## What changes were proposed in this pull request?
1, Use `SparkSession` according to [SPARK-15031](https://issues.apache.org/jira/browse/SPARK-15031)
2, Update indent for `SparkContext` according to [SPARK-15134](https://issues.apache.org/jira/browse/SPARK-15134)
3, BTW, remove some duplicate space and add missing '.'

## How was this patch tested?
manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #13050 from zhengruifeng/use_sparksession.

## What changes were proposed in this pull request?
Seems https://github.com/apache/spark/commit/db573fc743d12446dd0421fb45d00c2f541eaf9a did not remove withHiveSupport from readwrite.py

Author: Yin Huai <yhuai@databricks.com>

Closes #13069 from yhuai/fixPython.

## What changes were proposed in this pull request?

This PR adds native `SHOW CREATE TABLE` DDL command for data source tables. Support for Hive tables will be added in follow-up PR(s).

To show table creation DDL for data source tables created by CTAS statements, this PR also added partitioning and bucketing support for normal `CREATE TABLE ... USING ...` syntax.

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)

A new test suite `ShowCreateTableSuite` is added in sql/hive package to test the new feature.

Author: Cheng Lian <lian@databricks.com>

Closes #12781 from liancheng/spark-14346-show-create-table.

## What changes were proposed in this pull request?
Break copyAndReset into two methods copy and reset instead of just one.

## How was this patch tested?
Existing Tests

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #12936 from techaddict/SPARK-15080.

## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/12851
Remove `SparkSession.withHiveSupport` in PySpark and instead use `SparkSession.builder. enableHiveSupport`

## How was this patch tested?
Existing tests.

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #13063 from techaddict/SPARK-15072-followup.

## What changes were proposed in this pull request?

When a CSV begins with:
- `,,`
OR
- `"","",`

meaning that the first column names are either empty or blank strings and `header` is specified to be `true`, then the column name is replaced with `C` + the index number of that given column. For example, if you were to read in the CSV:
```
"","second column"
"hello", "there"
```
Then column names would become `"C0", "second column"`.

This behavior aligns with what currently happens when `header` is specified to be `false` in recent versions of Spark.

### Current Behavior in Spark <=1.6
In Spark <=1.6, a CSV with a blank column name becomes a blank string, `""`, meaning that this column cannot be accessed. However the CSV reads in without issue.

### Current Behavior in Spark 2.0
Spark throws a NullPointerError and will not read in the file.

#### Reproduction in 2.0
https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/346304/2828750690305044/484361/latest.html

## How was this patch tested?
A new test was added to `CSVSuite` to account for this issue. We then have asserts that test for being able to select both the empty column names as well as the regular column names.

Author: Bill Chambers <bill@databricks.com>
Author: Bill Chambers <wchambers@ischool.berkeley.edu>

Closes #13041 from anabranch/master.

## What changes were proposed in this pull request?

Before:
```sql
-- uses that location but issues a warning
CREATE TABLE my_tab LOCATION /some/path
-- deletes any existing data in the specified location
DROP TABLE my_tab
```

After:
```sql
-- uses that location but creates an EXTERNAL table instead
CREATE TABLE my_tab LOCATION /some/path
-- does not delete the data at /some/path
DROP TABLE my_tab
```

This patch essentially makes the `EXTERNAL` field optional. This is related to #13032.

## How was this patch tested?

New test in `DDLCommandSuite`.

Author: Andrew Or <andrew@databricks.com>

Closes #13060 from andrewor14/location-implies-external.

This PR:
* Corrects the documentation for the `properties` parameter, which is supposed to be a dictionary and not a list.
* Generally clarifies the Python docstring for DataFrameReader.jdbc() by pulling from the [Scala docstrings](https://github.com/apache/spark/blob/b28137764716f56fa1a923c4278624a56364a505/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala#L201-L251) and rephrasing things.
* Corrects minor Sphinx typos.

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

Closes #13034 from nchammas/SPARK-15256.

