## What changes were proposed in this pull request?

This should be credited to mvervuurt. The main purpose of this PR is
 - simply to include the change for the same instance in `DataFrameReader` just to match up.
 - just avoid duplicately verifying the PR (as I already did).

The documentation for both should be the same because both assume the `properties` should be  the same `dict` for the same option.

## How was this patch tested?

Manually building Python documentation.

This will produce the output as below:

- `DataFrameReader`

![2016-08-17 11 12 00](https://cloud.githubusercontent.com/assets/6477701/17722764/b3f6568e-646f-11e6-8b75-4fb672f3f366.png)

- `DataFrameWriter`

![2016-08-17 11 12 10](https://cloud.githubusercontent.com/assets/6477701/17722765/b58cb308-646f-11e6-841a-32f19800d139.png)

Closes #14624

Author: hyukjinkwon <gurwls223@gmail.com>
Author: mvervuurt <m.a.vervuurt@gmail.com>

Closes #14677 from HyukjinKwon/typo-python.

## What's Been Changed

The PR corrects several broken or missing class references in the Python API docs. It also correct formatting problems.

For example, you can see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.registerFunction) how Sphinx is not picking up the reference to `DataType`. That's because the reference is relative to the current module, whereas `DataType` is in a different module.

You can also see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.createDataFrame) how the formatting for byte, tinyint, and so on is italic instead of monospace. That's because in ReST single backticks just make things italic, unlike in Markdown.

## Testing

I tested this PR by [building the Python docs](https://github.com/apache/spark/tree/master/docs#generating-the-documentation-html) and reviewing the results locally in my browser. I confirmed that the broken or missing class references were resolved, and that the formatting was corrected.

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

Closes #14393 from nchammas/python-docstring-fixes.

## What changes were proposed in this pull request?

Adds an quoteAll option for writing CSV which will quote all fields.
See https://issues.apache.org/jira/browse/SPARK-13638

## How was this patch tested?

Added a test to verify the output columns are quoted for all fields in the Dataframe

Author: Jurriaan Pruis <email@jurriaanpruis.nl>

Closes #13374 from jurriaan/csv-quote-all.

## What changes were proposed in this pull request?

This PR corrects ORC compression option for PySpark as well. I think this was missed mistakenly in https://github.com/apache/spark/pull/13948.

## How was this patch tested?

N/A

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #13963 from HyukjinKwon/minor-orc-compress.

#### What changes were proposed in this pull request?
In Python API, we have the same issue. Thanks for identifying this issue, zsxwing ! Below is an example:
```Python
spark.read.format('json').load('python/test_support/sql/people.json')
```
#### How was this patch tested?
Existing test cases cover the changes by this PR

Author: gatorsmile <gatorsmile@gmail.com>

Closes #13965 from gatorsmile/optionPaths.

[SPARK-16266][SQL][STREAING] Moved DataStreamReader/Writer from pyspark.sql to pyspark.sql.streaming

## What changes were proposed in this pull request?

- Moved DataStreamReader/Writer from pyspark.sql to pyspark.sql.streaming to make them consistent with scala packaging
- Exposed the necessary classes in sql.streaming package so that they appear in the docs
- Added pyspark.sql.streaming module to the docs

## How was this patch tested?
- updated unit tests.
- generated docs for testing visibility of pyspark.sql.streaming classes.

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

Closes #13955 from tdas/SPARK-16266.

## What changes were proposed in this pull request?

There are some duplicated code for options in DataFrame reader/writer API, this PR clean them up, it also fix a bug for `escapeQuotes` of csv().

## How was this patch tested?

Existing tests.

Author: Davies Liu <davies@databricks.com>

Closes #13948 from davies/csv_options.

## What changes were proposed in this pull request?
This is a follow-up to https://github.com/apache/spark/pull/13795 to properly set CSV options in Python API. As part of this, I also make the Python option setting for both CSV and JSON more robust against positional errors.

## How was this patch tested?
N/A

Author: Reynold Xin <rxin@databricks.com>

Closes #13800 from rxin/SPARK-13792-2.

## What changes were proposed in this pull request?
This pull request adds a new option (maxMalformedLogPerPartition) in CSV reader to limit the maximum of logging message Spark generates per partition for malformed records.

