A few minor Spark SQL Scaladoc fixes.

Author: Reynold Xin <rxin@apache.org>

Closes #1139 from rxin/sparksqldoc and squashes the following commits:

c3049d8 [Reynold Xin] Fixed line length.
66dc72c [Reynold Xin] A few minor Spark SQL Scaladoc fixes.
HOTFIX: SPARK-2208 local metrics tests can fail on fast machines

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1141 from pwendell/hotfix and squashes the following commits:

83e4c79 [Patrick Wendell] HOTFIX: SPARK-2208 local metrics tests can fail on fast machines
More minor scaladoc cleanup for Spark SQL.

Author: Reynold Xin <rxin@apache.org>

Closes #1142 from rxin/sqlclean and squashes the following commits:

67a789e [Reynold Xin] More minor scaladoc cleanup for Spark SQL.
[SQL] Improve Speed of InsertIntoHiveTable

Author: Michael Armbrust <michael@databricks.com>

Closes #1130 from marmbrus/noFunctional and squashes the following commits:

ccdb68c [Michael Armbrust] Remove functional programming and Array allocations from fast path in InsertIntoHiveTable.
[SPARK-2177][SQL] describe table result contains only one column

```
scala> hql("describe src").collect().foreach(println)

[key                 	string              	None                ]
[value               	string              	None                ]
```

The result should contain 3 columns instead of one. This screws up JDBC or even the downstream consumer of the Scala/Java/Python APIs.

I am providing a workaround. We handle a subset of describe commands in Spark SQL, which are defined by ...
```
DESCRIBE [EXTENDED] [db_name.]table_name
```
All other cases are treated as Hive native commands.

Also, if we upgrade Hive to 0.13, we need to check the results of context.sessionState.isHiveServerQuery() to determine how to split the result. This method is introduced by https://issues.apache.org/jira/browse/HIVE-4545. We may want to set Hive to use JsonMetaDataFormatter for the output of a DDL statement (`set hive.ddl.output.format=json` introduced by https://issues.apache.org/jira/browse/HIVE-2822).

The link to JIRA: https://issues.apache.org/jira/browse/SPARK-2177

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1118 from yhuai/SPARK-2177 and squashes the following commits:

fd2534c [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
b9b9aa5 [Yin Huai] rxin's comments.
e7c4e72 [Yin Huai] Fix unit test.
656b068 [Yin Huai] 100 characters.
6387217 [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
8003cf3 [Yin Huai] Generate strings with the format like Hive for unit tests.
9787fff [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
440c5af [Yin Huai] rxin's comments.
f1a417e [Yin Huai] Update doc.
83adb2f [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
366f891 [Yin Huai] Add describe command.
74bd1d4 [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
342fdf7 [Yin Huai] Split to up to 3 parts.
725e88c [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
bb8bbef [Yin Huai] Split every string in the result of a describe command.
SPARK-1293 [SQL] Parquet support for nested types

It should be possible to import and export data stored in Parquet's columnar format that contains nested types. For example:
```java
message AddressBook {
   required binary owner;
   optional group ownerPhoneNumbers {
      repeated binary array;
   }
   optional group contacts {
      repeated group array {
         required binary name;
         optional binary phoneNumber;
      }
   }
   optional group nameToApartmentNumber {
      repeated group map {
         required binary key;
         required int32 value;
      }
   }
}
```
The example could model a type (AddressBook) that contains records made of strings (owner), lists (ownerPhoneNumbers) and a table of contacts (e.g., a list of pairs or a map that can contain null values but keys must not be null). The list of tasks are as follows:

<h6>Implement support for converting nested Parquet types to Spark/Catalyst types:</h6>
- [x] Structs
- [x] Lists
- [x] Maps (note: currently keys need to be Strings)

<h6>Implement import (via ``parquetFile``) of nested Parquet types (first version in this PR)</h6>
- [x] Initial version

<h6>Implement export (via ``saveAsParquetFile``)</h6>
- [x] Initial version

<h6>Test support for AvroParquet, etc.</h6>
- [x] Initial testing of import of avro-generated Parquet data (simple + nested)

