## What changes were proposed in this pull request?

Try excluding org.json:json from hive-exec dep as it's Cat X now. It may be the case that it's not used by the part of Hive Spark uses anyway.

## How was this patch tested?

Existing tests

Author: Sean Owen <sowen@cloudera.com>

Closes #15798 from srowen/SPARK-18262.

## What changes were proposed in this pull request?

This is a follow-up work of #15618.

Close file source;
For any newly created streaming context outside the withContext, explicitly close the context.

## How was this patch tested?

Existing unit tests.

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes #15818 from wangmiao1981/rtest.

## What changes were proposed in this pull request?
ALS.run fail with better message if ratings is empty rdd
ALS.train and ALS.trainImplicit are also affected

## How was this patch tested?
added new tests

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #15809 from techaddict/SPARK-18268.

## What changes were proposed in this pull request?

Currently the error message is correct but doesn't provide additional hint to new users. It would be better to hint related configuration to users in the message.

## How was this patch tested?

N/A because it only changes error message.

Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #15822 from viirya/minor-pyspark-worker-errmsg.

## What changes were proposed in this pull request?

~In `TypedAggregateExpression.evaluateExpression`, we may create `ReferenceToExpressions` with `CreateStruct`, and `CreateStruct` may generate too many codes and split them into several methods.  `ReferenceToExpressions` will replace `BoundReference` in `CreateStruct` with `LambdaVariable`, which can only be used as local variables and doesn't work if we split the generated code.~

It's already fixed by #15693 , this pr adds regression test

## How was this patch tested?

new test in `DatasetAggregatorSuite`

Author: Wenchen Fan <wenchen@databricks.com>

Closes #15807 from cloud-fan/typed-agg.

## What changes were proposed in this pull request?

Currently we use java serialization for the WAL that stores the offsets contained in each batch. This has two main issues:
It can break across spark releases (though this is not the only thing preventing us from upgrading a running query)
It is unnecessarily opaque to the user.
I'd propose we require offsets to provide a user readable serialization and use that instead. JSON is probably a good option.
## How was this patch tested?

Tests were added for KafkaSourceOffset in [KafkaSourceOffsetSuite](external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSourceOffsetSuite.scala) and for LongOffset in [OffsetSuite](sql/core/src/test/scala/org/apache/spark/sql/streaming/OffsetSuite.scala)

Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.

zsxwing marmbrus

Author: Tyson Condie <tcondie@gmail.com>
Author: Tyson Condie <tcondie@clash.local>

Closes #15626 from tcondie/spark-8360.

## What changes were proposed in this pull request?

We should call `setConf` if `OutputFormat` is `Configurable`, this should be done before we create `OutputCommitter` and `RecordWriter`.
This is follow up of #15769, see discussion [here](https://github.com/apache/spark/pull/15769/files#r87064229)

## How was this patch tested?

Add test of this case in `PairRDDFunctionsSuite`.

Author: jiangxingbo <jiangxb1987@gmail.com>

Closes #15823 from jiangxb1987/config-format.

## What changes were proposed in this pull request?
`InsertIntoHadoopFsRelationCommand` does not keep track if it inserts into a table and what table it inserts to. This can make debugging these statements problematic. This PR adds table information the `InsertIntoHadoopFsRelationCommand`. Explaining this SQL command `insert into prq select * from range(0, 100000)` now yields the following executed plan:
```
== Physical Plan ==
ExecutedCommand
   +- InsertIntoHadoopFsRelationCommand file:/dev/assembly/spark-warehouse/prq, ParquetFormat, <function1>, Map(serialization.format -> 1, path -> file:/dev/assembly/spark-warehouse/prq), Append, CatalogTable(
	Table: `default`.`prq`
	Owner: hvanhovell
	Created: Wed Nov 09 17:42:30 CET 2016
	Last Access: Thu Jan 01 01:00:00 CET 1970
	Type: MANAGED
	Schema: [StructField(id,LongType,true)]
	Provider: parquet
	Properties: [transient_lastDdlTime=1478709750]
	Storage(Location: file:/dev/assembly/spark-warehouse/prq, InputFormat: org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat, Serde: org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe, Properties: [serialization.format=1]))
         +- Project [id#7L]
            +- Range (0, 100000, step=1, splits=None)
```

## How was this patch tested?
Added extra checks to the `ParquetMetastoreSuite`

Author: Herman van Hovell <hvanhovell@databricks.com>

Closes #15832 from hvanhovell/SPARK-18370.

## What changes were proposed in this pull request?

This makes the result value both transient and lazy, so that if the RegExpReplace object is initialized then serialized, `result: StringBuffer` will be correctly initialized.

## How was this patch tested?

* Verified that this patch fixed the query that found the bug.
* Added a test case that fails without the fix.

Author: Ryan Blue <blue@apache.org>

Closes #15834 from rdblue/SPARK-18368-fix-regexp-replace.

This reverts commit b9192bb3.

## What changes were proposed in this pull request?

Application links generated on the history server UI no longer (regression from 1.6) contain the configured spark.ui.proxyBase in the links. To address this, made the uiRoot available globally to all javascripts for Web UI. Updated the mustache template (historypage-template.html) to include the uiroot for rendering links to the applications.

The existing test was not sufficient to verify the scenario where ajax call is used to populate the application listing template, so added a new selenium test case to cover this scenario.

## How was this patch tested?

Existing tests and a new unit test.
No visual changes to the UI.

Author: Vinayak <vijoshi5@in.ibm.com>

Closes #15742 from vijoshi/SPARK-16808_master.

## What changes were proposed in this pull request?

Test case initialization order under Maven and SBT are different. Maven always creates instances of all test cases and then run them all together.

