-16586.

### What changes were proposed in this pull request?
Currently, the `refreshTable` API is always case sensitive.

When users use the view name without the exact case match, the API silently ignores the call. Users might expect the command has been successfully completed. However, when users run the subsequent SQL commands, they might still get the exception, like
```
Job aborted due to stage failure:
Task 1 in stage 4.0 failed 1 times, most recent failure: Lost task 1.0 in stage 4.0 (TID 7, localhost):
java.io.FileNotFoundException:
File file:/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-bd4b9ea6-9aec-49c5-8f05-01cff426211e/part-r-00000-0c84b915-c032-4f2e-abf5-1d48fdbddf38.snappy.parquet does not exist
```

This PR is to fix the issue.

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

Author: gatorsmile <gatorsmile@gmail.com>

Closes #14523 from gatorsmile/refreshTempTable.

#### What changes were proposed in this pull request?
When doing a CTAS with a Partition By clause, we got a wrong error message.

For example,
```SQL
CREATE TABLE gen__tmp
PARTITIONED BY (key string)
AS SELECT key, value FROM mytable1
```
The error message we get now is like
```
Operation not allowed: Schema may not be specified in a Create Table As Select (CTAS) statement(line 2, pos 0)
```

However, based on the code, the message we should get is like
```
Operation not allowed: A Create Table As Select (CTAS) statement is not allowed to create a partitioned table using Hive's file formats. Please use the syntax of "CREATE TABLE tableName USING dataSource OPTIONS (...) PARTITIONED BY ...\" to create a partitioned table through a CTAS statement.(line 2, pos 0)
```

Currently, partitioning columns is part of the schema. This PR fixes the bug by changing the detection orders.

#### How was this patch tested?
Added test cases.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #14113 from gatorsmile/ctas.

[SPARK-16906][SQL] Adds auxiliary info like input class and input schema in TypedAggregateExpression

## What changes were proposed in this pull request?

This PR adds auxiliary info like input class and input schema in TypedAggregateExpression

## How was this patch tested?

Manual test.

Author: Sean Zhong <seanzhong@databricks.com>

Closes #14501 from clockfly/typed_aggregation.

[SPARK-16804][SQL] Correlated subqueries containing non-deterministic operations return incorrect results

## What changes were proposed in this pull request?

This patch fixes the incorrect results in the rule ResolveSubquery in Catalyst's Analysis phase by returning an error message when the LIMIT is found in the path from the parent table to the correlated predicate in the subquery.

## How was this patch tested?

./dev/run-tests
a new unit test on the problematic pattern.

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

Closes #14411 from nsyca/master.

## What changes were proposed in this pull request?
This PR is to fix the minor Java linter errors as following:
[ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/VariableLengthRowBasedKeyValueBatch.java:[42,10] (modifier) RedundantModifier: Redundant 'final' modifier.
[ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/VariableLengthRowBasedKeyValueBatch.java:[97,10] (modifier) RedundantModifier: Redundant 'final' modifier.

## How was this patch tested?
Manual test.
dev/lint-java
Using `mvn` from path: /usr/local/bin/mvn
Checkstyle checks passed.

Author: Weiqing Yang <yangweiqing001@gmail.com>

Closes #14532 from Sherry302/master.

## What changes were proposed in this pull request?
Similar to `LogisticAggregator`, `LeastSquaresAggregator` used for linear regression ends up serializing the coefficients and the features standard deviations, which is not necessary and can cause performance issues for high dimensional data. This patch removes this serialization.

In https://github.com/apache/spark/pull/13729 the approach was to pass these values directly to the add method. The approach used here, initially, is to mark these fields as transient instead which gives the benefit of keeping the signature of the add method simple and interpretable. The downside is that it requires the use of `transient lazy val`s which are difficult to reason about if one is not quite familiar with serialization in Scala/Spark.

## How was this patch tested?

**MLlib**
![image](https://cloud.githubusercontent.com/assets/7275795/16703660/436f79fa-4524-11e6-9022-ef00058ec718.png)

**ML without patch**
![image](https://cloud.githubusercontent.com/assets/7275795/16703831/c4d50b9e-4525-11e6-80cb-9b58c850cd41.png)

**ML with patch**
![image](https://cloud.githubusercontent.com/assets/7275795/16703675/63e0cf40-4524-11e6-9120-1f512a70e083.png)

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

Closes #14109 from sethah/LIR_serialize.