The error log looks something like
```
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: More than 10 malformed records have been found on this partition. Malformed records from now on will not be logged.
```

Closes #12173

## How was this patch tested?
Manually tested.

Author: Reynold Xin <rxin@databricks.com>

Closes #13795 from rxin/SPARK-13792.

## What changes were proposed in this pull request?

- Fixed bug in Python API of DataStreamReader.  Because a single path was being converted to a array before calling Java DataStreamReader method (which takes a string only), it gave the following error.
```
File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/readwriter.py", line 947, in pyspark.sql.readwriter.DataStreamReader.json
Failed example:
    json_sdf = spark.readStream.json(os.path.join(tempfile.mkdtemp(), 'data'),                 schema = sdf_schema)
Exception raised:
    Traceback (most recent call last):
      File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/doctest.py", line 1253, in __run
        compileflags, 1) in test.globs
      File "<doctest pyspark.sql.readwriter.DataStreamReader.json[0]>", line 1, in <module>
        json_sdf = spark.readStream.json(os.path.join(tempfile.mkdtemp(), 'data'),                 schema = sdf_schema)
      File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/readwriter.py", line 963, in json
        return self._df(self._jreader.json(path))
      File "/Users/tdas/Projects/Spark/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__
        answer, self.gateway_client, self.target_id, self.name)
      File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/utils.py", line 63, in deco
        return f(*a, **kw)
      File "/Users/tdas/Projects/Spark/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 316, in get_return_value
        format(target_id, ".", name, value))
    Py4JError: An error occurred while calling o121.json. Trace:
    py4j.Py4JException: Method json([class java.util.ArrayList]) does not exist
    	at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318)
    	at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:326)
    	at py4j.Gateway.invoke(Gateway.java:272)
    	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
    	at py4j.commands.CallCommand.execute(CallCommand.java:79)
    	at py4j.GatewayConnection.run(GatewayConnection.java:211)
    	at java.lang.Thread.run(Thread.java:744)
```

- Reduced code duplication between DataStreamReader and DataFrameWriter
- Added missing Python doctests

## How was this patch tested?
New tests

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

Closes #13703 from tdas/SPARK-15981.

Renamed for simplicity, so that its obvious that its related to streaming.

Existing unit tests.

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

Closes #13673 from tdas/SPARK-15953.

[SPARK-15933][SQL][STREAMING] Refactored DF reader-writer to use readStream and writeStream for streaming DFs

## What changes were proposed in this pull request?
Currently, the DataFrameReader/Writer has method that are needed for streaming and non-streaming DFs. This is quite awkward because each method in them through runtime exception for one case or the other. So rather having half the methods throw runtime exceptions, its just better to have a different reader/writer API for streams.

- [x] Python API!!

## How was this patch tested?
Existing unit tests + two sets of unit tests for DataFrameReader/Writer and DataStreamReader/Writer.

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

Closes #13653 from tdas/SPARK-15933.

## What changes were proposed in this pull request?

We want to maintain API compatibility for DataFrameReader.text, and will introduce a new API called DataFrameReader.textFile which returns Dataset[String].

affected PRs:
https://github.com/apache/spark/pull/11731
https://github.com/apache/spark/pull/13104
https://github.com/apache/spark/pull/13184

## How was this patch tested?

N/A

Author: Wenchen Fan <wenchen@databricks.com>

Closes #13604 from cloud-fan/revert.

## What changes were proposed in this pull request?

This PR

1. Adds the documentations for some missing options, `inferSchema` and `mergeSchema` for Python and Scala.

2. Fiixes `[[DataFrame]]` to ```:class:`DataFrame` ``` so that this can be shown

  - from
    ![2016-06-09 9 31 16](https://cloud.githubusercontent.com/assets/6477701/15929721/8b864734-2e89-11e6-83f6-207527de4ac9.png)

  - to (with class link)
    ![2016-06-09 9 31 00](https://cloud.githubusercontent.com/assets/6477701/15929717/8a03d728-2e89-11e6-8a3f-08294964db22.png)

  (Please refer [the latest documentation](https://people.apache.org/~pwendell/spark-nightly/spark-master-docs/latest/api/python/pyspark.sql.html))

3. Moves `mergeSchema` option to `ParquetOptions` with removing unused options, `metastoreSchema` and `metastoreTableName`.