This fails `ObjectHashAggregateSuite` because the randomized test cases there register a temporary Hive function right before creating a test case, and can be cleared while initializing other successive test cases. In SBT, this is fine since the created test case is executed immediately after creating the temporary function.

To fix this issue, we should put initialization/destruction code into `beforeAll()` and `afterAll()`.

## How was this patch tested?

Existing tests.

Author: Cheng Lian <lian@databricks.com>

Closes #15802 from liancheng/fix-flaky-object-hash-agg-suite.

[SPARK-18292][SQL] LogicalPlanToSQLSuite should not use resource dependent path for golden file generation

## What changes were proposed in this pull request?

`LogicalPlanToSQLSuite` uses the following command to update the existing answer files.

```bash
SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "hive/test-only *LogicalPlanToSQLSuite"
```

However, after introducing `getTestResourcePath`, it fails to update the previous golden answer files in the predefined directory. This issue aims to fix that.

## How was this patch tested?

It's a testsuite update. Manual.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #15789 from dongjoon-hyun/SPARK-18292.

### What changes were proposed in this pull request?

`Partitioned View` is not supported by SPARK SQL. For Hive partitioned view, SHOW CREATE TABLE is unable to generate the right DDL. Thus, SHOW CREATE TABLE should not support it like the other Hive-only features. This PR is to issue an exception when detecting the view is a partitioned view.
### How was this patch tested?

Added a test case

Author: gatorsmile <gatorsmile@gmail.com>

Closes #15233 from gatorsmile/partitionedView.

## What changes were proposed in this pull request?

This makes the result value both transient and lazy, so that if the RegExpReplace object is initialized then serialized, `result: StringBuffer` will be correctly initialized.

## How was this patch tested?

* Verified that this patch fixed the query that found the bug.
* Added a test case that fails without the fix.

Author: Ryan Blue <blue@apache.org>

Closes #15816 from rdblue/SPARK-18368-fix-regexp-replace.

## What changes were proposed in this pull request?

These are no longer needed after https://issues.apache.org/jira/browse/SPARK-17183

cc cloud-fan

## How was this patch tested?

Existing parquet and orc tests.

Author: Eric Liang <ekl@databricks.com>

Closes #15799 from ericl/sc-4929.

## What changes were proposed in this pull request?

Gradient Boosted Tree in R.
With a few minor improvements to RandomForest in R.

Since this is relatively isolated I'd like to target this for branch-2.1

## How was this patch tested?

manual tests, unit tests

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #15746 from felixcheung/rgbt.

## What changes were proposed in this pull request?

If the rename operation in the state store fails (`fs.rename` returns `false`), the StateStore should throw an exception and have the task retry. Currently if renames fail, nothing happens during execution immediately. However, you will observe that snapshot operations will fail, and then any attempt at recovery (executor failure / checkpoint recovery) also fails.

## How was this patch tested?

Unit test

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #15804 from brkyvz/rename-state.

## What changes were proposed in this pull request?

"StandaloneSchedulerBackend.dead" is called in a RPC thread, so it should not call "SparkContext.stop" in the same thread. "SparkContext.stop" will block until all RPC threads exit, if it's called inside a RPC thread, it will be dead-lock.

This PR add a thread local flag inside RPC threads. `SparkContext.stop` uses it to decide if launching a new thread to stop the SparkContext.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #15775 from zsxwing/SPARK-18280.

## What changes were proposed in this pull request?

* Made SingularMatrixException private ml
* WeightedLeastSquares: Changed to allow tol >= 0 instead of only tol > 0

## How was this patch tested?

existing tests

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

Closes #15779 from jkbradley/wls-cleanups.

## What changes were proposed in this pull request?

The #15627 broke functionality with yarn --files --archives does not accept any files.
This patch ensures that --files and --archives accept unique files.

## How was this patch tested?

A. I added unit tests.
B. Also, manually tested --files with --archives to throw exception if duplicate files are specified and continue if unique files are specified.

Author: Kishor Patil <kpatil@yahoo-inc.com>

Closes #15810 from kishorvpatil/SPARK18357.

## What changes were proposed in this pull request?

This PR port RDD API to use commit protocol, the changes made here:
1. Add new internal helper class that saves an RDD using a Hadoop OutputFormat named `SparkNewHadoopWriter`, it's similar with `SparkHadoopWriter` but uses commit protocol. This class supports the newer `mapreduce` API, instead of the old `mapred` API which is supported by `SparkHadoopWriter`;
2. Rewrite `PairRDDFunctions.saveAsNewAPIHadoopDataset` function, so it uses commit protocol now.

## How was this patch tested?
Exsiting test cases.

Author: jiangxingbo <jiangxb1987@gmail.com>

Closes #15769 from jiangxb1987/rdd-commit.

[SPARK-18346][SQL] TRUNCATE TABLE should fail if no partition is matched for the given non-partial partition spec

## What changes were proposed in this pull request?

a follow up of https://github.com/apache/spark/pull/15688

## How was this patch tested?

updated test in `DDLSuite`

Author: Wenchen Fan <wenchen@databricks.com>

Closes #15805 from cloud-fan/truncate.

## What changes were proposed in this pull request?

We generate bitmasks for grouping sets during the parsing process, and use these during analysis. These bitmasks are difficult to work with in practice and have lead to numerous bugs. This PR removes these and use actual sets instead, however we still need to generate these offsets for the grouping_id.

This PR does the following works:
1. Replace bitmasks by actual grouping sets durning Parsing/Analysis stage of CUBE/ROLLUP/GROUPING SETS;
2. Add new testsuite `ResolveGroupingAnalyticsSuite` to test the `Analyzer.ResolveGroupingAnalytics` rule directly;
3. Fix a minor bug in `ResolveGroupingAnalytics`.
## How was this patch tested?