## What changes were proposed in this pull request?

Currently the update interval for the console progress bar is hardcoded. This PR makes it configurable for users.

## How was this patch tested?

Ran a long running job and with a high value of update interval, the updates were shown less frequently.

Author: Tejas Patil <tejasp@fb.com>

Closes #14507 from tejasapatil/SPARK-16919.

## What changes were proposed in this pull request?

This PR aims to fix a build error on branch 1.6 at https://github.com/apache/spark/commit/8d8725208771a8815a60160a5a30dc6ea87a7e6a, but I think we had better have this consistently in master branch, too. It's because there exist other ongoing PR (https://github.com/apache/spark/pull/14525) about this.

https://amplab.cs.berkeley.edu/jenkins/job/spark-branch-1.6-compile-maven-with-yarn-2.3/286/console

```scala
[error] /home/jenkins/workspace/spark-branch-1.6-compile-maven-with-yarn-2.3/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala:82: value toDF is not a member of Seq[String]
[error]     val df = Seq("aaaac").toDF("s")
[error]                           ^
```

## How was this patch tested?

After passing Jenkins, run compilation test on branch 1.6.
```
build/mvn -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install
```

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #14526 from dongjoon-hyun/SPARK-16939.

## What changes were proposed in this pull request?

regexp_extract actually returns null when it shouldn't when a regex matches but the requested optional group did not. This makes it return an empty string, as apparently designed.

## How was this patch tested?

Additional unit test

Author: Sean Owen <sowen@cloudera.com>

Closes #14504 from srowen/SPARK-16409.

As per the postgreSQL JDBC driver [implementation](https://github.com/pgjdbc/pgjdbc/blob/ab2a6d89081fc2c1fdb2a8600f413db33669022c/pgjdbc/src/main/java/org/postgresql/PGProperty.java#L99), the default record fetch size is 0(which means, it caches all record)

This fix enforces default record fetch size as 10 to enable streaming of data.

Author: Prince J Wesley <princejohnwesley@gmail.com>

Closes #14502 from princejwesley/spark-postgres.

## What changes were proposed in this pull request?

 Fix the broken links in the programming guide of the Graphx Migration and understanding closures

## How was this patch tested?

By running the test cases  and checking the links.

Author: Shivansh <shiv4nsh@gmail.com>

Closes #14503 from shiv4nsh/SPARK-16911.

## What changes were proposed in this pull request?
default value for spark.sql.broadcastTimeout is 300s. and this property do not show in any docs of spark. so add "spark.sql.broadcastTimeout" into docs/sql-programming-guide.md to help people to how to fix this timeout error when it happenned

## How was this patch tested?

not need

(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

…ide.md

JIRA_ID:SPARK-16870
Description:default value for spark.sql.broadcastTimeout is 300s. and this property do not show in any docs of spark. so add "spark.sql.broadcastTimeout" into docs/sql-programming-guide.md to help people to how to fix this timeout error when it happenned
Test:done

Author: keliang <keliang@cmss.chinamobile.com>

Closes #14477 from biglobster/keliang.

## What changes were proposed in this pull request?

In the programming guide, the accumulator section mixes up both the old and new APIs causing it to be confusing.  This is not necessary for Scala, so all references to the old API are removed.  For Java, it is somewhat fixed up except for the example of a custom accumulator because I don't think an API exists yet.  Python has not currently implemented the new API.

## How was this patch tested?
built doc locally

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #14516 from BryanCutler/fixup-accumulator-programming-guide-SPARK-15702.

update docs to be consistent with SPARK-14645 https://issues.apache.org/jira/browse/SPARK-14645

Author: Michael Gummelt <mgummelt@mesosphere.io>

Closes #14514 from mgummelt/fix-docs.

## What changes were proposed in this pull request?

This patch fixes a bug in Spark's standalone Master which could cause applications to hang if tasks cause executors to exit with zero exit codes.