## What changes were proposed in this pull request?

Before:
```sql
-- uses warehouse dir anyway
CREATE EXTERNAL TABLE my_tab
-- doesn't actually delete the data
DROP TABLE my_tab
```
After:
```sql
-- no location is provided, throws exception
CREATE EXTERNAL TABLE my_tab
-- creates an external table using that location
CREATE EXTERNAL TABLE my_tab LOCATION '/path/to/something'
-- doesn't delete the data, which is expected
DROP TABLE my_tab
```

## How was this patch tested?

New test in `DDLCommandSuite`

Author: Andrew Or <andrew@databricks.com>

Closes #13032 from andrewor14/create-external-table-location.

## What changes were proposed in this pull request?
Earlier we removed experimental tag for Scala/Java DataFrames, but haven't done so for Python. This patch removes the experimental flag for Python and declares them stable.

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

Author: Reynold Xin <rxin@databricks.com>

Closes #13062 from rxin/SPARK-15278.

## What changes were proposed in this pull request?
Before:
Creating a hiveContext was failing
```python
from pyspark.sql import HiveContext
hc = HiveContext(sc)
```
with
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "spark-2.0/python/pyspark/sql/context.py", line 458, in __init__
    sparkSession = SparkSession.withHiveSupport(sparkContext)
  File "spark-2.0/python/pyspark/sql/session.py", line 192, in withHiveSupport
    jsparkSession = sparkContext._jvm.SparkSession.withHiveSupport(sparkContext._jsc.sc())
  File "spark-2.0/python/lib/py4j-0.9.2-src.zip/py4j/java_gateway.py", line 1048, in __getattr__
py4j.protocol.Py4JError: org.apache.spark.sql.SparkSession.withHiveSupport does not exist in the JVM
```

Now:
```python
>>> from pyspark.sql import HiveContext
>>> hc = HiveContext(sc)
>>> hc.range(0, 100)
DataFrame[id: bigint]
>>> hc.range(0, 100).count()
100
```
## How was this patch tested?
Existing Tests, tested manually in python shell

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #13056 from techaddict/SPARK-15270.

## What changes were proposed in this pull request?

If an executor is still alive even after the scheduler has removed its metadata, we may receive a heartbeat from that executor and tell its block manager to reregister itself. If that happens, the block manager master will know about the executor, but the scheduler will not.

That is a dangerous situation, because when the executor does get disconnected later, the scheduler will not ask the block manager to also remove metadata for that executor. Later, when we try to clean up an RDD or a broadcast variable, we may try to send a message to that executor, triggering an exception.

## How was this patch tested?

Jenkins.

Author: Andrew Or <andrew@databricks.com>

Closes #13055 from andrewor14/block-manager-remove.

https://issues.apache.org/jira/browse/SPARK-12200

Author: Maciej Brynski <maciej.brynski@adpilot.pl>
Author: Maciej Bryński <maciek-github@brynski.pl>

Closes #10194 from maver1ck/master.

## What changes were proposed in this pull request?

When we acquire execution memory, we do a lot of things between shrinking the storage memory pool and enlarging the execution memory pool. In particular, we call `memoryStore.evictBlocksToFreeSpace`, which may do a lot of I/O and can throw exceptions. If an exception is thrown, the pool sizes on that executor will be in a bad state.

This patch minimizes the things we do between the two calls to make the resizing more atomic.

## How was this patch tested?

Jenkins.

Author: Andrew Or <andrew@databricks.com>

Closes #13039 from andrewor14/safer-pool.

[SPARK-15248][SQL] Make MetastoreFileCatalog consider directories from partition specs of a partitioned metastore table

Table partitions can be added with locations different from default warehouse location of a hive table.
`CREATE TABLE parquetTable (a int) PARTITIONED BY (b int) STORED AS parquet `
`ALTER TABLE parquetTable ADD PARTITION (b=1) LOCATION '/partition'`
Querying such a table throws error as the MetastoreFileCatalog does not list the added partition directory, it only lists the default base location.