By existing test cases, and add new testsuite `ResolveGroupingAnalyticsSuite` to test directly.

Author: jiangxingbo <jiangxb1987@gmail.com>

Closes #15484 from jiangxb1987/group-set.

## What changes were proposed in this pull request?
1, `**Example**` => `**Examples**`, because more algos use `**Examples**`.
2,  delete `### Examples` in `Isotonic regression`, because it's not that special in http://spark.apache.org/docs/latest/ml-classification-regression.html
3, add missing marks for `LDA` and other algos.

## How was this patch tested?
No tests for it only modify doc

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #15783 from zhengruifeng/doc_fix.

I created Scala and Java example and added documentation.

Author: chie8842 <hayashidac@nttdata.co.jp>

Closes #15658 from hayashidac/SPARK-13770.

[SPARK-18137][SQL] Fix RewriteDistinctAggregates UnresolvedException when a UDAF has a foldable TypeCheck

## What changes were proposed in this pull request?

In RewriteDistinctAggregates rewrite funtion,after the UDAF's childs are mapped to AttributeRefference, If the UDAF(such as ApproximatePercentile) has a foldable TypeCheck for the input, It will failed because the AttributeRefference is not foldable,then the UDAF is not resolved, and then nullify on the unresolved object will throw a Exception.

In this PR, only map Unfoldable child to AttributeRefference, this can avoid the UDAF's foldable TypeCheck. and then only Expand Unfoldable child, there is no need to Expand a static value(foldable value).

**Before sql result**

> select percentile_approxy(key,0.99999),count(distinct key),sume(distinc key) from src limit 1
> org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid call to dataType on unresolved object, tree: 'percentile_approx(CAST(src.`key` AS DOUBLE), CAST(0.99999BD AS DOUBLE), 10000)
> at org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute.dataType(unresolved.scala:92)
>     at org.apache.spark.sql.catalyst.optimizer.RewriteDistinctAggregates$.org$apache$spark$sql$catalyst$optimizer$RewriteDistinctAggregates$$nullify(RewriteDistinctAggregates.scala:261)

**After sql result**

> select percentile_approxy(key,0.99999),count(distinct key),sume(distinc key) from src limit 1
> [498.0,309,79136]
## How was this patch tested?

Add a test case in HiveUDFSuit.

Author: root <root@iZbp1gsnrlfzjxh82cz80vZ.(none)>

Closes #15668 from windpiger/RewriteDistinctUDAFUnresolveExcep.

## What changes were proposed in this pull request?

This PR avoids a compilation error due to more than 64KB Java byte code size. This error occur since  generate java code for computing a hash value for a row is too big. This PR fixes this compilation error by splitting a big code chunk into multiple methods by calling `CodegenContext.splitExpression` at `HashExpression.doGenCode`

The test case requires a calculation of hash code for a row that includes 1000 String fields. `HashExpression.doGenCode` generate a lot of Java code for this computation into one function. As a result, the size of the corresponding Java bytecode is more than 64 KB.

Generated code without this PR
````java
/* 027 */   public UnsafeRow apply(InternalRow i) {
/* 028 */     boolean isNull = false;
/* 029 */
/* 030 */     int value1 = 42;
/* 031 */
/* 032 */     boolean isNull2 = i.isNullAt(0);
/* 033 */     UTF8String value2 = isNull2 ? null : (i.getUTF8String(0));
/* 034 */     if (!isNull2) {
/* 035 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value2.getBaseObject(), value2.getBaseOffset(), value2.numBytes(), value1);
/* 036 */     }
/* 037 */
/* 038 */
/* 039 */     boolean isNull3 = i.isNullAt(1);
/* 040 */     UTF8String value3 = isNull3 ? null : (i.getUTF8String(1));
/* 041 */     if (!isNull3) {
/* 042 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value3.getBaseObject(), value3.getBaseOffset(), value3.numBytes(), value1);
/* 043 */     }
/* 044 */
/* 045 */
...
/* 7024 */
/* 7025 */     boolean isNull1001 = i.isNullAt(999);
/* 7026 */     UTF8String value1001 = isNull1001 ? null : (i.getUTF8String(999));
/* 7027 */     if (!isNull1001) {
/* 7028 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1001.getBaseObject(), value1001.getBaseOffset(), value1001.numBytes(), value1);
/* 7029 */     }
/* 7030 */
/* 7031 */
/* 7032 */     boolean isNull1002 = i.isNullAt(1000);
/* 7033 */     UTF8String value1002 = isNull1002 ? null : (i.getUTF8String(1000));
/* 7034 */     if (!isNull1002) {
/* 7035 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1002.getBaseObject(), value1002.getBaseOffset(), value1002.numBytes(), value1);
/* 7036 */     }
````