As an example of the bug, run

```
sc.parallelize(1 to 1, 1).foreachPartition { _ => System.exit(0) }
```

on a standalone cluster which has a single Spark application. This will cause all executors to die but those executors won't be replaced unless another Spark application or worker joins or leaves the cluster (or if an executor exits with a non-zero exit code). This behavior is caused by a bug in how the Master handles the `ExecutorStateChanged` event: the current implementation calls `schedule()` only if the executor exited with a non-zero exit code, so a task which causes a JVM to unexpectedly exit "cleanly" will skip the `schedule()` call.

This patch addresses this by modifying the `ExecutorStateChanged` to always unconditionally call `schedule()`. This should be safe because it should always be safe to call `schedule()`; adding extra `schedule()` calls can only affect performance and should not introduce correctness bugs.

## How was this patch tested?

I added a regression test in `DistributedSuite`.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #14510 from JoshRosen/SPARK-16925.

## Proposed Changes

* Update the list of "important classes" in `pyspark.sql` to match 2.0.
* Fix references to `UDFRegistration` so that the class shows up in the docs. It currently [doesn't](http://spark.apache.org/docs/latest/api/python/pyspark.sql.html).
* Remove some unnecessary whitespace in the Python RST doc files.

I reused the [existing JIRA](https://issues.apache.org/jira/browse/SPARK-16772) I created last week for similar API doc fixes.

## How was this patch tested?

* I ran `lint-python` successfully.
* I ran `make clean build` on the Python docs and confirmed the results are as expected locally in my browser.

Author: Nicholas Chammas <nicholas.chammas@gmail.com>

Closes #14496 from nchammas/SPARK-16772-UDFRegistration.

## What changes were proposed in this pull request?

Mask `spark.authenticate.secret` on Spark environment page (Web UI).
This is addition to https://github.com/apache/spark/pull/14409

## How was this patch tested?
`./dev/run-tests`
[info] ScalaTest
[info] Run completed in 1 hour, 8 minutes, 38 seconds.
[info] Total number of tests run: 2166
[info] Suites: completed 65, aborted 0
[info] Tests: succeeded 2166, failed 0, canceled 0, ignored 590, pending 0
[info] All tests passed.

Author: Artur Sukhenko <artur.sukhenko@gmail.com>

Closes #14484 from Devian-ua/SPARK-16796.

[SPARK-16847][SQL] Prevent to potentially read corrupt statstics on binary in Parquet vectorized reader

## What changes were proposed in this pull request?

This problem was found in [PARQUET-251](https://issues.apache.org/jira/browse/PARQUET-251) and we disabled filter pushdown on binary columns in Spark before. We enabled this after upgrading Parquet but it seems there is potential incompatibility for Parquet files written in lower Spark versions.

Currently, this does not happen in normal Parquet reader. However, In Spark, we implemented a vectorized reader, separately with Parquet's standard API. For normal Parquet reader this is being handled but not in the vectorized reader.

It is okay to just pass `FileMetaData`. This is being handled in parquet-mr (See https://github.com/apache/parquet-mr/commit/e3b95020f777eb5e0651977f654c1662e3ea1f29). This will prevent loading corrupt statistics in each page in Parquet.

This PR replaces the deprecated usage of constructor.

## How was this patch tested?

N/A

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #14450 from HyukjinKwon/SPARK-16847.

## What changes were proposed in this pull request?
When we create the HiveConf for metastore client, we use a Hadoop Conf as the base, which may contain Hive settings in hive-site.xml (https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/internal/SharedState.scala#L49). However, HiveConf's initialize function basically ignores the base Hadoop Conf and always its default values (i.e. settings with non-null default values) as the base (https://github.com/apache/hive/blob/release-1.2.1/common/src/java/org/apache/hadoop/hive/conf/HiveConf.java#L2687). So, even a user put javax.jdo.option.ConnectionURL in hive-site.xml, it is not used and Hive will use its default, which is jdbc:derby:;databaseName=metastore_db;create=true.

This issue only shows up when `spark.sql.hive.metastore.jars` is not set to builtin.

## How was this patch tested?
New test in HiveSparkSubmitSuite.

Author: Yin Huai <yhuai@databricks.com>

Closes #14497 from yhuai/SPARK-16901.

[SPARK-16750][FOLLOW-UP][ML] Add transformSchema for StringIndexer/VectorAssembler and fix failed tests.