  They are not used anymore. They were removed in https://github.com/apache/spark/commit/e720dda42e806229ccfd970055c7b8a93eb447bf and there are no use cases as below:

  ```bash
  grep -r -e METASTORE_SCHEMA -e \"metastoreSchema\" -e \"metastoreTableName\" -e METASTORE_TABLE_NAME .
  ```

  ```
  ./sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala:  private[sql] val METASTORE_SCHEMA = "metastoreSchema"
  ./sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala:  private[sql] val METASTORE_TABLE_NAME = "metastoreTableName"
  ./sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala:        ParquetFileFormat.METASTORE_TABLE_NAME -> TableIdentifier(
```

  It only sets `metastoreTableName` in the last case but does not use the table name.

4. Sets the correct default values (in the documentation) for `compression` option for ORC(`snappy`, see [OrcOptions.scala#L33-L42](https://github.com/apache/spark/blob/3ded5bc4db2badc9ff49554e73421021d854306b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcOptions.scala#L33-L42)) and Parquet(`the value specified in SQLConf`, see [ParquetOptions.scala#L38-L47](https://github.com/apache/spark/blob/3ded5bc4db2badc9ff49554e73421021d854306b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetOptions.scala#L38-L47)) and `columnNameOfCorruptRecord` for JSON(`the value specified in SQLConf`, see [JsonFileFormat.scala#L53-L55](https://github.com/apache/spark/blob/4538443e276597530a27c6922e48503677b13956/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala#L53-L55) and [JsonFileFormat.scala#L105-L106](https://github.com/apache/spark/blob/4538443e276597530a27c6922e48503677b13956/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala#L105-L106)).

## How was this patch tested?

Existing tests should cover this.

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

Closes #13576 from HyukjinKwon/SPARK-15840.

## What changes were proposed in this pull request?
This pr is to add doc for turning off quotations because this behavior is different from `com.databricks.spark.csv`.

## How was this patch tested?
Check behavior  to put an empty string in csv options.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #13616 from maropu/SPARK-15585-2.

This reverts commit b7e8d1cb.

## What changes were proposed in this pull request?
This pr fixes the behaviour of `format("csv").option("quote", null)` along with one of spark-csv.
Also, it explicitly sets default values for CSV options in python.

## How was this patch tested?
Added tests in CSVSuite.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #13372 from maropu/SPARK-15585.

## What changes were proposed in this pull request?
Currently structured streaming only supports append output mode.  This PR adds the following.

- Added support for Complete output mode in the internal state store, analyzer and planner.
- Added public API in Scala and Python for users to specify output mode
- Added checks for unsupported combinations of output mode and DF operations
  - Plans with no aggregation should support only Append mode
  - Plans with aggregation should support only Update and Complete modes
  - Default output mode is Append mode (**Question: should we change this to automatically set to Complete mode when there is aggregation?**)
- Added support for Complete output mode in Memory Sink. So Memory Sink internally supports append and complete, update. But from public API only Complete and Append output modes are supported.

## How was this patch tested?
Unit tests in various test suites
- StreamingAggregationSuite: tests for complete mode
- MemorySinkSuite: tests for checking behavior in Append and Complete modes.
- UnsupportedOperationSuite: tests for checking unsupported combinations of DF ops and output modes
- DataFrameReaderWriterSuite: tests for checking that output mode cannot be called on static DFs
- Python doc test and existing unit tests modified to call write.outputMode.

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

Closes #13286 from tdas/complete-mode.

## What changes were proposed in this pull request?

This reverts commit c24b6b67. Sent a PR to run Jenkins tests due to the revert conflicts of `dev/deps/spark-deps-hadoop*`.

## How was this patch tested?

Jenkins unit tests, integration tests, manual tests)

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #13417 from zsxwing/revert-SPARK-11753.

## What changes were proposed in this pull request?

`a` -> `an`

I use regex to generate potential error lines:
`grep -in ' a [aeiou]' mllib/src/main/scala/org/apache/spark/ml/*/*scala`
and review them line by line.

## How was this patch tested?

local build
`lint-java` checking

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #13317 from zhengruifeng/a_an.

## What changes were proposed in this pull request?

Default QuoteEscapingEnabled flag to true when writing CSV and add an escapeQuotes option to be able to change this.

