## What changes were proposed in this pull request?

Currently, `OptimizeIn` optimizer replaces `In` expression into `InSet` expression if the size of set is greater than a constant, 10.
This issue aims to make a configuration `spark.sql.optimizer.inSetConversionThreshold` for that.

After this PR, `OptimizerIn` is configurable.
```scala
scala> sql("select a in (1,2,3) from (select explode(array(1,2)) a) T").explain()
== Physical Plan ==
WholeStageCodegen
:  +- Project [a#7 IN (1,2,3) AS (a IN (1, 2, 3))#8]
:     +- INPUT
+- Generate explode([1,2]), false, false, [a#7]
   +- Scan OneRowRelation[]

scala> sqlContext.setConf("spark.sql.optimizer.inSetConversionThreshold", "2")

scala> sql("select a in (1,2,3) from (select explode(array(1,2)) a) T").explain()
== Physical Plan ==
WholeStageCodegen
:  +- Project [a#16 INSET (1,2,3) AS (a IN (1, 2, 3))#17]
:     +- INPUT
+- Generate explode([1,2]), false, false, [a#16]
   +- Scan OneRowRelation[]
```

## How was this patch tested?

Pass the Jenkins tests (with a new testcase)

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #12562 from dongjoon-hyun/SPARK-14796.

## What changes were proposed in this pull request?

1. Fix the "spill size" of TungstenAggregate and Sort
2. Rename "data size" to "peak memory" to match the actual meaning (also consistent with task metrics)
3. Added "data size" for ShuffleExchange and BroadcastExchange
4. Added some timing for Sort, Aggregate and BroadcastExchange (this requires another patch to work)

## How was this patch tested?

Existing tests.
![metrics](https://cloud.githubusercontent.com/assets/40902/14573908/21ad2f00-030d-11e6-9e2c-c544f30039ea.png)

Author: Davies Liu <davies@databricks.com>

Closes #12425 from davies/fix_metrics.

## What changes were proposed in this pull request?

SparkPlan.prepare() could be called in different threads (BroadcastExchange will call it in a thread pool), it only make sure that doPrepare() will only be called once, the second call to prepare() may return earlier before all the children had finished prepare(). Then some operator may call doProduce() before prepareSubqueries(), `null` will be used as the result of subquery, which is wrong. This cause TPCDS Q23B returns wrong answer sometimes.

This PR added synchronization for prepare(), make sure all the children had finished prepare() before return. Also call prepare() in produce() (similar to execute()).

Added checking for ScalarSubquery to make sure that the subquery has finished before using the result.

## How was this patch tested?

Manually tested with Q23B, no wrong answer anymore.

Author: Davies Liu <davies@databricks.com>

Closes #12600 from davies/fix_risk.

## What changes were proposed in this pull request?

Currently, a column could be resolved wrongly if there are columns from both outer table and subquery have the same name, we should only resolve the attributes that can't be resolved within subquery. They may have same exprId than other attributes in subquery, so we should create alias for them.

Also, the column in IN subquery could have same exprId, we should create alias for them.

## How was this patch tested?

Added regression tests. Manually tests TPCDS Q70 and Q95, work well after this patch.

Author: Davies Liu <davies@databricks.com>

Closes #12539 from davies/fix_subquery.

### What changes were proposed in this pull request?
TPCDS Q90 fails to parse because it uses a reserved keyword as an Identifier; `AT` was used as an alias for one of the subqueries. `AT` is not a reserved keyword and should have been registerd as a in the `nonReserved` rule.

In order to prevent this from happening again I have added tests for all keywords that are non-reserved in Hive. See the `nonReserved`, `sql11ReservedKeywordsUsedAsCastFunctionName` & `sql11ReservedKeywordsUsedAsIdentifier` rules in https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/parse/IdentifiersParser.g.