```
[info] - SPARK-15248: explicitly added partitions should be readable *** FAILED *** (1 second, 8 milliseconds)
[info]   java.util.NoSuchElementException: key not found: file:/Users/tdas/Projects/Spark/spark2/target/tmp/spark-b39ad224-c5d1-4966-8981-fb45a2066d61/partition
[info]   at scala.collection.MapLike$class.default(MapLike.scala:228)
[info]   at scala.collection.AbstractMap.default(Map.scala:59)
[info]   at scala.collection.MapLike$class.apply(MapLike.scala:141)
[info]   at scala.collection.AbstractMap.apply(Map.scala:59)
[info]   at org.apache.spark.sql.execution.datasources.PartitioningAwareFileCatalog$$anonfun$listFiles$1.apply(PartitioningAwareFileCatalog.scala:59)
[info]   at org.apache.spark.sql.execution.datasources.PartitioningAwareFileCatalog$$anonfun$listFiles$1.apply(PartitioningAwareFileCatalog.scala:55)
[info]   at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
[info]   at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
[info]   at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
[info]   at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
[info]   at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
[info]   at scala.collection.AbstractTraversable.map(Traversable.scala:104)
[info]   at org.apache.spark.sql.execution.datasources.PartitioningAwareFileCatalog.listFiles(PartitioningAwareFileCatalog.scala:55)
[info]   at org.apache.spark.sql.execution.datasources.FileSourceStrategy$.apply(FileSourceStrategy.scala:93)
[info]   at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:59)
[info]   at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:59)
[info]   at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
[info]   at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
[info]   at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:60)
[info]   at org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:55)
[info]   at org.apache.spark.sql.execution.SparkStrategies$SpecialLimits$.apply(SparkStrategies.scala:55)
[info]   at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:59)
[info]   at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:59)
[info]   at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
[info]   at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
[info]   at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:60)
[info]   at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:77)
[info]   at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:75)
[info]   at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:82)
[info]   at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:82)
[info]   at org.apache.spark.sql.QueryTest.assertEmptyMissingInput(QueryTest.scala:330)
[info]   at org.apache.spark.sql.QueryTest.checkAnswer(QueryTest.scala:146)
[info]   at org.apache.spark.sql.QueryTest.checkAnswer(QueryTest.scala:159)
[info]   at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12$$anonfun$apply$mcV$sp$7$$anonfun$apply$mcV$sp$25.apply(parquetSuites.scala:554)
[info]   at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12$$anonfun$apply$mcV$sp$7$$anonfun$apply$mcV$sp$25.apply(parquetSuites.scala:535)
[info]   at org.apache.spark.sql.test.SQLTestUtils$class.withTempDir(SQLTestUtils.scala:125)
[info]   at org.apache.spark.sql.hive.ParquetPartitioningTest.withTempDir(parquetSuites.scala:726)
[info]   at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12$$anonfun$apply$mcV$sp$7.apply$mcV$sp(parquetSuites.scala:535)
[info]   at org.apache.spark.sql.test.SQLTestUtils$class.withTable(SQLTestUtils.scala:166)
[info]   at org.apache.spark.sql.hive.ParquetPartitioningTest.withTable(parquetSuites.scala:726)
[info]   at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12.apply$mcV$sp(parquetSuites.scala:534)
[info]   at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12.apply(parquetSuites.scala:534)
[info]   at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12.apply(parquetSuites.scala:534)
```

The solution in this PR to get the paths to list from the partition spec and not rely on the default table path alone.

unit tests.

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

Closes #13022 from tdas/SPARK-15248.

## What changes were proposed in this pull request?
Renaming the streaming-kafka artifact to include kafka version, in anticipation of needing a different artifact for later kafka versions

## How was this patch tested?
Unit tests

Author: cody koeninger <cody@koeninger.org>

Closes #12946 from koeninger/SPARK-15085.

## What changes were proposed in this pull request?

After SPARK-14669 it seems the sort time metric includes both spill and record insertion time. This makes it not very useful since the metric becomes close to the total execution time of the node.

We should track just the time spent for in-memory sort, as before.

## How was this patch tested?

Verified metric in the UI, also unit test on UnsafeExternalRowSorter.

cc davies

Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>

Closes #13035 from ericl/fix-metrics.

## What changes were proposed in this pull request?
Use SparkSession instead of SQLContext in Python TestSuites

## How was this patch tested?
Existing tests

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #13044 from techaddict/SPARK-15037-python.

## What changes were proposed in this pull request?

SPARK-15241: We now support java decimal and catalyst decimal in external row, it makes sense to also support scala decimal.

SPARK-15242: This is a long-standing bug, and is exposed after https://github.com/apache/spark/pull/12364, which eliminate the `If` expression if the field is not nullable:
```
val fieldValue = serializerFor(
  GetExternalRowField(inputObject, i, externalDataTypeForInput(f.dataType)),
  f.dataType)
if (f.nullable) {
  If(
