## What changes were proposed in this pull request?

https://github.com/apache/spark/pull/18266 add a new feature to support read JDBC table use custom schema, but we must specify all the fields. For simplicity, this PR support  specify partial fields.

## How was this patch tested?
unit tests

Author: Yuming Wang <wgyumg@gmail.com>

Closes #19231 from wangyum/SPARK-22002.

## What changes were proposed in this pull request?

Auto generated Oracle schema some times not we expect:

- `number(1)` auto mapped to BooleanType, some times it's not we expect, per [SPARK-20921](https://issues.apache.org/jira/browse/SPARK-20921).
-  `number` auto mapped to Decimal(38,10), It can't read big data, per [SPARK-20427](https://issues.apache.org/jira/browse/SPARK-20427).

This PR fix this issue by custom schema as follows:
```scala
val props = new Properties()
props.put("customSchema", "ID decimal(38, 0), N1 int, N2 boolean")
val dfRead = spark.read.schema(schema).jdbc(jdbcUrl, "tableWithCustomSchema", props)
dfRead.show()
```
or
```sql
CREATE TEMPORARY VIEW tableWithCustomSchema
USING org.apache.spark.sql.jdbc
OPTIONS (url '$jdbcUrl', dbTable 'tableWithCustomSchema', customSchema'ID decimal(38, 0), N1 int, N2 boolean')
```

## How was this patch tested?

unit tests

Author: Yuming Wang <wgyumg@gmail.com>

Closes #18266 from wangyum/SPARK-20427.

## What changes were proposed in this pull request?
```
echo '{"field": 1}
{"field": 2}
{"field": "3"}' >/tmp/sample.json
```

```scala
import org.apache.spark.sql.types._

val schema = new StructType()
  .add("field", ByteType)
  .add("_corrupt_record", StringType)

val file = "/tmp/sample.json"

val dfFromFile = spark.read.schema(schema).json(file)

scala> dfFromFile.show(false)
+-----+---------------+
|field|_corrupt_record|
+-----+---------------+
|1    |null           |
|2    |null           |
|null |{"field": "3"} |
+-----+---------------+

scala> dfFromFile.filter($"_corrupt_record".isNotNull).count()
res1: Long = 0

scala> dfFromFile.filter($"_corrupt_record".isNull).count()
res2: Long = 3
```
When the `requiredSchema` only contains `_corrupt_record`, the derived `actualSchema` is empty and the `_corrupt_record` are all null for all rows. This PR captures above situation and raise an exception with a reasonable workaround messag so that users can know what happened and how to fix the query.

## How was this patch tested?

Added test case.

Author: Jen-Ming Chung <jenmingisme@gmail.com>

Closes #18865 from jmchung/SPARK-21610.

## What changes were proposed in this pull request?

Since [SPARK-15639](https://github.com/apache/spark/pull/13701), `spark.sql.parquet.cacheMetadata` and `PARQUET_CACHE_METADATA` is not used. This PR removes from SQLConf and docs.

## How was this patch tested?

Pass the existing Jenkins.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #19129 from dongjoon-hyun/SPARK-13656.

## What changes were proposed in this pull request?

All built-in data sources support `Partition Discovery`. We had better update the document to give the users more benefit clearly.

**AFTER**

<img width="906" alt="1" src="https://user-images.githubusercontent.com/9700541/30083628-14278908-9244-11e7-98dc-9ad45fe233a9.png">

## How was this patch tested?

```
SKIP_API=1 jekyll serve --watch
```

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #19139 from dongjoon-hyun/partitiondiscovery.

Add an option to the JDBC data source to initialize the environment of the remote database session

## What changes were proposed in this pull request?

This proposes an option to the JDBC datasource, tentatively called " sessionInitStatement" to implement the functionality of session initialization present for example in the Sqoop connector for Oracle (see https://sqoop.apache.org/docs/1.4.6/SqoopUserGuide.html#_oraoop_oracle_session_initialization_statements ) . After each database session is opened to the remote DB, and before starting to read data, this option executes a custom SQL statement (or a PL/SQL block in the case of Oracle).

See also https://issues.apache.org/jira/browse/SPARK-21519

## How was this patch tested?

Manually tested using Spark SQL data source and Oracle JDBC

Author: LucaCanali <luca.canali@cern.ch>

Closes #18724 from LucaCanali/JDBC_datasource_sessionInitStatement.