### How was this patch tested?

Added tests to for all Hive non reserved keywords to `TableIdentifierParserSuite`.

cc davies

Author: Herman van Hovell <hvanhovell@questtec.nl>

Closes #12537 from hvanhovell/SPARK-14762.

## What changes were proposed in this pull request?
The concurrency issue reported in SPARK-13178 was fixed by the PR https://github.com/apache/spark/pull/10947 for SPARK-12792.
This PR just removes a workaround not needed anymore.

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

Author: Sun Rui <rui.sun@intel.com>

Closes #12606 from sun-rui/SPARK-13178.

## What changes were proposed in this pull request?
This patch moves SQLBuilder into sql/core so we can in the future move view generation also into sql/core.

## How was this patch tested?
Also moved unit tests.

Author: Reynold Xin <rxin@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>

Closes #12602 from rxin/SPARK-14841.

## What changes were proposed in this pull request?

We use `RowEncoder` in libsvm data source to serialize the label and features read from libsvm files. However, the schema passed in this encoder is not correct. As the result, we can't correctly select `features` column from the DataFrame. We should use full data schema instead of `requiredSchema` to serialize the data read in. Then do projection to select required columns later.

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

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

Closes #12611 from viirya/fix-libsvm.

## What changes were proposed in this pull request?
This is a follow-up to #12557, with the following changes:

1. Fixes some of the style issues.
2. Merges Signaling and SignalLogger into a new class called SignalUtils. It was pretty confusing to have Signaling and Signal in one file, and it was also confusing to have two classes named Signaling and one called the other.
3. Made logging registration idempotent.

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

Author: Reynold Xin <rxin@databricks.com>

Closes #12605 from rxin/SPARK-10001.

## 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?
Change test to compare sets rather than sequence

## How was this patch tested?
Full test runs on little endian and big endian platforms

Author: Pete Robbins <robbinspg@gmail.com>

Closes #12610 from robbinspg/DatasetSuiteFix.

## What changes were proposed in this pull request?
1, fix the indentation
2, add a missing param desc

## How was this patch tested?
unit tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12499 from zhengruifeng/fix_doc.

## What changes were proposed in this pull request?

Implement some `hashCode` and `equals` together in order to enable the scalastyle.
This is a first batch, I will continue to implement them but I wanted to know your thoughts.

Author: Joan <joan@goyeau.com>

Closes #12157 from joan38/SPARK-6429-HashCode-Equals.

## What changes were proposed in this pull request?

Add the native support for LOAD DATA DDL command that loads data into Hive table/partition.

## How was this patch tested?

`HiveDDLCommandSuite` and `HiveQuerySuite`. Besides, few Hive tests (`WindowQuerySuite`, `HiveTableScanSuite` and `HiveSerDeSuite`) also use `LOAD DATA` command.

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

Closes #12412 from viirya/ddl-load-data.

## What changes were proposed in this pull request?
This patch removes HiveQueryExecution. As part of this, I consolidated all the describe commands into DescribeTableCommand.

## How was this patch tested?
Should be covered by existing tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #12588 from rxin/SPARK-14826.

## What changes were proposed in this pull request?

Improve signal handling to allow interrupting running tasks from the REPL (with Ctrl+C).
If no tasks are running or Ctrl+C is pressed twice, the signal is forwarded to the default handler resulting in the usual termination of the application.

This PR is a rewrite of -- and therefore closes #8216 -- as per piaozhexiu's request

## How was this patch tested?
Signal handling is not easily testable therefore no unit tests were added. Nevertheless, the new functionality is implemented in a best-effort approach, soft-failing in case signals aren't available on a specific OS.

Author: Jakob Odersky <jakob@odersky.com>

Closes #12557 from jodersky/SPARK-10001-sigint.

## What changes were proposed in this pull request?
This patch removes SQLBuilder's dependency on MetastoreRelation. We should be able to move SQLBuilder into the sql/core package after this change.

## How was this patch tested?
N/A - covered by existing tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #12594 from rxin/SPARK-14835.