## What changes were proposed in this pull request?
This is follow-up for #14378. When we add ```transformSchema``` for all estimators and transformers, I found there are tests failed for ```StringIndexer``` and ```VectorAssembler```. So I moved these parts of work separately in this PR, to make it more clear to review.
The corresponding tests should throw ```IllegalArgumentException``` at schema validation period after we add ```transformSchema```. It's efficient that to throw exception at the start of ```fit``` or ```transform``` rather than during the process.

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

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #14455 from yanboliang/transformSchema.

The current ganglia reporter doesn't set metric expiration time (dmax). The metrics of all finished applications are indefinitely left displayed in ganglia web. The dmax parameter allows user to set the lifetime of the metrics. The default value is 0 for compatibility with previous versions.

Author: Ekasit Kijsipongse <ekasitk@gmail.com>

Closes #11127 from ekasitk/ganglia-dmax.

## What changes were proposed in this pull request?
Improve example outputs to better reflect the functionality that is being presented.  This mostly consisted of modifying what was printed at the end of the example, such as calling show() with truncate=False, but sometimes required minor tweaks in the example data to get relevant output.  Explicitly set parameters when they are used as part of the example.  Fixed Java examples that failed to run because of using old-style MLlib Vectors or problem with schema.  Synced examples between different APIs.

## How was this patch tested?
Ran each example for Scala, Python, and Java and made sure output was legible on a terminal of width 100.

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #14308 from BryanCutler/ml-examples-improve-output-SPARK-16260.

## What changes were proposed in this pull request?
The java.net.URL class has a globally synchronized Hashtable, which limits the throughput of any single executor doing lots of calls to parse_url(). Tests have shown that a 36-core machine can only get to 10% CPU use because the threads are locked most of the time.

This patch switches to java.net.URI which has less features than java.net.URL but focuses on URI parsing, which is enough for parse_url().

New tests were added to make sure a few common edge cases didn't change behaviour.
https://issues.apache.org/jira/browse/SPARK-16826

## How was this patch tested?
I've kept the old URL code commented for now, so that people can verify that the new unit tests do pass with java.net.URL.

Thanks to srowen for the help!

Author: Sylvain Zimmer <sylvain@sylvainzimmer.com>

Closes #14488 from sylvinus/master.

## What changes were proposed in this pull request?

Spark will convert **BooleanType** to **BIT(1)**, **LongType** to **BIGINT**, **ByteType**  to **BYTE** when saving DataFrame to Oracle, but Oracle does not support BIT, BIGINT and BYTE types.

This PR is convert following _Spark Types_ to _Oracle types_ refer to [Oracle Developer's Guide](https://docs.oracle.com/cd/E19501-01/819-3659/gcmaz/)

Spark Type | Oracle
----|----
BooleanType | NUMBER(1)
IntegerType | NUMBER(10)
LongType | NUMBER(19)
FloatType | NUMBER(19, 4)
DoubleType | NUMBER(19, 4)
ByteType | NUMBER(3)
ShortType | NUMBER(5)

## How was this patch tested?

Add new tests in [JDBCSuite.scala](https://github.com/wangyum/spark/commit/22b0c2a4228cb8b5098ad741ddf4d1904e745ff6#diff-dc4b58851b084b274df6fe6b189db84d) and [OracleDialect.scala](https://github.com/wangyum/spark/commit/22b0c2a4228cb8b5098ad741ddf4d1904e745ff6#diff-5e0cadf526662f9281aa26315b3750ad)

Author: Yuming Wang <wgyumg@gmail.com>

Closes #14377 from wangyum/SPARK-16625.

## What changes were proposed in this pull request?
As reported by Bryan Cutler on the mailing list, AccumulatorV2 does not have a += method, yet the documentation still references it.

## How was this patch tested?
N/A

Author: petermaxlee <petermaxlee@gmail.com>

Closes #14466 from petermaxlee/accumulator.

## What changes were proposed in this pull request?
Doc for the Kafka 0.10 integration

## How was this patch tested?
Scala code examples were taken from my example repo, so hopefully they compile.

Author: cody koeninger <cody@koeninger.org>

Closes #14385 from koeninger/SPARK-16312.