    Invoke(inputObject, "isNullAt", BooleanType, Literal(i) :: Nil),
    Literal.create(null, f.dataType),
    fieldValue)
} else {
  fieldValue
}
```

Previously, we always use `DecimalType.SYSTEM_DEFAULT` as the output type of converted decimal field, which is wrong as it doesn't match the real decimal type. However, it works well because we always put converted field into `If` expression to do the null check, and `If` use its `trueValue`'s data type as its output type.
Now if we have a not nullable decimal field, then the converted field's output type will be `DecimalType.SYSTEM_DEFAULT`, and we will write wrong data into unsafe row.

The fix is simple, just use the given decimal type as the output type of converted decimal field.

These 2 issues was found at https://github.com/apache/spark/pull/13008

## How was this patch tested?

new tests in RowEncoderSuite

Author: Wenchen Fan <wenchen@databricks.com>

Closes #13019 from cloud-fan/encoder-decimal.

## What changes were proposed in this pull request?

This fixes compile errors.

## How was this patch tested?

Pass the Jenkins tests.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13053 from dongjoon-hyun/hotfix_sqlquerysuite.

## What changes were proposed in this pull request?

We have a private `UDTRegistration` API to register user defined type. Currently `JavaTypeInference` can't work with it. So `SparkSession.createDataFrame` from a bean class will not correctly infer the schema of the bean class.

## How was this patch tested?
`VectorUDTSuite`.

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

Closes #13046 from viirya/fix-udt-registry-javatypeinference.

## What changes were proposed in this pull request?
#### Symptom
 If a table is created as parquet or ORC table with hive syntaxt DDL, such as
```SQL
create table t1 (c1 int, c2 string) stored as parquet
```
The following command will fail
```SQL
create view v1 as select * from t1
```
#### Root Cause
Currently, `HiveMetaStoreCatalog` converts Paruqet/Orc tables to `LogicalRelation` without giving any `tableIdentifier`. `SQLBuilder` expects the `LogicalRelation` to have an associated `tableIdentifier`. However, the `LogicalRelation` created earlier does not have such a `tableIdentifier`. Thus, `SQLBuilder.toSQL` can not recognize this logical plan and issue an exception.

This PR is to assign a `TableIdentifier` to the `LogicalRelation` when resolving parquet or orc tables in `HiveMetaStoreCatalog`.

## How was this patch tested?
testcases created and dev/run-tests is run.

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

Closes #12716 from xwu0226/SPARK_14933.

## What changes were proposed in this pull request?
1,create a libsvm-type dataset for lda: `data/mllib/sample_lda_libsvm_data.txt`
2,add python example
3,directly read the datafile in examples
4,BTW, change to `SparkSession` in `aft_survival_regression.py`

## How was this patch tested?
manual tests
`./bin/spark-submit examples/src/main/python/ml/lda_example.py`

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12927 from zhengruifeng/lda_pe.

This PR:
* Clarifies that Spark *does* support Python 3, starting with Python 3.4.

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

Closes #13017 from nchammas/supported-python-versions.

## What changes were proposed in this pull request?
make StreamingContext.textFileStream support wildcard
like /home/user/*/file

## How was this patch tested?
I did manual test and added a new unit test case

Author: mwws <wei.mao@intel.com>
Author: unknown <maowei@maowei-MOBL.ccr.corp.intel.com>

Closes #12752 from mwws/SPARK_FileStream.

## What changes were proposed in this pull request?
Python example for ml.kmeans already exists, but not included in user guide.
1,small changes like: `example_on` `example_off`
2,add it to user guide
3,update examples to directly read datafile

## How was this patch tested?
manual tests
`./bin/spark-submit examples/src/main/python/ml/kmeans_example.py

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12925 from zhengruifeng/km_pe.

## What changes were proposed in this pull request?

1, add BisectingKMeans to ml-clustering.md
2, add the missing Scala BisectingKMeansExample
3, create a new datafile `data/mllib/sample_kmeans_data.txt`

## How was this patch tested?

manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #11844 from zhengruifeng/doc_bkm.

## What changes were proposed in this pull request?
1, Add python example for OneVsRest
2, remove args-parsing

## How was this patch tested?
manual tests
`./bin/spark-submit examples/src/main/python/ml/one_vs_rest_example.py`

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12920 from zhengruifeng/ovr_pe.

[SPARK-15231][SQL] Document the semantic of saveAsTable and insertInto and don't drop columns silently

## What changes were proposed in this pull request?

This PR adds documents about the different behaviors between `insertInto` and `saveAsTable`, and throws an exception when the user try to add too man columns using `saveAsTable with append`.

## How was this patch tested?

Unit tests added in this PR.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #13013 from zsxwing/SPARK-15231.

## What changes were proposed in this pull request?

Fix doctest issue, short param description, and tag items as Experimental

## How was this patch tested?

build docs locally & doctests

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

Closes #12964 from holdenk/SPARK-15189-ml.Evaluation-PyDoc-issues.

[SPARK-15235][WEBUI] Corresponding row cannot be highlighted even though cursor is on the job on Web UI's timeline

## What changes were proposed in this pull request?

To extract job descriptions and stage name, there are following regular expressions in timeline-view.js