## What changes were proposed in this pull request?

This commit adds a new argument for IllegalArgumentException message. This recent commit added the argument:

[https://github.com/apache/spark/commit/dcac1d57f0fd05605edf596c303546d83062a352](https://github.com/apache/spark/commit/dcac1d57f0fd05605edf596c303546d83062a352)

## How was this patch tested?

Unit test have been passed

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

Author: Marcos P. Sanchez <mpenate@stratio.com>

Closes #18862 from mpenate/feature/exception-errorifexists.

## What changes were proposed in this pull request?
This pr added documents about unsupported functions in Hive UDF/UDTF/UDAF.
This pr relates to #18768 and #18527.

## How was this patch tested?
N/A

Author: Takeshi Yamamuro <yamamuro@apache.org>

Closes #18792 from maropu/HOTFIX-20170731.

## What changes were proposed in this pull request?

Few changes to the Structured Streaming documentation
- Clarify that the entire stream input table is not materialized
- Add information for Ganglia
- Add Kafka Sink to the main docs
- Removed a couple of leftover experimental tags
- Added more associated reading material and talk videos.

In addition, https://github.com/apache/spark/pull/16856 broke the link to the RDD programming guide in several places while renaming the page. This PR fixes those sameeragarwal cloud-fan.
- Added a redirection to avoid breaking internal and possible external links.
- Removed unnecessary redirection pages that were there since the separate scala, java, and python programming guides were merged together in 2013 or 2014.

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

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

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

Closes #18485 from tdas/SPARK-21267.

## What changes were proposed in this pull request?

doc only change

## How was this patch tested?

manually

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #18312 from felixcheung/sqljsonwholefiledoc.

## What changes were proposed in this pull request?

- Add Scala, Python and Java examples for `partitionBy`, `sortBy` and `bucketBy`.
- Add _Bucketing, Sorting and Partitioning_ section to SQL Programming Guide
- Remove bucketing from Unsupported Hive Functionalities.

## How was this patch tested?

Manual tests, docs build.

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

Closes #17938 from zero323/DOCS-BUCKETING-AND-PARTITIONING.

[SPARK-20888][SQL][DOCS] Document change of default setting of spark.sql.hive.caseSensitiveInferenceMode

(Link to Jira: https://issues.apache.org/jira/browse/SPARK-20888)

## What changes were proposed in this pull request?

Document change of default setting of spark.sql.hive.caseSensitiveInferenceMode configuration key from NEVER_INFO to INFER_AND_SAVE in the Spark SQL 2.1 to 2.2 migration notes.

Author: Michael Allman <michael@videoamp.com>

Closes #18112 from mallman/spark-20888-document_infer_and_save.

## What changes were proposed in this pull request?

Typos at a couple of place in the docs.

## How was this patch tested?

build including docs

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

Author: ymahajan <ymahajan@snappydata.io>

Closes #17690 from ymahajan/master.

## What changes were proposed in this pull request?

This PR proposes corrections related to JSON APIs as below:

- Rendering links in Python documentation
- Replacing `RDD` to `Dataset` in programing guide
- Adding missing description about JSON Lines consistently in `DataFrameReader.json` in Python API
- De-duplicating little bit of `DataFrameReader.json` in Scala/Java API

## How was this patch tested?

Manually build the documentation via `jekyll build`. Corresponding snapstops will be left on the codes.

Note that currently there are Javadoc8 breaks in several places. These are proposed to be handled in https://github.com/apache/spark/pull/17477. So, this PR does not fix those.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17602 from HyukjinKwon/minor-json-documentation.

## What changes were proposed in this pull request?

Since SPARK-18112 and SPARK-13446, Apache Spark starts to support reading Hive metastore 2.0 ~ 2.1.1. This updates the docs.

## How was this patch tested?

N/A

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #17612 from dongjoon-hyun/metastore.

[SPARK-10849][SQL] Adds option to the JDBC data source write for user to specify database column type for the create table

## What changes were proposed in this pull request?
Currently JDBC data source creates tables in the target database using the default type mapping, and the JDBC dialect mechanism.  If users want to specify different database data type for only some of columns, there is no option available. In scenarios where default mapping does not work, users are forced to create tables on the target database before writing. This workaround is probably not acceptable from a usability point of view. This PR is to provide a user-defined type mapping for specific columns.