See https://github.com/uniVocity/univocity-parsers/blob/f3eb2af26374940e60d91d1703bde54619f50c51/src/main/java/com/univocity/parsers/csv/CsvWriterSettings.java#L231-L247

This change is needed to be able to write RFC 4180 compatible CSV files (https://tools.ietf.org/html/rfc4180#section-2)

https://issues.apache.org/jira/browse/SPARK-15493

## How was this patch tested?

Added a test that verifies the output is quoted correctly.

Author: Jurriaan Pruis <email@jurriaanpruis.nl>

Closes #13267 from jurriaan/quote-escaping.

## What changes were proposed in this pull request?

Jackson suppprts `allowNonNumericNumbers` option to parse non-standard non-numeric numbers such as "NaN", "Infinity", "INF".  Currently used Jackson version (2.5.3) doesn't support it all. This patch upgrades the library and make the two ignored tests in `JsonParsingOptionsSuite` passed.

## How was this patch tested?

`JsonParsingOptionsSuite`.

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

Closes #9759 from viirya/fix-json-nonnumric.

## What changes were proposed in this pull request?
This patch is a follow-up to https://github.com/apache/spark/pull/13104 and adds documentation to clarify the semantics of read.text with respect to partitioning.

## How was this patch tested?
N/A

Author: Reynold Xin <rxin@databricks.com>

Closes #13184 from rxin/SPARK-14463.

## What changes were proposed in this pull request?

Update the unit test code, examples, and documents to remove calls to deprecated method `dataset.registerTempTable`.

## How was this patch tested?

This PR only changes the unit test code, examples, and comments. It should be safe.
This is a follow up of PR https://github.com/apache/spark/pull/12945 which was merged.

Author: Sean Zhong <seanzhong@databricks.com>

Closes #13098 from clockfly/spark-15171-remove-deprecation.

## 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?

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.

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?
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?

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.

## What changes were proposed in this pull request?

https://issues.apache.org/jira/browse/SPARK-15050

This PR adds function parameters for Python API for reading and writing `csv()`.

## How was this patch tested?

This was tested by `./dev/run_tests`.

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

Closes #12834 from HyukjinKwon/SPARK-15050.

## What changes were proposed in this pull request?

This PR adds the explanation and documentation for CSV options for reading and writing.

## How was this patch tested?

Style tests with `./dev/run_tests` for documentation style.

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

Closes #12817 from HyukjinKwon/SPARK-13425.

## What changes were proposed in this pull request?

This PR adds Python APIs for:
 - `ContinuousQueryManager`
 - `ContinuousQueryException`

The `ContinuousQueryException` is a very basic wrapper, it doesn't provide the functionality that the Scala side provides, but it follows the same pattern for `AnalysisException`.

For `ContinuousQueryManager`, all APIs are provided except for registering listeners.

This PR also attempts to fix test flakiness by stopping all active streams just before tests.

## How was this patch tested?

Python Doc tests and unit tests

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #12673 from brkyvz/pyspark-cqm.

## What changes were proposed in this pull request?

```
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.0.0-SNAPSHOT
      /_/

Using Python version 2.7.5 (default, Mar  9 2014 22:15:05)
SparkSession available as 'spark'.
>>> spark
<pyspark.sql.session.SparkSession object at 0x101f3bfd0>
>>> spark.sql("SHOW TABLES").show()
...
+---------+-----------+
|tableName|isTemporary|
+---------+-----------+
|      src|      false|
+---------+-----------+

>>> spark.range(1, 10, 2).show()
+---+
| id|
+---+
|  1|
|  3|
|  5|
|  7|
|  9|
+---+
```
**Note**: This API is NOT complete in its current state. In particular, for now I left out the `conf` and `catalog` APIs, which were added later in Scala. These will be added later before 2.0.

## How was this patch tested?

Python tests.

Author: Andrew Or <andrew@databricks.com>

Closes #12746 from andrewor14/python-spark-session.

## What changes were proposed in this pull request?

In Python, the `option` and `options` method of `DataFrameReader` and `DataFrameWriter` were sending the string "None" instead of `null` when passed `None`, therefore making it impossible to send an actual `null`. This fixes that problem.

This is based on #11305 from mathieulongtin.

## How was this patch tested?

Added test to readwriter.py.

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

Closes #12494 from viirya/py-df-none-option.

## What changes were proposed in this pull request?

This patch provides a first cut of python APIs for structured streaming. This PR provides the new classes:
 - ContinuousQuery
 - Trigger
 - ProcessingTime
in pyspark under `pyspark.sql.streaming`.