```
var jobIdText = $($(baseElem).find(".application-timeline-content")[0]).text();
var jobId = jobIdText.match("\\(Job (\\d+)\\)")[1];
...
var stageIdText = $($(baseElem).find(".job-timeline-content")[0]).text();
var stageIdAndAttempt = stageIdText.match("\\(Stage (\\d+\\.\\d+)\\)")[1].split(".");
```

But if job descriptions include patterns like "(Job x)" or stage names include patterns like "(Stage x.y)", the regular expressions cannot be match as we expected, ending up with corresponding row cannot be highlighted even though we move the cursor onto the job on Web UI's timeline.

## How was this patch tested?

Manually tested with spark-shell and Web UI.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #13016 from sarutak/SPARK-15235.

## What changes were proposed in this pull request?
1. Fix code style
2. remove volatile of elementsRead method because there is only one thread to use it.
3. avoid volatile of _elementsRead because Collection increases number of  _elementsRead when it insert a element. It is very expensive. So we can avoid it.

After this PR, I will push another PR for branch 1.6.
## How was this patch tested?
unit tests

Author: Lianhui Wang <lianhuiwang09@gmail.com>

Closes #13020 from lianhuiwang/SPARK-4452-hotfix.

## What changes were proposed in this pull request?

We use the tree string of an SparkPlan as the name of cached DataFrame, that could be very long, cause the browser to be not responsive. This PR will limit the length of the name to 1000 characters.

## How was this patch tested?

Here is how the UI looks right now:

![ui](https://cloud.githubusercontent.com/assets/40902/15163355/d5640f9c-16bc-11e6-8655-809af8a4fed1.png)

Author: Davies Liu <davies@databricks.com>

Closes #13033 from davies/cache_name.

## What changes were proposed in this pull request?

This issue fixes the error message indentation consistently with other set queries (EXCEPT/INTERSECT).

**Before (4 lines)**
```
scala> sql("(select 1) union (select 1, 2)").head
org.apache.spark.sql.AnalysisException:
Unions can only be performed on tables with the same number of columns,
 but one table has '2' columns and another table has
 '1' columns;
```

**After (one-line)**
```
scala> sql("(select 1) union (select 1, 2)").head
org.apache.spark.sql.AnalysisException: Unions can only be performed on tables with the same number of columns, but one table has '2' columns and another table has '1' columns;
```
**Reference (EXCEPT / INTERSECT)**
```
scala> sql("(select 1) intersect (select 1, 2)").head
org.apache.spark.sql.AnalysisException: Intersect can only be performed on tables with the same number of columns, but the left table has 1 columns and the right has 2;
```

## How was this patch tested?

Manual.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13043 from dongjoon-hyun/SPARK-15265.

## What changes were proposed in this pull request?

This PR removes the old `json(path: String)` API which is covered by the new `json(paths: String*)`.

## How was this patch tested?

Jenkins tests (existing tests should cover this)

Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>

Closes #13040 from HyukjinKwon/SPARK-15250.

## What changes were proposed in this pull request?
This patch removes experimental tag from DataFrameReader and DataFrameWriter, and explicitly tags a few methods added for structured streaming as experimental.