The solution is to allow users to specify database column data type for the create table  as JDBC datasource option(createTableColumnTypes) on write. Data type information can be specified in the same format as table schema DDL format (e.g: `name CHAR(64), comments VARCHAR(1024)`).

All supported target database types can not be specified ,  the data types has to be valid spark sql data types also.  For example user can not specify target database  CLOB data type. This will be supported in the follow-up PR.

Example:
```Scala
df.write
.option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
.jdbc(url, "TEST.DBCOLTYPETEST", properties)
```
## How was this patch tested?
Added new test cases to the JDBCWriteSuite

Author: sureshthalamati <suresh.thalamati@gmail.com>

Closes #16209 from sureshthalamati/jdbc_custom_dbtype_option_json-spark-10849.

## What changes were proposed in this pull request?

Update doc for R, programming guide. Clarify default behavior for all languages.

## How was this patch tested?

manually

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #17128 from felixcheung/jsonwholefiledoc.

## What changes were proposed in this pull request?

Removed duplicated lines in sql python example and found a typo.

## How was this patch tested?

Searched for other typo's in the page to minimize PR's.

Author: Boaz Mohar <boazmohar@gmail.com>

Closes #17066 from boazmohar/doc-fix.

## What changes were proposed in this pull request?

https://spark.apache.org/docs/latest/sql-programming-guide.html#caching-data-in-memory
In the doc, the call spark.cacheTable(“tableName”) and spark.uncacheTable(“tableName”) actually needs to be spark.catalog.cacheTable and spark.catalog.uncacheTable

## How was this patch tested?
Built the docs and verified the change shows up fine.

Author: Sunitha Kambhampati <skambha@us.ibm.com>

Closes #16919 from skambha/docChange.

### What changes were proposed in this pull request?
The case are not sensitive in JDBC options, after the PR https://github.com/apache/spark/pull/15884 is merged to Spark 2.1.

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

Author: gatorsmile <gatorsmile@gmail.com>

Closes #16734 from gatorsmile/fixDocCaseInsensitive.

## What changes were proposed in this pull request?

- A separate subsection for Aggregations under “Getting Started” in the Spark SQL programming guide. It mentions which aggregate functions are predefined and how users can create their own.
- Examples of using the `UserDefinedAggregateFunction` abstract class for untyped aggregations in Java and Scala.
- Examples of using the `Aggregator` abstract class for type-safe aggregations in Java and Scala.
- Python is not covered.
- The PR might not resolve the ticket since I do not know what exactly was planned by the author.

In total, there are four new standalone examples that can be executed via `spark-submit` or `run-example`. The updated Spark SQL programming guide references to these examples and does not contain hard-coded snippets.

## How was this patch tested?

The patch was tested locally by building the docs. The examples were run as well.

![image](https://cloud.githubusercontent.com/assets/6235869/21292915/04d9d084-c515-11e6-811a-999d598dffba.png)

Author: aokolnychyi <okolnychyyanton@gmail.com>

Closes #16329 from aokolnychyi/SPARK-16046.

## What changes were proposed in this pull request?

This PR adds a new behavior change description on `CREATE TABLE ... LOCATION` at `sql-programming-guide.md` clearly under `Upgrading From Spark SQL 1.6 to 2.0`. This change is introduced at Apache Spark 2.0.0 as [SPARK-15276](https://issues.apache.org/jira/browse/SPARK-15276).

## How was this patch tested?

```
SKIP_API=1 jekyll build
```

**Newly Added Description**
<img width="913" alt="new" src="https://cloud.githubusercontent.com/assets/9700541/21743606/7efe2b12-d4ba-11e6-8a0d-551222718ea2.png">

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #16400 from dongjoon-hyun/SPARK-18941.

## What changes were proposed in this pull request?

Today we have different syntax to create data source or hive serde tables, we should unify them to not confuse users and step forward to make hive a data source.

Please read https://issues.apache.org/jira/secure/attachment/12843835/CREATE-TABLE.pdf for  details.