Generated code with this PR
````java
/* 3807 */   private void apply_249(InternalRow i) {
/* 3808 */
/* 3809 */     boolean isNull998 = i.isNullAt(996);
/* 3810 */     UTF8String value998 = isNull998 ? null : (i.getUTF8String(996));
/* 3811 */     if (!isNull998) {
/* 3812 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value998.getBaseObject(), value998.getBaseOffset(), value998.numBytes(), value1);
/* 3813 */     }
/* 3814 */
/* 3815 */     boolean isNull999 = i.isNullAt(997);
/* 3816 */     UTF8String value999 = isNull999 ? null : (i.getUTF8String(997));
/* 3817 */     if (!isNull999) {
/* 3818 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value999.getBaseObject(), value999.getBaseOffset(), value999.numBytes(), value1);
/* 3819 */     }
/* 3820 */
/* 3821 */     boolean isNull1000 = i.isNullAt(998);
/* 3822 */     UTF8String value1000 = isNull1000 ? null : (i.getUTF8String(998));
/* 3823 */     if (!isNull1000) {
/* 3824 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1000.getBaseObject(), value1000.getBaseOffset(), value1000.numBytes(), value1);
/* 3825 */     }
/* 3826 */
/* 3827 */     boolean isNull1001 = i.isNullAt(999);
/* 3828 */     UTF8String value1001 = isNull1001 ? null : (i.getUTF8String(999));
/* 3829 */     if (!isNull1001) {
/* 3830 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1001.getBaseObject(), value1001.getBaseOffset(), value1001.numBytes(), value1);
/* 3831 */     }
/* 3832 */
/* 3833 */   }
/* 3834 */
...
/* 4532 */   private void apply_0(InternalRow i) {
/* 4533 */
/* 4534 */     boolean isNull2 = i.isNullAt(0);
/* 4535 */     UTF8String value2 = isNull2 ? null : (i.getUTF8String(0));
/* 4536 */     if (!isNull2) {
/* 4537 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value2.getBaseObject(), value2.getBaseOffset(), value2.numBytes(), value1);
/* 4538 */     }
/* 4539 */
/* 4540 */     boolean isNull3 = i.isNullAt(1);
/* 4541 */     UTF8String value3 = isNull3 ? null : (i.getUTF8String(1));
/* 4542 */     if (!isNull3) {
/* 4543 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value3.getBaseObject(), value3.getBaseOffset(), value3.numBytes(), value1);
/* 4544 */     }
/* 4545 */
/* 4546 */     boolean isNull4 = i.isNullAt(2);
/* 4547 */     UTF8String value4 = isNull4 ? null : (i.getUTF8String(2));
/* 4548 */     if (!isNull4) {
/* 4549 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value4.getBaseObject(), value4.getBaseOffset(), value4.numBytes(), value1);
/* 4550 */     }
/* 4551 */
/* 4552 */     boolean isNull5 = i.isNullAt(3);
/* 4553 */     UTF8String value5 = isNull5 ? null : (i.getUTF8String(3));
/* 4554 */     if (!isNull5) {
/* 4555 */       value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value5.getBaseObject(), value5.getBaseOffset(), value5.numBytes(), value1);
/* 4556 */     }
/* 4557 */
/* 4558 */   }
...
/* 7344 */   public UnsafeRow apply(InternalRow i) {
/* 7345 */     boolean isNull = false;
/* 7346 */
/* 7347 */     value1 = 42;
/* 7348 */     apply_0(i);
/* 7349 */     apply_1(i);
...
/* 7596 */     apply_248(i);
/* 7597 */     apply_249(i);
/* 7598 */     apply_250(i);
/* 7599 */     apply_251(i);
...
````

## How was this patch tested?

Add a new test in `DataFrameSuite`

Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>

Closes #15745 from kiszk/SPARK-18207.

## What changes were proposed in this pull request?

This Pull request comprises of the critical bug SPARK-16575 changes. This change rectifies the issue with BinaryFileRDD partition calculations as  upon creating an RDD with sc.binaryFiles, the resulting RDD always just consisted of two partitions only.
## How was this patch tested?

The original issue ie. getNumPartitions on binary Files RDD (always having two partitions) was first replicated and then tested upon the changes. Also the unit tests have been checked and passed.

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

srowen hvanhovell rxin vanzin skyluc kmader zsxwing datafarmer Please have a look .

Author: fidato <fidato.july13@gmail.com>

Closes #15327 from fidato13/SPARK-16575.

### What changes were proposed in this pull request?
Based on the discussion in [SPARK-18209](https://issues.apache.org/jira/browse/SPARK-18209). It doesn't really make sense to create permanent views based on temporary views or temporary UDFs.

To disallow the supports and issue the exceptions, this PR needs to detect whether a temporary view/UDF is being used when defining a permanent view. Basically, this PR can be split to two sub-tasks:

**Task 1:** detecting a temporary view from the query plan of view definition.
When finding an unresolved temporary view, Analyzer replaces it by a `SubqueryAlias` with the corresponding logical plan, which is stored in an in-memory HashMap. After replacement, it is impossible to detect whether the `SubqueryAlias` is added/generated from a temporary view. Thus, to detect the usage of a temporary view in view definition, this PR traverses the unresolved logical plan and uses the name of an `UnresolvedRelation` to detect whether it is a (global) temporary view.

**Task 2:** detecting a temporary UDF from the query plan of view definition.
Detecting usage of a temporary UDF in view definition is not straightfoward.

First, in the analyzed plan, we are having different forms to represent the functions. More importantly, some classes (e.g., `HiveGenericUDF`) are not accessible from `CreateViewCommand`, which is part of  `sql/core`. Thus, we used the unanalyzed plan `child` of `CreateViewCommand` to detect the usage of a temporary UDF. Because the plan has already been successfully analyzed, we can assume the functions have been defined/registered.

Second, in Spark, the functions have four forms: Spark built-in functions, built-in hash functions, permanent UDFs and temporary UDFs. We do not have any direct way to determine whether a function is temporary or not. Thus, we introduced a function `isTemporaryFunction` in `SessionCatalog`. This function contains the detailed logics to determine whether a function is temporary or not.

### How was this patch tested?
Added test cases.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #15764 from gatorsmile/blockTempFromPermViewCreation.

## What changes were proposed in this pull request?

Right now, there is no way to join the output of a memory sink with any table:

> UnsupportedOperationException: LeafNode MemoryPlan must implement statistics

This patch adds statistics to MemorySink, making joining snapshots of memory streams with tables possible.

## How was this patch tested?

Added a test case.

Author: Liwei Lin <lwlin7@gmail.com>

Closes #15786 from lw-lin/memory-sink-stat.