## What changes were proposed in this pull request?

we have various logical plans for CREATE TABLE and CTAS: `CreateTableUsing`, `CreateTableUsingAsSelect`, `CreateHiveTableAsSelectLogicalPlan`. This PR unifies them to reduce the complexity and centralize the error handling.

## How was this patch tested?

existing tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #14482 from cloud-fan/table.

## What changes were proposed in this pull request?

DatasetBenchmark compares the performances of RDD, DataFrame and Dataset while running the same operations. However, there are two problems that make the comparisons unfair.

1) In backToBackMap test case, only DataFrame implementation executes less work compared to RDD or Dataset implementations. This test case processes Long+String pairs, but the output from the DataFrame implementation does not include String part while RDD or Dataset generates Long+String pairs as output. This difference significantly changes the performance characteristics due to the String manipulation and creation overheads.

2) In back-to-back map and back-to-back filter test cases, `map` or `filter` operation is executed only once regardless of `numChains` parameter for RDD. Hence the execution times for RDD have been largely underestimated.

Of course, these issues do not affect Spark users, but it may confuse Spark developers.

## How was this patch tested?
By executing the DatasetBenchmark

Author: Hiroshi Inoue <inouehrs@jp.ibm.com>

Closes #13459 from inouehrs/fix_benchmark_fairness.

[SPARK-16907][SQL] Fix performance regression for parquet table when vectorized parquet record reader is not being used

## What changes were proposed in this pull request?

For non-partitioned parquet table, if the vectorized parquet record reader is not being used, Spark 2.0 adds an extra unnecessary memory copy to append partition values for each row.

There are several typical cases that vectorized parquet record reader is not being used:
1. When the table schema is not flat, like containing nested fields.
2. When `spark.sql.parquet.enableVectorizedReader = false`

By fixing this bug, we get about 20% - 30% performance gain in test case like this:

```
// Generates parquet table with nested columns
spark.range(100000000).select(struct($"id").as("nc")).write.parquet("/tmp/data4")

def time[R](block: => R): Long = {
    val t0 = System.nanoTime()
    val result = block    // call-by-name
    val t1 = System.nanoTime()
    println("Elapsed time: " + (t1 - t0)/1000000 + "ms")
    (t1 - t0)/1000000
}

val x = ((0 until 20).toList.map(x => time(spark.read.parquet("/tmp/data4").filter($"nc.id" < 100).collect()))).sum/20
```

## How was this patch tested?

After a few times warm up, we get 26% performance improvement

Before fix:
```
Average: 4584ms, raw data (10 tries): 4726ms 4509ms 4454ms 4879ms 4586ms 4733ms 4500ms 4361ms 4456ms 4640ms
```

After fix:
```
Average: 3614ms, raw data(10 tries): 3554ms 3740ms 4019ms 3439ms 3460ms 3664ms 3557ms 3584ms 3612ms 3531ms
```

Test env: Intel(R) Core(TM) i7-6700 CPU  3.40GHz, Intel SSD SC2KW24

Author: Sean Zhong <seanzhong@databricks.com>

Closes #14445 from clockfly/fix_parquet_regression_2.

Closing the following PRs due to requests or unresponsive users.

Closes #13923
Closes #14462
Closes #13123
Closes #14423 (requested by srowen)
Closes #14424 (requested by srowen)
Closes #14101 (requested by jkbradley)
Closes #10676 (requested by srowen)
Closes #10943 (requested by yhuai)
Closes #9936
Closes #10701
Closes #10474
Closes #13248
Closes #14347
Closes #10356
Closes #9866
Closes #14310 (requested by srowen)
Closes #14390 (requested by srowen)
Closes #14343 (requested by srowen)
Closes #14402 (requested by srowen)
Closes #14437 (requested by srowen)
Closes #12000 (already merged)

Author: Josh Rosen <joshrosen@databricks.com>

Closes #14499 from JoshRosen/hotfix.

## What changes were proposed in this pull request?

Shuffle fetch on large intermediate dataset is slow because the shuffle service open/close the index file for each shuffle fetch. This change introduces a cache for the index information so that we can avoid accessing the index files for each block fetch

## How was this patch tested?

Tested by running a job on the cluster and the shuffle read time was reduced by 50%.

Author: Sital Kedia <skedia@fb.com>

Closes #12944 from sitalkedia/shuffle_service.