TODO(for follow-up PRs):
1. TBLPROPERTIES is not added to the new syntax, we should decide if we wanna add it later.
2. `SHOW CREATE TABLE` should be updated to use the new syntax.
3. we should decide if we wanna change the behavior of `SET LOCATION`.

## How was this patch tested?

new tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #16296 from cloud-fan/create-table.

## What changes were proposed in this pull request?

This PR documents the scalable partition handling feature in the body of the programming guide.

Before this PR, we only mention it in the migration guide. It's not super clear that external datasource tables require an extra `MSCK REPAIR TABLE` command is to have per-partition information persisted since 2.1.

## How was this patch tested?

N/A.

Author: Cheng Lian <lian@databricks.com>

Closes #16424 from liancheng/scalable-partition-handling-doc.

## What changes were proposed in this pull request?
Although, currently, the saveAsTable does not provide an API to save the table as an external table from a DataFrame, we can achieve this functionality by using options on DataFrameWriter where the key for the map is the String: "path" and the value is another String which is the location of the external table itself. This can be provided before the call to saveAsTable is performed.

## How was this patch tested?
Documentation was reviewed for formatting and content after the push was performed on the branch.
![updated documentation](https://cloud.githubusercontent.com/assets/15376052/20953147/4cfcf308-bc57-11e6-807c-e21fb774a760.PNG)

Author: c-sahuja <sahuja@cloudera.com>

Closes #16185 from c-sahuja/createExternalTable.

## What changes were proposed in this pull request?

In `SQL Programming Guide`, this PR uses `TRUE` instead of `True` in SparkR and adds default values of `nullable` for `StructField` in Scala/Python/R (i.e., "Note: The default value of nullable is true."). In Java API, `nullable` is not optional.

**BEFORE**
* SPARK 2.1.0 RC1
http://people.apache.org/~pwendell/spark-releases/spark-2.1.0-rc1-docs/sql-programming-guide.html#data-types

**AFTER**

* R
<img width="916" alt="screen shot 2016-12-04 at 11 58 19 pm" src="https://cloud.githubusercontent.com/assets/9700541/20877443/abba19a6-ba7d-11e6-8984-afbe00333fb0.png">

* Scala
<img width="914" alt="screen shot 2016-12-04 at 11 57 37 pm" src="https://cloud.githubusercontent.com/assets/9700541/20877433/99ce734a-ba7d-11e6-8bb5-e8619041b09b.png">

* Python
<img width="914" alt="screen shot 2016-12-04 at 11 58 04 pm" src="https://cloud.githubusercontent.com/assets/9700541/20877440/a5c89338-ba7d-11e6-8f92-6c0ae9388d7e.png">

## How was this patch tested?

Manual.

```
cd docs
SKIP_API=1 jekyll build
open _site/index.html
```

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #16141 from dongjoon-hyun/SPARK-SQL-GUIDE.

## What changes were proposed in this pull request?

This documents the partition handling changes for Spark 2.1 and how to migrate existing tables.

## How was this patch tested?

Built docs locally.

rxin

Author: Eric Liang <ekl@databricks.com>

Closes #16074 from ericl/spark-18145.

## What changes were proposed in this pull request?
This PR is to fix incorrect `code` tag in `sql-programming-guide.md`

## How was this patch tested?
Manually.

Author: Weiqing Yang <yangweiqing001@gmail.com>

Closes #15941 from weiqingy/fixtag.

## What changes were proposed in this pull request?

This is a follow-up PR of #15868 to merge `maxConnections` option into `numPartitions` options.

## How was this patch tested?

Pass the existing tests.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #15966 from dongjoon-hyun/SPARK-18413-2.

## What changes were proposed in this pull request?

This PR adds a new JDBCOption `maxConnections` which means the maximum number of simultaneous JDBC connections allowed. This option applies only to writing with coalesce operation if needed. It defaults to the number of partitions of RDD. Previously, SQL users cannot cannot control this while Scala/Java/Python users can use `coalesce` (or `repartition`) API.