## What changes were proposed in this pull request?

This adds support for Hive variables:

* Makes values set via `spark-sql --hivevar name=value` accessible
* Adds `getHiveVar` and `setHiveVar` to the `HiveClient` interface
* Adds a SessionVariables trait for sessions like Hive that support variables (including Hive vars)
* Adds SessionVariables support to variable substitution
* Adds SessionVariables support to the SET command

## How was this patch tested?

* Adds a test to all supported Hive versions for accessing Hive variables
* Adds HiveVariableSubstitutionSuite

Author: Ryan Blue <blue@apache.org>

Closes #15738 from rdblue/SPARK-18086-add-hivevar-support.

## What changes were proposed in this pull request?

This PR proposes to match up the behaviour of `to_json` to `from_json` function for null-safety.

Currently, it throws `NullPointException` but this PR fixes this to produce `null` instead.

with the data below:

```scala
import spark.implicits._

val df = Seq(Some(Tuple1(Tuple1(1))), None).toDF("a")
df.show()
```

```
+----+
|   a|
+----+
| [1]|
|null|
+----+
```

the codes below

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

df.select(to_json($"a")).show()
```

produces..

**Before**

throws `NullPointException` as below:

```
java.lang.NullPointerException
  at org.apache.spark.sql.catalyst.json.JacksonGenerator.org$apache$spark$sql$catalyst$json$JacksonGenerator$$writeFields(JacksonGenerator.scala:138)
  at org.apache.spark.sql.catalyst.json.JacksonGenerator$$anonfun$write$1.apply$mcV$sp(JacksonGenerator.scala:194)
  at org.apache.spark.sql.catalyst.json.JacksonGenerator.org$apache$spark$sql$catalyst$json$JacksonGenerator$$writeObject(JacksonGenerator.scala:131)
  at org.apache.spark.sql.catalyst.json.JacksonGenerator.write(JacksonGenerator.scala:193)
  at org.apache.spark.sql.catalyst.expressions.StructToJson.eval(jsonExpressions.scala:544)
  at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:142)
  at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:48)
  at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:30)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
```

**After**

```
+---------------+
|structtojson(a)|
+---------------+
|       {"_1":1}|
|           null|
+---------------+
```

## How was this patch tested?

Unit test in `JsonExpressionsSuite.scala` and `JsonFunctionsSuite.scala`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #15792 from HyukjinKwon/SPARK-18295.

## What changes were proposed in this pull request?

When profiling heap dumps from the HistoryServer and live Spark web UIs, I found a large amount of memory being wasted on duplicated objects and strings. This patch's changes remove most of this duplication, resulting in over 40% memory savings for some benchmarks.

- **Task metrics** (6441f0624dfcda9c7193a64bfb416a145b5aabdf): previously, every `TaskUIData` object would have its own instances of `InputMetricsUIData`, `OutputMetricsUIData`, `ShuffleReadMetrics`, and `ShuffleWriteMetrics`, but for many tasks these metrics are irrelevant because they're all zero. This patch changes how we construct these metrics in order to re-use a single immutable "empty" value for the cases where these metrics are empty.
- **TaskInfo.accumulables** (ade86db901127bf13c0e0bdc3f09c933a093bb76): Previously, every `TaskInfo` object had its own empty `ListBuffer` for holding updates from named accumulators. Tasks which didn't use named accumulators still paid for the cost of allocating and storing this empty buffer. To avoid this overhead, I changed the `val` with a mutable buffer into a `var` which holds an immutable Scala list, allowing tasks which do not have named accumulator updates to share the same singleton `Nil` object.
- **String.intern() in JSONProtocol** (7e05630e9a78c455db8c8c499f0590c864624e05): in the HistoryServer, executor hostnames and ids are deserialized from JSON, leading to massive duplication of these string objects. By calling `String.intern()` on the deserialized values we can remove all of this duplication. Since Spark now requires Java 7+ we don't have to worry about string interning exhausting the permgen (see http://java-performance.info/string-intern-in-java-6-7-8/).

## How was this patch tested?

I ran

```
sc.parallelize(1 to 100000, 100000).count()
```

in `spark-shell` with event logging enabled, then loaded that event log in the HistoryServer, performed a full GC, and took a heap dump. According to YourKit, the changes in this patch reduced memory consumption by roughly 28 megabytes (or 770k Java objects):

![image](https://cloud.githubusercontent.com/assets/50748/19953276/4f3a28aa-a129-11e6-93df-d7fa91396f66.png)

Here's a table illustrating the drop in objects due to deduplication (the drop is <100k for some objects because some events were dropped from the listener bus; this is a separate, existing bug that I'll address separately after CPU-profiling):

![image](https://cloud.githubusercontent.com/assets/50748/19953290/6a271290-a129-11e6-93ad-b825f1448886.png)

Author: Josh Rosen <joshrosen@databricks.com>

Closes #15743 from JoshRosen/spark-ui-memory-usage.

## What changes were proposed in this pull request?

Waiting for merging #13680

This PR optimizes `SerializeFromObject()` for an primitive array. This is derived from #13758 to address one of problems by using a simple way in #13758.

The current implementation always generates `GenericArrayData` from `SerializeFromObject()` for any type of an array in a logical plan. This involves a boxing at a constructor of `GenericArrayData` when `SerializedFromObject()` has an primitive array.

This PR enables to generate `UnsafeArrayData` from `SerializeFromObject()` for a primitive array. It can avoid boxing to create an instance of `ArrayData` in the generated code by Catalyst.

This PR also generate `UnsafeArrayData` in a case for `RowEncoder.serializeFor` or `CatalystTypeConverters.createToCatalystConverter`.

Performance improvement of `SerializeFromObject()` is up to 2.0x

```
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)

Without this PR
Write an array in Dataset:               Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------
Int                                            556 /  608         15.1          66.3       1.0X
Double                                        1668 / 1746          5.0         198.8       0.3X

with this PR
Write an array in Dataset:               Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------
Int                                            352 /  401         23.8          42.0       1.0X
Double                                         821 /  885         10.2          97.9       0.4X
```

Here is an example program that will happen in mllib as described in [SPARK-16070](https://issues.apache.org/jira/browse/SPARK-16070).