Example:
```scala
val data = TestSQLContext
  .parquetFile("input.dir")
  .toSchemaRDD
data.registerAsTable("data")
sql("SELECT owner, contacts[1].name, nameToApartmentNumber['John'] FROM data").collect()
```

Author: Andre Schumacher <andre.schumacher@iki.fi>
Author: Michael Armbrust <michael@databricks.com>

Closes #360 from AndreSchumacher/nested_parquet and squashes the following commits:

30708c8 [Andre Schumacher] Taking out AvroParquet test for now to remove Avro dependency
95c1367 [Andre Schumacher] Changes to ParquetRelation and its metadata
7eceb67 [Andre Schumacher] Review feedback
94eea3a [Andre Schumacher] Scalastyle
403061f [Andre Schumacher] Fixing some issues with tests and schema metadata
b8a8b9a [Andre Schumacher] More fixes to short and byte conversion
63d1b57 [Andre Schumacher] Cleaning up and Scalastyle
88e6bdb [Andre Schumacher] Attempting to fix loss of schema
37e0a0a [Andre Schumacher] Cleaning up
14c3fd8 [Andre Schumacher] Attempting to fix Spark-Parquet schema conversion
3e1456c [Michael Armbrust] WIP: Directly serialize catalyst attributes.
f7aeba3 [Michael Armbrust] [SPARK-1982] Support for ByteType and ShortType.
3104886 [Michael Armbrust] Nested Rows should be Rows, not Seqs.
3c6b25f [Andre Schumacher] Trying to reduce no-op changes wrt master
31465d6 [Andre Schumacher] Scalastyle: fixing commented out bottom
de02538 [Andre Schumacher] Cleaning up ParquetTestData
2f5a805 [Andre Schumacher] Removing stripMargin from test schemas
191bc0d [Andre Schumacher] Changing to Seq for ArrayType, refactoring SQLParser for nested field extension
cbb5793 [Andre Schumacher] Code review feedback
32229c7 [Andre Schumacher] Removing Row nested values and placing by generic types
0ae9376 [Andre Schumacher] Doc strings and simplifying ParquetConverter.scala
a6b4f05 [Andre Schumacher] Cleaning up ArrayConverter, moving classTag to NativeType, adding NativeRow
431f00f [Andre Schumacher] Fixing problems introduced during rebase
c52ff2c [Andre Schumacher] Adding native-array converter
619c397 [Andre Schumacher] Completing Map testcase
79d81d5 [Andre Schumacher] Replacing field names for array and map in WriteSupport
f466ff0 [Andre Schumacher] Added ParquetAvro tests and revised Array conversion
adc1258 [Andre Schumacher] Optimizing imports
e99cc51 [Andre Schumacher] Fixing nested WriteSupport and adding tests
1dc5ac9 [Andre Schumacher] First version of WriteSupport for nested types
d1911dc [Andre Schumacher] Simplifying ArrayType conversion
f777b4b [Andre Schumacher] Scalastyle
824500c [Andre Schumacher] Adding attribute resolution for MapType
b539fde [Andre Schumacher] First commit for MapType
a594aed [Andre Schumacher] Scalastyle
4e25fcb [Andre Schumacher] Adding resolution of complex ArrayTypes
f8f8911 [Andre Schumacher] For primitive rows fall back to more efficient converter, code reorg
6dbc9b7 [Andre Schumacher] Fixing some problems intruduced during rebase
b7fcc35 [Andre Schumacher] Documenting conversions, bugfix, wrappers of Rows
ee70125 [Andre Schumacher] fixing one problem with arrayconverter
98219cf [Andre Schumacher] added struct converter
5d80461 [Andre Schumacher] fixing one problem with nested structs and breaking up files
1b1b3d6 [Andre Schumacher] Fixing one problem with nested arrays
ddb40d2 [Andre Schumacher] Extending tests for nested Parquet data
745a42b [Andre Schumacher] Completing testcase for nested data (Addressbook(
6125c75 [Andre Schumacher] First working nested Parquet record input
4d4892a [Andre Schumacher] First commit nested Parquet read converters
aa688fe [Andre Schumacher] Adding conversion of nested Parquet schemas
[SPARK-2210] cast to boolean on boolean value gets turned into NOT((boolean_condition) = 0)

```
explain select cast(cast(key=0 as boolean) as boolean) aaa from src
```
should be
```
[Physical execution plan:]
[Project [(key#10:0 = 0) AS aaa#7]]
[ HiveTableScan [key#10], (MetastoreRelation default, src, None), None]
```