(This PR is a rebased version of PR #12153.)

## What changes were proposed in this pull request?

This PR adds preliminary locality support for `FileFormat` data sources by overriding `FileScanRDD.preferredLocations()`. The strategy can be divided into two parts:

1.  Block location lookup

    Unlike `HadoopRDD` or `NewHadoopRDD`, `FileScanRDD` doesn't have access to the underlying `InputFormat` or `InputSplit`, and thus can't rely on `InputSplit.getLocations()` to gather locality information. Instead, this PR queries block locations using `FileSystem.getBlockLocations()` after listing all `FileStatus`es in `HDFSFileCatalog` and convert all `FileStatus`es into `LocatedFileStatus`es.

    Note that although S3/S3A/S3N file systems don't provide valid locality information, their `getLocatedStatus()` implementations don't actually issue remote calls either. So there's no need to special case these file systems.

2.  Selecting preferred locations

    For each `FilePartition`, we pick up top 3 locations that containing the most data to be retrieved. This isn't necessarily the best algorithm out there. Further improvements may be brought up in follow-up PRs.

## How was this patch tested?

Tested by overriding default `FileSystem` implementation for `file:///` with a mocked one, which returns mocked block locations.

Author: Cheng Lian <lian@databricks.com>

Closes #12527 from liancheng/spark-14369-locality-rebased.

## What changes were proposed in this pull request?

This PR adds support for all primitive datatypes, decimal types and stringtypes in the VectorizedHashmap during aggregation.

## How was this patch tested?

Existing tests for group-by aggregates should already test for all these datatypes. Additionally, manually inspected the generated code for all supported datatypes (details below).

Author: Sameer Agarwal <sameer@databricks.com>

Closes #12440 from sameeragarwal/all-datatypes.

## What changes were proposed in this pull request?

Code generation for complex type, `CreateArray`, `CreateMap`, `CreateStruct`, `CreateNamedStruct`, exceeds JVM size limit for large elements.

We should split generated code into multiple `apply` functions if the complex types have large elements,  like `UnsafeProjection` or others for large expressions.

## How was this patch tested?

I added some tests to check if the generated codes for the expressions exceed or not.

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

Closes #12559 from ueshin/issues/SPARK-14793.

## What changes were proposed in this pull request?

Just a rename so we can get rid of `HiveContext.scala`. Note that this will conflict with #12585.

## How was this patch tested?

No change in functionality.

Author: Andrew Or <andrew@databricks.com>

Closes #12586 from andrewor14/rename-hc-object.

## What changes were proposed in this pull request?
This patch moves analyze table parsing into SparkSqlAstBuilder and removes HiveSqlAstBuilder.

In order to avoid extensive refactoring, I created a common trait for CatalogRelation and MetastoreRelation, and match on that. In the future we should probably just consolidate the two into a single thing so we don't need this common trait.

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

Author: Reynold Xin <rxin@databricks.com>

Closes #12584 from rxin/SPARK-14821.

## What changes were proposed in this pull request?
GLM supports output link prediction.
## How was this patch tested?
unit test.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #12287 from yanboliang/spark-14479.

## What changes were proposed in this pull request?

For maintaining wrappers around spark.mllib algorithms in spark.ml, it will be useful to have ```private[spark]``` methods for converting from one linear algebra representation to another.
This PR adds toNew, fromNew methods for all spark.mllib Vector and Matrix types.

## How was this patch tested?

Unit tests for all conversions

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #12504 from jkbradley/linalg-conversions.

## What changes were proposed in this pull request?

Spark currently uses TimSort for all in-memory sorts, including sorts done for shuffle. One low-hanging fruit is to use radix sort when possible (e.g. sorting by integer keys). This PR adds a radix sort implementation to the unsafe sort package and switches shuffles and sorts to use it when possible.

The current implementation does not have special support for null values, so we cannot radix-sort `LongType`. I will address this in a follow-up PR.