```
sparkContext.parallelize(Seq(Array(1, 2)), 1).toDS.map(e => e).show
```

Generated code before applying this PR

``` java
/* 039 */   protected void processNext() throws java.io.IOException {
/* 040 */     while (inputadapter_input.hasNext()) {
/* 041 */       InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 042 */       int[] inputadapter_value = (int[])inputadapter_row.get(0, null);
/* 043 */
/* 044 */       Object mapelements_obj = ((Expression) references[0]).eval(null);
/* 045 */       scala.Function1 mapelements_value1 = (scala.Function1) mapelements_obj;
/* 046 */
/* 047 */       boolean mapelements_isNull = false || false;
/* 048 */       int[] mapelements_value = null;
/* 049 */       if (!mapelements_isNull) {
/* 050 */         Object mapelements_funcResult = null;
/* 051 */         mapelements_funcResult = mapelements_value1.apply(inputadapter_value);
/* 052 */         if (mapelements_funcResult == null) {
/* 053 */           mapelements_isNull = true;
/* 054 */         } else {
/* 055 */           mapelements_value = (int[]) mapelements_funcResult;
/* 056 */         }
/* 057 */
/* 058 */       }
/* 059 */       mapelements_isNull = mapelements_value == null;
/* 060 */
/* 061 */       serializefromobject_argIsNulls[0] = mapelements_isNull;
/* 062 */       serializefromobject_argValue = mapelements_value;
/* 063 */
/* 064 */       boolean serializefromobject_isNull = false;
/* 065 */       for (int idx = 0; idx < 1; idx++) {
/* 066 */         if (serializefromobject_argIsNulls[idx]) { serializefromobject_isNull = true; break; }
/* 067 */       }
/* 068 */
/* 069 */       final ArrayData serializefromobject_value = serializefromobject_isNull ? null : new org.apache.spark.sql.catalyst.util.GenericArrayData(serializefromobject_argValue);
/* 070 */       serializefromobject_holder.reset();
/* 071 */
/* 072 */       serializefromobject_rowWriter.zeroOutNullBytes();
/* 073 */
/* 074 */       if (serializefromobject_isNull) {
/* 075 */         serializefromobject_rowWriter.setNullAt(0);
/* 076 */       } else {
/* 077 */         // Remember the current cursor so that we can calculate how many bytes are
/* 078 */         // written later.
/* 079 */         final int serializefromobject_tmpCursor = serializefromobject_holder.cursor;
/* 080 */
/* 081 */         if (serializefromobject_value instanceof UnsafeArrayData) {
/* 082 */           final int serializefromobject_sizeInBytes = ((UnsafeArrayData) serializefromobject_value).getSizeInBytes();
/* 083 */           // grow the global buffer before writing data.
/* 084 */           serializefromobject_holder.grow(serializefromobject_sizeInBytes);
/* 085 */           ((UnsafeArrayData) serializefromobject_value).writeToMemory(serializefromobject_holder.buffer, serializefromobject_holder.cursor);
/* 086 */           serializefromobject_holder.cursor += serializefromobject_sizeInBytes;
/* 087 */
/* 088 */         } else {
/* 089 */           final int serializefromobject_numElements = serializefromobject_value.numElements();
/* 090 */           serializefromobject_arrayWriter.initialize(serializefromobject_holder, serializefromobject_numElements, 4);
/* 091 */
/* 092 */           for (int serializefromobject_index = 0; serializefromobject_index < serializefromobject_numElements; serializefromobject_index++) {
/* 093 */             if (serializefromobject_value.isNullAt(serializefromobject_index)) {
/* 094 */               serializefromobject_arrayWriter.setNullInt(serializefromobject_index);
/* 095 */             } else {
/* 096 */               final int serializefromobject_element = serializefromobject_value.getInt(serializefromobject_index);
/* 097 */               serializefromobject_arrayWriter.write(serializefromobject_index, serializefromobject_element);
/* 098 */             }
/* 099 */           }
/* 100 */         }
/* 101 */
/* 102 */         serializefromobject_rowWriter.setOffsetAndSize(0, serializefromobject_tmpCursor, serializefromobject_holder.cursor - serializefromobject_tmpCursor);
/* 103 */       }
/* 104 */       serializefromobject_result.setTotalSize(serializefromobject_holder.totalSize());
/* 105 */       append(serializefromobject_result);
/* 106 */       if (shouldStop()) return;
/* 107 */     }
/* 108 */   }
/* 109 */ }
```

Generated code after applying this PR