However, it is currently
```
[Physical execution plan:]
[Project [NOT((key#10=0) = 0) AS aaa#7]]
[ HiveTableScan [key#10], (MetastoreRelation default, src, None), None]
```

Author: Reynold Xin <rxin@apache.org>

Closes #1144 from rxin/booleancast and squashes the following commits:

c4e543d [Reynold Xin] [SPARK-2210] boolean cast on boolean value should be removed.
[SPARK-2209][SQL] Cast shouldn't do null check twice.

Also took the chance to clean up cast a little bit. Too many arrows on each line before!

Author: Reynold Xin <rxin@apache.org>

Closes #1143 from rxin/cast and squashes the following commits:

dd006cb [Reynold Xin] Code review feedback.
c2b88ae [Reynold Xin] [SPARK-2209][SQL] Cast shouldn't do null check twice.
SPARK-2203: PySpark defaults to use same num reduce partitions as map side

For shuffle-based operators, such as rdd.groupBy() or rdd.sortByKey(), PySpark will always assume that the default parallelism to use for the reduce side is ctx.defaultParallelism, which is a constant typically determined by the number of cores in cluster.

In contrast, Spark's Partitioner#defaultPartitioner will use the same number of reduce partitions as map partitions unless the defaultParallelism config is explicitly set. This tends to be a better default in order to avoid OOMs, and should also be the behavior of PySpark.

JIRA: https://issues.apache.org/jira/browse/SPARK-2203

Author: Aaron Davidson <aaron@databricks.com>

Closes #1138 from aarondav/pyfix and squashes the following commits:

1bd5751 [Aaron Davidson] SPARK-2203: PySpark defaults to use same num reduce partitions as map partitions
[SPARK-2196] [SQL] Fix nullability of CaseWhen.

`CaseWhen` should use `branches.length` to check if `elseValue` is provided or not.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1133 from ueshin/issues/SPARK-2196 and squashes the following commits:

510f12d [Takuya UESHIN] Add some tests.
dc25e8d [Takuya UESHIN] Fix nullable of CaseWhen to be nullable if the elseValue is nullable.
4f049cc [Takuya UESHIN] Fix nullability of CaseWhen.
[SPARK-2218] rename Equals to EqualTo in Spark SQL expressions.

Due to the existence of scala.Equals, it is very error prone to name the expression Equals, especially because we use a lot of partial functions and pattern matching in the optimizer.

Note that this sits on top of #1144.

Author: Reynold Xin <rxin@apache.org>

Closes #1146 from rxin/equals and squashes the following commits:

f8583fd [Reynold Xin] Merge branch 'master' of github.com:apache/spark into equals
326b388 [Reynold Xin] Merge branch 'master' of github.com:apache/spark into equals
bd19807 [Reynold Xin] Rename EqualsTo to EqualTo.
81148d1 [Reynold Xin] [SPARK-2218] rename Equals to EqualsTo in Spark SQL expressions.
c4e543d [Reynold Xin] [SPARK-2210] boolean cast on boolean value should be removed.
[SPARK-2163] class LBFGS optimize with Double tolerance instead of Int

https://issues.apache.org/jira/browse/SPARK-2163

This pull request includes the change for **[SPARK-2163]**:

* Changed the convergence tolerance parameter from type `Int` to type `Double`.
* Added types for vars in `class LBFGS`, making the style consistent with `class GradientDescent`.
* Added associated test to check that optimizing via `class LBFGS` produces the same results as via calling `runLBFGS` from `object LBFGS`.

This is a very minor change but it will solve the problem in my implementation of a regression model for count data, where I make use of LBFGS for parameter estimation.

Author: Gang Bai <me@baigang.net>

Closes #1104 from BaiGang/fix_int_tol and squashes the following commits:

cecf02c [Gang Bai] Changed setConvergenceTol'' to specify tolerance with a parameter of type Double. For the reason and the problem caused by an Int parameter, please check https://issues.apache.org/jira/browse/SPARK-2163. Added a test in LBFGSSuite for validating that optimizing via class LBFGS produces the same results as calling runLBFGS from object LBFGS. Keep the indentations and styles correct.
SPARK-1868: Users should be allowed to cogroup at least 4 RDDs

Adds cogroup for 4 RDDs.