## How was this patch tested?

Unit tests, enabling radix sort on existing tests. Microbenchmark results:

```
Running benchmark: radix sort 25000000
Java HotSpot(TM) 64-Bit Server VM 1.8.0_66-b17 on Linux 3.13.0-44-generic
Intel(R) Core(TM) i7-4600U CPU  2.10GHz

radix sort 25000000:                Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------
reference TimSort key prefix array     15546 / 15859          1.6         621.9       1.0X
reference Arrays.sort                    2416 / 2446         10.3          96.6       6.4X
radix sort one byte                       133 /  137        188.4           5.3     117.2X
radix sort two bytes                      255 /  258         98.2          10.2      61.1X
radix sort eight bytes                    991 /  997         25.2          39.6      15.7X
radix sort key prefix array              1540 / 1563         16.2          61.6      10.1X
```

I also ran a mix of the supported TPCDS queries and compared TimSort vs RadixSort metrics. The overall benchmark ran ~10% faster with radix sort on. In the breakdown below, the radix-enabled sort phases averaged about 20x faster than TimSort, however sorting is only a small fraction of the overall runtime. About half of the TPCDS queries were able to take advantage of radix sort.

```
TPCDS on master: 2499s real time, 8185s executor
    - 1171s in TimSort, avg 267 MB/s
(note the /s accounting is weird here since dataSize counts the record sizes too)

TPCDS with radix enabled: 2294s real time, 7391s executor
    - 596s in TimSort, avg 254 MB/s
    - 26s in radix sort, avg 4.2 GB/s
```

cc davies rxin

Author: Eric Liang <ekl@databricks.com>

Closes #12490 from ericl/sort-benchmark.

## What changes were proposed in this pull request?

https://issues.apache.org/jira/browse/SPARK-14569

Log instrumentation in KMeans:

- featuresCol
- predictionCol
- k
- initMode
- initSteps
- maxIter
- seed
- tol
- summary

## How was this patch tested?

Manually test on local machine, by running and checking output of org.apache.spark.examples.ml.KMeansExample

Author: Xin Ren <iamshrek@126.com>

Closes #12432 from keypointt/SPARK-14569.

## What changes were proposed in this pull request?

This PR aims to add `setLogLevel` function to SparkR shell.

**Spark Shell**
```scala
scala> sc.setLogLevel("ERROR")
```

**PySpark**
```python
>>> sc.setLogLevel("ERROR")
```

**SparkR (this PR)**
```r
> setLogLevel(sc, "ERROR")
NULL
```

## How was this patch tested?

Pass the Jenkins tests including a new R testcase.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #12547 from dongjoon-hyun/SPARK-14780.

## What changes were proposed in this pull request?

We recently made `ColumnarBatch.row` mutable and added a new `ColumnVector.putDecimal` method to support putting `Decimal` values in the `ColumnarBatch`. This unfortunately introduced a bug wherein we were not updating the vector with the proper unscaled values.

## How was this patch tested?

This codepath is hit only when the vectorized aggregate hashmap is enabled. https://github.com/apache/spark/pull/12440 makes sure that a number of regression tests/benchmarks test this bugfix.

Author: Sameer Agarwal <sameer@databricks.com>

Closes #12541 from sameeragarwal/fix-bigdecimal.

## What changes were proposed in this pull request?
This patch moves native command and script transformation into SparkSqlAstBuilder. This builds on #12561. See the last commit for diff.

## How was this patch tested?
Updated test cases to reflect this.

Author: Reynold Xin <rxin@databricks.com>

Closes #12564 from rxin/SPARK-14798.

## What changes were proposed in this pull request?

After removing most of `HiveContext` in 8fc267ab we can now move existing functionality in `SQLContext` to `SparkSession`. As of this PR `SQLContext` becomes a simple wrapper that has a `SparkSession` and delegates all functionality to it.