``` java
/* 035 */   protected void processNext() throws java.io.IOException {
/* 036 */     while (inputadapter_input.hasNext()) {
/* 037 */       InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 038 */       int[] inputadapter_value = (int[])inputadapter_row.get(0, null);
/* 039 */
/* 040 */       Object mapelements_obj = ((Expression) references[0]).eval(null);
/* 041 */       scala.Function1 mapelements_value1 = (scala.Function1) mapelements_obj;
/* 042 */
/* 043 */       boolean mapelements_isNull = false || false;
/* 044 */       int[] mapelements_value = null;
/* 045 */       if (!mapelements_isNull) {
/* 046 */         Object mapelements_funcResult = null;
/* 047 */         mapelements_funcResult = mapelements_value1.apply(inputadapter_value);
/* 048 */         if (mapelements_funcResult == null) {
/* 049 */           mapelements_isNull = true;
/* 050 */         } else {
/* 051 */           mapelements_value = (int[]) mapelements_funcResult;
/* 052 */         }
/* 053 */
/* 054 */       }
/* 055 */       mapelements_isNull = mapelements_value == null;
/* 056 */
/* 057 */       boolean serializefromobject_isNull = mapelements_isNull;
/* 058 */       final ArrayData serializefromobject_value = serializefromobject_isNull ? null : org.apache.spark.sql.catalyst.expressions.UnsafeArrayData.fromPrimitiveArray(mapelements_value);
/* 059 */       serializefromobject_isNull = serializefromobject_value == null;
/* 060 */       serializefromobject_holder.reset();
/* 061 */
/* 062 */       serializefromobject_rowWriter.zeroOutNullBytes();
/* 063 */
/* 064 */       if (serializefromobject_isNull) {
/* 065 */         serializefromobject_rowWriter.setNullAt(0);
/* 066 */       } else {
/* 067 */         // Remember the current cursor so that we can calculate how many bytes are
/* 068 */         // written later.
/* 069 */         final int serializefromobject_tmpCursor = serializefromobject_holder.cursor;
/* 070 */
/* 071 */         if (serializefromobject_value instanceof UnsafeArrayData) {
/* 072 */           final int serializefromobject_sizeInBytes = ((UnsafeArrayData) serializefromobject_value).getSizeInBytes();
/* 073 */           // grow the global buffer before writing data.
/* 074 */           serializefromobject_holder.grow(serializefromobject_sizeInBytes);
/* 075 */           ((UnsafeArrayData) serializefromobject_value).writeToMemory(serializefromobject_holder.buffer, serializefromobject_holder.cursor);
/* 076 */           serializefromobject_holder.cursor += serializefromobject_sizeInBytes;
/* 077 */
/* 078 */         } else {
/* 079 */           final int serializefromobject_numElements = serializefromobject_value.numElements();
/* 080 */           serializefromobject_arrayWriter.initialize(serializefromobject_holder, serializefromobject_numElements, 4);
/* 081 */
/* 082 */           for (int serializefromobject_index = 0; serializefromobject_index < serializefromobject_numElements; serializefromobject_index++) {
/* 083 */             if (serializefromobject_value.isNullAt(serializefromobject_index)) {
/* 084 */               serializefromobject_arrayWriter.setNullInt(serializefromobject_index);
/* 085 */             } else {
/* 086 */               final int serializefromobject_element = serializefromobject_value.getInt(serializefromobject_index);
/* 087 */               serializefromobject_arrayWriter.write(serializefromobject_index, serializefromobject_element);
/* 088 */             }
/* 089 */           }
/* 090 */         }
/* 091 */
/* 092 */         serializefromobject_rowWriter.setOffsetAndSize(0, serializefromobject_tmpCursor, serializefromobject_holder.cursor - serializefromobject_tmpCursor);
/* 093 */       }
/* 094 */       serializefromobject_result.setTotalSize(serializefromobject_holder.totalSize());
/* 095 */       append(serializefromobject_result);
/* 096 */       if (shouldStop()) return;
/* 097 */     }
/* 098 */   }
/* 099 */ }
```
## How was this patch tested?

Added a test in `DatasetSuite`, `RowEncoderSuite`, and `CatalystTypeConvertersSuite`

Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>

Closes #15044 from kiszk/SPARK-17490.

## What changes were proposed in this pull request?

Close `FileStreams`, `ZipFiles` etc to release the resources after using. Not closing the resources will cause IO Exception to be raised while deleting temp files.
## How was this patch tested?

Existing tests

Author: U-FAREAST\tl <tl@microsoft.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Tao LI <tl@microsoft.com>

Closes #15618 from HyukjinKwon/SPARK-14914-1.

## What changes were proposed in this pull request?

Add a function to check if two integers are compatible when invoking `acceptsType()` in `DataType`.
## How was this patch tested?

Manually.
E.g.

```
    spark.sql("create table t3(a map<bigint, array<string>>)")
    spark.sql("select * from t3 where a[1] is not null")
```

Before:

```
cannot resolve 't.`a`[1]' due to data type mismatch: argument 2 requires bigint type, however, '1' is of int type.; line 1 pos 22
org.apache.spark.sql.AnalysisException: cannot resolve 't.`a`[1]' due to data type mismatch: argument 2 requires bigint type, however, '1' is of int type.; line 1 pos 22
    at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
    at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:82)
    at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:307)
```

After:
 Run the sql queries above. No errors.

Author: Weiqing Yang <yangweiqing001@gmail.com>

Closes #15448 from weiqingy/SPARK_17108.

[SPARK-18283][STRUCTURED STREAMING][KAFKA] Added test to check whether default starting offset in latest

## What changes were proposed in this pull request?

Added test to check whether default starting offset in latest

## How was this patch tested?
new unit test

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

Closes #15778 from tdas/SPARK-18283.

## What changes were proposed in this pull request?
SparkR ```spark.glm``` predict should output original label when family = "binomial".

## How was this patch tested?
Add unit test.
You can also run the following code to test:
```R
training <- suppressWarnings(createDataFrame(iris))
training <- training[training$Species %in% c("versicolor", "virginica"), ]
model <- spark.glm(training, Species ~ Sepal_Length + Sepal_Width,family = binomial(link = "logit"))
showDF(predict(model, training))
```
Before this change:
```
+------------+-----------+------------+-----------+----------+-----+-------------------+
|Sepal_Length|Sepal_Width|Petal_Length|Petal_Width|   Species|label|         prediction|
+------------+-----------+------------+-----------+----------+-----+-------------------+
|         7.0|        3.2|         4.7|        1.4|versicolor|  0.0| 0.8271421517601544|
|         6.4|        3.2|         4.5|        1.5|versicolor|  0.0| 0.6044595910413112|
|         6.9|        3.1|         4.9|        1.5|versicolor|  0.0| 0.7916340858281998|
|         5.5|        2.3|         4.0|        1.3|versicolor|  0.0|0.16080518180591158|
|         6.5|        2.8|         4.6|        1.5|versicolor|  0.0| 0.6112229217050189|
|         5.7|        2.8|         4.5|        1.3|versicolor|  0.0| 0.2555087295500885|
|         6.3|        3.3|         4.7|        1.6|versicolor|  0.0| 0.5681507664364834|
|         4.9|        2.4|         3.3|        1.0|versicolor|  0.0|0.05990570219972002|
|         6.6|        2.9|         4.6|        1.3|versicolor|  0.0| 0.6644434078306246|
|         5.2|        2.7|         3.9|        1.4|versicolor|  0.0|0.11293577405862379|
|         5.0|        2.0|         3.5|        1.0|versicolor|  0.0|0.06152372321585971|
|         5.9|        3.0|         4.2|        1.5|versicolor|  0.0|0.35250697207602555|
|         6.0|        2.2|         4.0|        1.0|versicolor|  0.0|0.32267018290814303|
|         6.1|        2.9|         4.7|        1.4|versicolor|  0.0|  0.433391153814592|
|         5.6|        2.9|         3.6|        1.3|versicolor|  0.0| 0.2280744262436993|
|         6.7|        3.1|         4.4|        1.4|versicolor|  0.0| 0.7219848389339459|
|         5.6|        3.0|         4.5|        1.5|versicolor|  0.0|0.23527698971404695|
|         5.8|        2.7|         4.1|        1.0|versicolor|  0.0|  0.285024533520016|
|         6.2|        2.2|         4.5|        1.5|versicolor|  0.0| 0.4107047877447493|
|         5.6|        2.5|         3.9|        1.1|versicolor|  0.0|0.20083561961645083|
+------------+-----------+------------+-----------+----------+-----+-------------------+
```
After this change:
```
+------------+-----------+------------+-----------+----------+-----+----------+
|Sepal_Length|Sepal_Width|Petal_Length|Petal_Width|   Species|label|prediction|
+------------+-----------+------------+-----------+----------+-----+----------+
|         7.0|        3.2|         4.7|        1.4|versicolor|  0.0| virginica|
|         6.4|        3.2|         4.5|        1.5|versicolor|  0.0| virginica|
|         6.9|        3.1|         4.9|        1.5|versicolor|  0.0| virginica|
|         5.5|        2.3|         4.0|        1.3|versicolor|  0.0|versicolor|
|         6.5|        2.8|         4.6|        1.5|versicolor|  0.0| virginica|
|         5.7|        2.8|         4.5|        1.3|versicolor|  0.0|versicolor|
|         6.3|        3.3|         4.7|        1.6|versicolor|  0.0| virginica|
|         4.9|        2.4|         3.3|        1.0|versicolor|  0.0|versicolor|
|         6.6|        2.9|         4.6|        1.3|versicolor|  0.0| virginica|
|         5.2|        2.7|         3.9|        1.4|versicolor|  0.0|versicolor|
|         5.0|        2.0|         3.5|        1.0|versicolor|  0.0|versicolor|
|         5.9|        3.0|         4.2|        1.5|versicolor|  0.0|versicolor|
|         6.0|        2.2|         4.0|        1.0|versicolor|  0.0|versicolor|
|         6.1|        2.9|         4.7|        1.4|versicolor|  0.0|versicolor|
|         5.6|        2.9|         3.6|        1.3|versicolor|  0.0|versicolor|
|         6.7|        3.1|         4.4|        1.4|versicolor|  0.0| virginica|
|         5.6|        3.0|         4.5|        1.5|versicolor|  0.0|versicolor|
|         5.8|        2.7|         4.1|        1.0|versicolor|  0.0|versicolor|
|         6.2|        2.2|         4.5|        1.5|versicolor|  0.0|versicolor|
|         5.6|        2.5|         3.9|        1.1|versicolor|  0.0|versicolor|
+------------+-----------+------------+-----------+----------+-----+----------+
```

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #15788 from yanboliang/spark-18291.

## What changes were proposed in this pull request?

As reported in the jira, sometimes the generated java code in codegen will cause compilation error.

Code snippet to test it:

    case class Route(src: String, dest: String, cost: Int)
    case class GroupedRoutes(src: String, dest: String, routes: Seq[Route])

    val ds = sc.parallelize(Array(
      Route("a", "b", 1),
      Route("a", "b", 2),
      Route("a", "c", 2),
      Route("a", "d", 10),
      Route("b", "a", 1),
      Route("b", "a", 5),
      Route("b", "c", 6))
    ).toDF.as[Route]

    val grped = ds.map(r => GroupedRoutes(r.src, r.dest, Seq(r)))
      .groupByKey(r => (r.src, r.dest))
      .reduceGroups { (g1: GroupedRoutes, g2: GroupedRoutes) =>
        GroupedRoutes(g1.src, g1.dest, g1.routes ++ g2.routes)
      }.map(_._2)

The problem here is, in `ReferenceToExpressions` we evaluate the children vars to local variables. Then the result expression is evaluated to use those children variables. In the above case, the result expression code is too long and will be split by `CodegenContext.splitExpression`. So those local variables cannot be accessed and cause compilation error.

## How was this patch tested?

Jenkins tests.

Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #15693 from viirya/fix-codege-compilation-error.