Author: Allan Douglas R. de Oliveira <allandouglas@gmail.com>

Closes #813 from douglaz/more_cogroups and squashes the following commits:

f8d6273 [Allan Douglas R. de Oliveira] Test python groupWith for one more case
0e9009c [Allan Douglas R. de Oliveira] Added scala tests
c3ffcdd [Allan Douglas R. de Oliveira] Added java tests
517a67f [Allan Douglas R. de Oliveira] Added tests for python groupWith
2f402d5 [Allan Douglas R. de Oliveira] Removed TODO
17474f4 [Allan Douglas R. de Oliveira] Use new cogroup function
7877a2a [Allan Douglas R. de Oliveira] Fixed code
ba02414 [Allan Douglas R. de Oliveira] Added varargs cogroup to pyspark
c4a8a51 [Allan Douglas R. de Oliveira] Added java cogroup 4
e94963c [Allan Douglas R. de Oliveira] Fixed spacing
f1ee57b [Allan Douglas R. de Oliveira] Fixed scala style issues
d7196f1 [Allan Douglas R. de Oliveira] Allow the cogroup of 4 RDDs
SPARK-2180:  support HAVING clauses in Hive queries

This PR extends Spark's HiveQL support to handle HAVING clauses in aggregations.  The HAVING test from the Hive compatibility suite doesn't appear to be runnable from within Spark, so I added a simple comparable test to `HiveQuerySuite`.

Author: William Benton <willb@redhat.com>

Closes #1136 from willb/SPARK-2180 and squashes the following commits:

3bbaf26 [William Benton] Added casts to HAVING expressions
83f1340 [William Benton] scalastyle fixes
18387f1 [William Benton] Add test for HAVING without GROUP BY
b880bef [William Benton] Added semantic error for HAVING without GROUP BY
942428e [William Benton] Added test coverage for SPARK-2180.
56084cc [William Benton] Add support for HAVING clauses in Hive queries.
[SPARK-2225] Turn HAVING without GROUP BY into WHERE.

@willb

Author: Reynold Xin <rxin@apache.org>

Closes #1161 from rxin/having-filter and squashes the following commits:

fa8359a [Reynold Xin] [SPARK-2225] Turn HAVING without GROUP BY into WHERE.
Clean up CacheManager et al.

**UPDATE**

I have removed the special handling for `StorageLevel.MEMORY_*_SER` for now, because it introduces a potential performance regression. With the latest changes, this PR should include mainly style (code readability) fixes. The only functionality change is the update in `MemoryStore#putBytes` to actually return updated blocks, though this is a minor bug fix.

Now this is mainly a precursor to another PR (once again).

---------
*Old comment*

The deserialized version of a partition may occupy much more space than the serialized version. Therefore, if a partition is to be cached with `StorageLevel.MEMORY_*_SER`, we don't need to fully unroll it into an `ArrayBuffer`, but instead we can unroll it into a potentially much smaller `ByteBuffer`. This may save us from OOMs in this case.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1083 from andrewor14/unroll-them-partitions and squashes the following commits:

7048aa0 [Andrew Or] Merge branch 'master' of github.com:apache/spark into unroll-them-partitions
3d9a366 [Andrew Or] Minor change for readability
d12b95f [Andrew Or] Remove unused imports (minor)
a4c387b [Andrew Or] Merge branch 'master' of github.com:apache/spark into unroll-them-partitions
cf5f565 [Andrew Or] Remove special handling for MEM_*_SER
0091ec0 [Andrew Or] Address review feedback
44ef282 [Andrew Or] Actually return updated blocks in putBytes
2941c89 [Andrew Or] Clean up BlockStore (minor)
a8f181d [Andrew Or] Add special handling for StorageLevel.MEMORY_*_SER
Move ScriptTransformation into the appropriate place.

Author: Reynold Xin <rxin@apache.org>

Closes #1162 from rxin/script and squashes the following commits:

2c836b9 [Reynold Xin] Move ScriptTransformation into the appropriate place.