**Reported Scenario**

For the following cases, the number of connections becomes 200 and database cannot handle all of them.

```sql
CREATE OR REPLACE TEMPORARY VIEW resultview
USING org.apache.spark.sql.jdbc
OPTIONS (
  url "jdbc:oracle:thin:10.129.10.111:1521:BKDB",
  dbtable "result",
  user "HIVE",
  password "HIVE"
);
-- set spark.sql.shuffle.partitions=200
INSERT OVERWRITE TABLE resultview SELECT g, count(1) AS COUNT FROM tnet.DT_LIVE_INFO GROUP BY g
```

## How was this patch tested?

Manual. Do the followings and see Spark UI.

**Step 1 (MySQL)**
```
CREATE TABLE t1 (a INT);
CREATE TABLE data (a INT);
INSERT INTO data VALUES (1);
INSERT INTO data VALUES (2);
INSERT INTO data VALUES (3);
```

**Step 2 (Spark)**
```scala
SPARK_HOME=$PWD bin/spark-shell --driver-memory 4G --driver-class-path mysql-connector-java-5.1.40-bin.jar
scala> sql("SET spark.sql.shuffle.partitions=3")
scala> sql("CREATE OR REPLACE TEMPORARY VIEW data USING org.apache.spark.sql.jdbc OPTIONS (url 'jdbc:mysql://localhost:3306/t', dbtable 'data', user 'root', password '')")
scala> sql("CREATE OR REPLACE TEMPORARY VIEW t1 USING org.apache.spark.sql.jdbc OPTIONS (url 'jdbc:mysql://localhost:3306/t', dbtable 't1', user 'root', password '', maxConnections '1')")
scala> sql("INSERT OVERWRITE TABLE t1 SELECT a FROM data GROUP BY a")
scala> sql("CREATE OR REPLACE TEMPORARY VIEW t1 USING org.apache.spark.sql.jdbc OPTIONS (url 'jdbc:mysql://localhost:3306/t', dbtable 't1', user 'root', password '', maxConnections '2')")
scala> sql("INSERT OVERWRITE TABLE t1 SELECT a FROM data GROUP BY a")
scala> sql("CREATE OR REPLACE TEMPORARY VIEW t1 USING org.apache.spark.sql.jdbc OPTIONS (url 'jdbc:mysql://localhost:3306/t', dbtable 't1', user 'root', password '', maxConnections '3')")
scala> sql("INSERT OVERWRITE TABLE t1 SELECT a FROM data GROUP BY a")
scala> sql("CREATE OR REPLACE TEMPORARY VIEW t1 USING org.apache.spark.sql.jdbc OPTIONS (url 'jdbc:mysql://localhost:3306/t', dbtable 't1', user 'root', password '', maxConnections '4')")
scala> sql("INSERT OVERWRITE TABLE t1 SELECT a FROM data GROUP BY a")
```

![maxconnections](https://cloud.githubusercontent.com/assets/9700541/20287987/ed8409c2-aa84-11e6-8aab-ae28e63fe54d.png)

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #15868 from dongjoon-hyun/SPARK-18413.

[MINOR][DOC] Fix typos in the 'configuration', 'monitoring' and 'sql-programming-guide' documentation

## What changes were proposed in this pull request?

Fix typos in the 'configuration', 'monitoring' and 'sql-programming-guide' documentation.

## How was this patch tested?
Manually.

Author: Weiqing Yang <yangweiqing001@gmail.com>

Closes #15886 from weiqingy/fixTypo.

## What changes were proposed in this pull request?

API and programming guide doc changes for Scala, Python and R.

## How was this patch tested?

manual test

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #15629 from felixcheung/jsondoc.

[SPARK-17810][SQL] Default spark.sql.warehouse.dir is relative to local FS but can resolve as HDFS path

## What changes were proposed in this pull request?

Always resolve spark.sql.warehouse.dir as a local path, and as relative to working dir not home dir

## How was this patch tested?

Existing tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #15382 from srowen/SPARK-17810.

## What changes were proposed in this pull request?

In http://spark.apache.org/docs/latest/sql-programming-guide.html, Section "Untyped Dataset Operations (aka DataFrame Operations)"

Link to R DataFrame doesn't work that return
The requested URL /docs/latest/api/R/DataFrame.html was not found on this server.

Correct link is SparkDataFrame.html for spark 2.0

## How was this patch tested?

Manual checked.

Author: Tommy YU <tummyyu@163.com>

Closes #15543 from Wenpei/spark-18001.