## How was this patch tested?
N/A

Author: Reynold Xin <rxin@databricks.com>

Closes #13038 from rxin/SPARK-15261.

[SPARK-14476][SQL] Improve the physical plan visualization by adding meta info like table name and file path for data source.

## What changes were proposed in this pull request?
Improve the physical plan visualization by adding meta info like table name and file path for data source.

Meta info InputPaths and TableName are newly added. Example:
```
scala> spark.range(10).write.saveAsTable("tt")
scala> spark.sql("select * from tt").explain()
== Physical Plan ==
WholeStageCodegen
:  +- BatchedScan HadoopFiles[id#13L] Format: ParquetFormat, InputPaths: file:/home/xzhong10/spark-linux/assembly/spark-warehouse/tt, PushedFilters: [], ReadSchema: struct<id:bigint>, TableName: default.tt
```

## How was this patch tested?

manual tests.

Changes for UI:
Before:
![ui_before_change](https://cloud.githubusercontent.com/assets/2595532/15064559/3d423e3c-1388-11e6-8099-7803ef496c4d.jpg)

After:
![fix_long_string](https://cloud.githubusercontent.com/assets/2595532/15133566/8ad09e26-1696-11e6-939c-99b908249b9d.jpg)

![for_load](https://cloud.githubusercontent.com/assets/2595532/15157224/3ba95c98-171d-11e6-885a-de0ee8dec27c.jpg)

Author: Sean Zhong <clockfly@gmail.com>

Closes #12947 from clockfly/spark-14476.

[SPARK-14837][SQL][STREAMING] Added support in file stream source for reading new files added to subdirs

## What changes were proposed in this pull request?
Currently, file stream source can only find new files if they appear in the directory given to the source, but not if they appear in subdirs. This PR add support for providing glob patterns when creating file stream source so that it can find new files in nested directories based on the glob pattern.

## How was this patch tested?

Unit test that tests when new files are discovered with globs and partitioned directories.

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

Closes #12616 from tdas/SPARK-14837.

https://issues.apache.org/jira/browse/SPARK-14936

## What changes were proposed in this pull request?

FlumePollingStreamSuite contains two tests which run for a minute each. This seems excessively slow and we should speed it up if possible.

In this PR, instead of creating `StreamingContext` directly from `conf`, here an underlying `SparkContext` is created before all and it is used to create  each`StreamingContext`.

Running time is reduced by avoiding multiple `SparkContext` creations and destroys.

## How was this patch tested?

Tested on my local machine running `testOnly *.FlumePollingStreamSuite`

Author: Xin Ren <iamshrek@126.com>

Closes #12845 from keypointt/SPARK-14936.

[SPARK-15249][SQL] Use FunctionResource instead of (String, String) in CreateFunction and CatalogFunction for resource

Use FunctionResource instead of (String, String) in CreateFunction and CatalogFunction for resource
see: TODO's here
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/interface.scala#L36
https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/command/functions.scala#L42

Existing tests

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #13024 from techaddict/SPARK-15249.