## How was this patch tested?

Jenkins.

Author: Andrew Or <andrew@databricks.com>

Closes #12553 from andrewor14/implement-spark-session.

## What changes were proposed in this pull request?
This class is currently in HiveMetastoreCatalog.scala, which is a large file that makes refactoring and searching of usage difficult. Moving it out so I can then do SPARK-14799 and make the review of that simpler.

## How was this patch tested?
N/A - this is a straightforward move and should be covered by existing tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #12567 from rxin/SPARK-14801.

## What changes were proposed in this pull request?

In general, `onDisconnected` is for dealing with unexpected network disconnections. When RpcEnv.shutdown is called, the disconnections are expected so RpcEnv should not fire these events.

This PR moves `dispatcher.stop()` above closing the connections so that when stopping RpcEnv, the endpoints won't receive `onDisconnected` events.

In addition, Outbox should not close the client since it will be reused by others. This PR fixes it as well.

## How was this patch tested?

test("SPARK-14699: RpcEnv.shutdown should not fire onDisconnected events")

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #12481 from zsxwing/SPARK-14699.

## What changes were proposed in this pull request?
This patch builds on #12556 and completely removes the use of Hive's variable substitution.

## How was this patch tested?
Covered by existing tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #12561 from rxin/SPARK-14795.

## What changes were proposed in this pull request?
This patch isolates AnalyzeTable's dependency on MetastoreRelation into a single line. After this we can work on converging MetastoreRelation and CatalogTable.

## How was this patch tested?
Covered by existing tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #12566 from rxin/SPARK-14799.

In IsolatedClientLoader, we have a`catch` block which throws an exception without wrapping the original exception, causing the full exception stacktrace and any nested exceptions to be lost. This patch fixes this, improving the usefulness of classloading error messages.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #12548 from JoshRosen/improve-logging-for-hive-classloader-issues.

## What changes were proposed in this pull request?
In #9241 It implemented a mechanism to call spill() on those SQL operators that support spilling if there is not enough memory for execution.
But ExternalSorter and AppendOnlyMap in Spark core are not worked. So this PR make them benefit from #9241. Now when there is not enough memory for execution, it can get memory by spilling ExternalSorter and AppendOnlyMap in Spark core.

## How was this patch tested?
add two unit tests for it.

Author: Lianhui Wang <lianhuiwang09@gmail.com>

Closes #10024 from lianhuiwang/SPARK-4452-2.

Spark SQL's POM hardcodes a dependency on `spark-sketch_2.11`, which causes Scala 2.10 builds to include the `_2.11` dependency. This is harmless since `spark-sketch` is a pure-Java module (see #12334 for a discussion of dropping the Scala version suffixes from these modules' artifactIds), but it's confusing to people looking at the published POMs.

This patch fixes this by using `${scala.binary.version}` to substitute the correct suffix, and also adds a set of Maven Enforcer rules to ensure that `_2.11` artifacts are not used in 2.10 builds (and vice-versa).

/cc ahirreddy, who spotted this issue.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #12563 from JoshRosen/fix-sketch-scala-version.

[SPARK-13988][CORE] Make replaying event logs multi threaded in Histo…ry server to ensure a single large log does not block other logs from being rendered.

## What changes were proposed in this pull request?
The patch makes event log processing multi threaded.

## How was this patch tested?
Existing tests pass, there is no new tests needed to test the functionality as this is a perf improvement. I tested the patch locally by generating one big event log (big1), one small event log(small1) and again a big event log(big2). Without this patch UI does not render any app for almost 30 seconds and then big2 and small1 appears. another 30 second delay and finally big1 also shows up in UI. With this change small1 shows up immediately and big1 and big2 comes up in 30 seconds. Locally it also displays them in the correct order in the UI.

Author: Parth Brahmbhatt <pbrahmbhatt@netflix.com>

Closes #11800 from Parth-Brahmbhatt/SPARK-13988.