In addition, it contains the new methods added under:
 -  `DataFrameWriter`
     a) `startStream`
     b) `trigger`
     c) `queryName`

 -  `DataFrameReader`
     a) `stream`

 - `DataFrame`
    a) `isStreaming`

This PR doesn't contain all methods exposed for `ContinuousQuery`, for example:
 - `exception`
 - `sourceStatuses`
 - `sinkStatus`

They may be added in a follow up.

This PR also contains some very minor doc fixes in the Scala side.

## How was this patch tested?

Python doc tests

TODO:
 - [ ] verify Python docs look good

Author: Burak Yavuz <brkyvz@gmail.com>
Author: Burak Yavuz <burak@databricks.com>

Closes #12320 from brkyvz/stream-python.

[SPARK-14231] [SQL] JSON data source infers floating-point values as a double when they do not fit in a decimal

## What changes were proposed in this pull request?

https://issues.apache.org/jira/browse/SPARK-14231

Currently, JSON data source supports to infer `DecimalType` for big numbers and `floatAsBigDecimal` option which reads floating-point values as `DecimalType`.

But there are few restrictions in Spark `DecimalType` below:

1. The precision cannot be bigger than 38.
2. scale cannot be bigger than precision.

Currently, both restrictions are not being handled.

This PR handles the cases by inferring them as `DoubleType`. Also, the option name was changed from `floatAsBigDecimal` to `prefersDecimal` as suggested [here](https://issues.apache.org/jira/browse/SPARK-14231?focusedCommentId=15215579&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15215579).

So, the codes below:

```scala
def doubleRecords: RDD[String] =
  sqlContext.sparkContext.parallelize(
    s"""{"a": 1${"0" * 38}, "b": 0.01}""" ::
    s"""{"a": 2${"0" * 38}, "b": 0.02}""" :: Nil)

val jsonDF = sqlContext.read
  .option("prefersDecimal", "true")
  .json(doubleRecords)
jsonDF.printSchema()
```

produces below:

- **Before**

```scala
org.apache.spark.sql.AnalysisException: Decimal scale (2) cannot be greater than precision (1).;
	at org.apache.spark.sql.types.DecimalType.<init>(DecimalType.scala:44)
	at org.apache.spark.sql.execution.datasources.json.InferSchema$.org$apache$spark$sql$execution$datasources$json$InferSchema$$inferField(InferSchema.scala:144)
	at org.apache.spark.sql.execution.datasources.json.InferSchema$.org$apache$spark$sql$execution$datasources$json$InferSchema$$inferField(InferSchema.scala:108)
	at
...
```

- **After**

```scala
root
 |-- a: double (nullable = true)
 |-- b: double (nullable = true)
```

## How was this patch tested?

Unit tests were used and `./dev/run_tests` for coding style tests.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #12030 from HyukjinKwon/SPARK-14231.

## What changes were proposed in this pull request?

https://issues.apache.org/jira/browse/SPARK-13953

Currently, JSON data source creates a new field in `PERMISSIVE` mode for storing malformed string.
This field can be renamed via `spark.sql.columnNameOfCorruptRecord` option but it is a global configuration.

This PR make that option can be applied per read and can be specified via `option()`. This will overwrites `spark.sql.columnNameOfCorruptRecord` if it is set.

## How was this patch tested?

Unit tests were used and `./dev/run_tests` for coding style tests.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #11881 from HyukjinKwon/SPARK-13953.

## What changes were proposed in this pull request?

Currently, there is no way to control the behaviour when fails to parse corrupt records in JSON data source .

This PR adds the support for parse modes just like CSV data source. There are three modes below:

- `PERMISSIVE` :  When it fails to parse, this sets `null` to to field. This is a default mode when it has been this mode.
- `DROPMALFORMED`: When it fails to parse, this drops the whole record.
- `FAILFAST`: When it fails to parse, it just throws an exception.

This PR also make JSON data source share the `ParseModes` in CSV data source.

## How was this patch tested?

Unit tests were used and `./dev/run_tests` for code style tests.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #11756 from HyukjinKwon/SPARK-13764.

## What changes were proposed in this pull request?

This PR adds the support to specify compression codecs for both ORC and Parquet.

## How was this patch tested?

unittests within IDE and code style tests with `dev/run_tests`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #11464 from HyukjinKwon/SPARK-13543.