## What changes were proposed in this pull request?

Add threshoulds' length checking for Classifiers which extends ProbabilisticClassifier

## How was this patch tested?

unit tests and manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #14470 from zhengruifeng/classifier_check_setThreshoulds_length.

## What changes were proposed in this pull request?

This PR adds both rules for preventing to use `Deprecated` and `Override`.

- Java's `Override`
  It seems Scala compiler just ignores this. Apparently, `override` modifier is only mandatory for " that override some other **concrete member definition** in a parent class" but not for for **incomplete member definition** (such as ones from trait or abstract), see (http://www.scala-lang.org/files/archive/spec/2.11/05-classes-and-objects.html#override)

  For a simple example,

  - Normal class - needs `override` modifier

  ```bash
  scala> class A { def say = {}}
  defined class A

  scala> class B extends A { def say = {}}
  <console>:8: error: overriding method say in class A of type => Unit;
   method say needs `override' modifier
         class B extends A { def say = {}}
                                 ^
  ```

  - Trait - does not need `override` modifier

  ```bash
  scala> trait A { def say }
  defined trait A

  scala> class B extends A { def say = {}}
  defined class B
  ```

  To cut this short, this case below is possible,

  ```bash
  scala> class B extends A {
       |    Override
       |    def say = {}
       | }
  defined class B
  ```
  we can write `Override` annotation (meaning nothing) which might confuse engineers that Java's annotation is working fine. It might be great if we prevent those potential confusion.

- Java's `Deprecated`
  When `Deprecated` is used,  it seems Scala compiler recognises this correctly but it seems we use Scala one `deprecated` across codebase.

## How was this patch tested?

Manually tested, by inserting both `Override` and `Deprecated`. This will shows the error messages as below:

```bash
Scalastyle checks failed at following occurrences:
[error] ... : deprecated should be used instead of java.lang.Deprecated.
```

```basg
Scalastyle checks failed at following occurrences:
[error] ... : override modifier should be used instead of java.lang.Override.
```

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #14490 from HyukjinKwon/SPARK-16877.

## What changes were proposed in this pull request?

To Make sure ANN layer input training data to be persisted,
so that it can avoid overhead cost if the RDD need to be computed from lineage.

## How was this patch tested?

Existing Tests.

Author: WeichenXu <WeichenXu123@outlook.com>

Closes #14483 from WeichenXu123/add_ann_persist_training_data.

## What changes were proposed in this pull request?

Add the missing args-checking for randomSplit and sample

## How was this patch tested?
unit tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #14478 from zhengruifeng/fix_randomSplit.

## What changes were proposed in this pull request?

This moves DataSourceScanExec out so it's more discoverable, and now that it doesn't necessarily depend on an existing RDD.  cc davies

## How was this patch tested?

Existing tests.

Author: Eric Liang <ekl@databricks.com>

Closes #14487 from ericl/split-scan.

## What changes were proposed in this pull request?

This patch fix the overflow in LongToUnsafeRowMap when the range of key is very wide (the key is much much smaller then minKey, for example, key is Long.MinValue, minKey is > 0).

## How was this patch tested?

Added regression test (also for SPARK-16740)

Author: Davies Liu <davies@databricks.com>

Closes #14464 from davies/fix_overflow.

## What changes were proposed in this pull request?

For DataSet typed select:
```
def select[U1: Encoder](c1: TypedColumn[T, U1]): Dataset[U1]
```
If type T is a case class or a tuple class that is not atomic, the resulting logical plan's schema will mismatch with `Dataset[T]` encoder's schema, which will cause encoder error and throw AnalysisException.

### Before change:
```
scala> case class A(a: Int, b: Int)
scala> Seq((0, A(1,2))).toDS.select($"_2".as[A])
org.apache.spark.sql.AnalysisException: cannot resolve '`a`' given input columns: [_2];
..
```

### After change:
```
scala> case class A(a: Int, b: Int)
scala> Seq((0, A(1,2))).toDS.select($"_2".as[A]).show
+---+---+
|  a|  b|
+---+---+
|  1|  2|
+---+---+
```

## How was this patch tested?

Unit test.

Author: Sean Zhong <seanzhong@databricks.com>

Closes #14474 from clockfly/SPARK-16853.