## What changes were proposed in this pull request?
Add more built-in sources in sql-programming-guide.md.

## How was this patch tested?
Manually.

Author: Weiqing Yang <yangweiqing001@gmail.com>

Closes #15522 from weiqingy/dsDoc.

Change is too trivial to file a JIRA.

Author: Dhruve Ashar <dhruveashar@gmail.com>

Closes #15485 from dhruve/master.

[SPARK-17719][SPARK-17776][SQL] Unify and tie up options in a single place in JDBC datasource package

## What changes were proposed in this pull request?

This PR proposes to fix arbitrary usages among `Map[String, String]`, `Properties` and `JDBCOptions` instances for options in `execution/jdbc` package and make the connection properties exclude Spark-only options.

This PR includes some changes as below:

  - Unify `Map[String, String]`, `Properties` and `JDBCOptions` in `execution/jdbc` package to `JDBCOptions`.

- Move `batchsize`, `fetchszie`, `driver` and `isolationlevel` options into `JDBCOptions` instance.

- Document `batchSize` and `isolationlevel` with marking both read-only options and write-only options. Also, this includes minor types and detailed explanation for some statements such as url.

- Throw exceptions fast by checking arguments first rather than in execution time (e.g. for `fetchsize`).

- Exclude Spark-only options in connection properties.

## How was this patch tested?

Existing tests should cover this.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #15292 from HyukjinKwon/SPARK-17719.

## What changes were proposed in this pull request?

Global temporary view is a cross-session temporary view, which means it's shared among all sessions. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database `global_temp`(configurable via SparkConf), and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.

changes for `SessionCatalog`:

1. add a new field `gloabalTempViews: GlobalTempViewManager`, to access the shared global temp views, and the global temp db name.
2. `createDatabase` will fail if users wanna create `global_temp`, which is system preserved.
3. `setCurrentDatabase` will fail if users wanna set `global_temp`, which is system preserved.
4. add `createGlobalTempView`, which is used in `CreateViewCommand` to create global temp views.
5. add `dropGlobalTempView`, which is used in `CatalogImpl` to drop global temp view.
6. add `alterTempViewDefinition`, which is used in `AlterViewAsCommand` to update the view definition for local/global temp views.
7. `renameTable`/`dropTable`/`isTemporaryTable`/`lookupRelation`/`getTempViewOrPermanentTableMetadata`/`refreshTable` will handle global temp views.

changes for SQL commands:

1. `CreateViewCommand`/`AlterViewAsCommand` is updated to support global temp views
2. `ShowTablesCommand` outputs a new column `database`, which is used to distinguish global and local temp views.
3. other commands can also handle global temp views if they call `SessionCatalog` APIs which accepts global temp views, e.g. `DropTableCommand`, `AlterTableRenameCommand`, `ShowColumnsCommand`, etc.

changes for other public API

1. add a new method `dropGlobalTempView` in `Catalog`
2. `Catalog.findTable` can find global temp view
3. add a new method `createGlobalTempView` in `Dataset`

## How was this patch tested?

new tests in `SQLViewSuite`

Author: Wenchen Fan <wenchen@databricks.com>

Closes #14897 from cloud-fan/global-temp-view.

## What changes were proposed in this pull request?

This change modifies the implementation of DataFrameWriter.save such that it works with jdbc, and the call to jdbc merely delegates to save.

## How was this patch tested?

This was tested via unit tests in the JDBCWriteSuite, of which I added one new test to cover this scenario.

## Additional details

rxin This seems to have been most recently touched by you and was also commented on in the JIRA.

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

Author: Justin Pihony <justin.pihony@gmail.com>
Author: Justin Pihony <justin.pihony@typesafe.com>

Closes #12601 from JustinPihony/jdbc_reconciliation.

## What changes were proposed in this pull request?

Replace `fetchSize` with `fetchsize` in the docs.

## How was this patch tested?

I manually tested `fetchSize` and `fetchsize`. The latter has an effect. See also [`JdbcUtils.scala#L38`](https://github.com/apache/spark/blob/v2.0.0/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L38) for the definition of the property.

Author: Daniel Darabos <darabos.daniel@gmail.com>

Closes #14975 from darabos/patch-3.