## What changes were proposed in this pull request?

In summary, cost of recreating a KafkaProducer for writing every batch is high as it starts a lot threads and make connections and then closes them. A KafkaProducer instance is promised to be thread safe in Kafka docs. Reuse of KafkaProducer instance while writing via multiple threads is encouraged.

Furthermore, I have performance improvement of 10x in latency, with this patch.

### These are times that addBatch took in ms. Without applying this patch
![with-out_patch](https://cloud.githubusercontent.com/assets/992952/23994612/a9de4a42-0a6b-11e7-9d5b-7ae18775bee4.png)
### These are times that addBatch took in ms. After applying this patch
![with_patch](https://cloud.githubusercontent.com/assets/992952/23994616/ad8c11ec-0a6b-11e7-8634-2266ebb5033f.png)

## How was this patch tested?
Running distributed benchmarks comparing runs with this patch and without it.
Added relevant unit tests.

Author: Prashant Sharma <prashsh1@in.ibm.com>

Closes #17308 from ScrapCodes/cached-kafka-producer.

## What changes were proposed in this pull request?

Add additional function description for weekofyear.

## How was this patch tested?

 manual tests

![weekofyear](https://cloud.githubusercontent.com/assets/5399861/26525752/08a1c278-4394-11e7-8988-7cbf82c3a999.gif)

Author: Yuming Wang <wgyumg@gmail.com>

Closes #18132 from wangyum/SPARK-8184.

## What changes were proposed in this pull request?

Supress console output by using `testQuietly` in test suites

## How was this patch tested?

Tested by `"SPARK-19372: Filter can be executed w/o generated code due to JVM code size limit"` in `DataFrameSuite`

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

Closes #18135 from kiszk/SPARK-20907.

## What changes were proposed in this pull request?

This PR adds built-in SQL function `(REPLACE(<string_expression>, <search_string> [, <replacement_string>])`

`REPLACE()` return that string that is replaced all occurrences with given string.

## How was this patch tested?

added new test suites

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

Closes #18047 from kiszk/SPARK-20750.

## What changes were proposed in this pull request?

See class doc of `ConstantPropagation` for the approach used.

## How was this patch tested?

- Added unit tests

Author: Tejas Patil <tejasp@fb.com>

Closes #17993 from tejasapatil/SPARK-20758_const_propagation.

## What changes were proposed in this pull request?

Now, we have two sources of statistics, i.e. Spark's stats and Hive's stats. Spark's stats is generated by running "analyze" command in Spark. Once it's available, we respect this stats over Hive's.

This pr is to clearly document in related code the mechanism to choose between these two sources of stats.

## How was this patch tested?

Not related.

Author: Zhenhua Wang <wzh_zju@163.com>

Closes #18105 from wzhfy/cboSwitchStats.

## What changes were proposed in this pull request?
This pr added parsing rules to support table column aliases in FROM clause.

## How was this patch tested?
Added tests in `PlanParserSuite`,  `SQLQueryTestSuite`, and `PlanParserSuite`.

Author: Takeshi Yamamuro <yamamuro@apache.org>

Closes #18079 from maropu/SPARK-20841.

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

In Cache manager, the plan matching should ignore Hint.
```Scala
      val df1 = spark.range(10).join(broadcast(spark.range(10)))
      df1.cache()
      spark.range(10).join(spark.range(10)).explain()
```
The output plan of the above query shows that the second query is  not using the cached data of the first query.
```
BroadcastNestedLoopJoin BuildRight, Inner
:- *Range (0, 10, step=1, splits=2)
+- BroadcastExchange IdentityBroadcastMode
   +- *Range (0, 10, step=1, splits=2)
```

After the fix, the plan becomes
```
InMemoryTableScan [id#20L, id#23L]
   +- InMemoryRelation [id#20L, id#23L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
         +- BroadcastNestedLoopJoin BuildRight, Inner
            :- *Range (0, 10, step=1, splits=2)
            +- BroadcastExchange IdentityBroadcastMode
               +- *Range (0, 10, step=1, splits=2)
```

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

Author: Xiao Li <gatorsmile@gmail.com>

Closes #18131 from gatorsmile/HintCache.

[SPARK-20876][SQL] If the input parameter is float type for ceil or floor,the result is not we expected

## What changes were proposed in this pull request?

spark-sql>SELECT ceil(cast(12345.1233 as float));
spark-sql>12345
For this case, the result we expected is `12346`
spark-sql>SELECT floor(cast(-12345.1233 as float));
spark-sql>-12345
For this case, the result we expected is `-12346`

Because in `Ceil` or `Floor`, `inputTypes` has no FloatType, so it is converted to LongType.
## How was this patch tested?

After the modification:
spark-sql>SELECT ceil(cast(12345.1233 as float));
spark-sql>12346
spark-sql>SELECT floor(cast(-12345.1233 as float));
spark-sql>-12346

Author: liuxian <liu.xian3@zte.com.cn>

Closes #18103 from 10110346/wip-lx-0525-1.

## What changes were proposed in this pull request?

The failed test case is, we have a `SortMergeJoinExec` for a self-join, which means we have a `ReusedExchange` node in the query plan. It works fine without caching, but throws an exception in `SortMergeJoinExec.outputPartitioning` if we cache it.

The root cause is, `ReusedExchange` doesn't propagate the output partitioning from its child, so in `SortMergeJoinExec.outputPartitioning` we create `PartitioningCollection` with a hash partitioning and an unknown partitioning, and fail.

This bug is mostly fine, because inserting the `ReusedExchange` is the last step to prepare the physical plan, we won't call `SortMergeJoinExec.outputPartitioning` anymore after this.

However, if the dataframe is cached, the physical plan of it becomes `InMemoryTableScanExec`, which contains another physical plan representing the cached query, and it has gone through the entire planning phase and may have `ReusedExchange`. Then the planner call `InMemoryTableScanExec.outputPartitioning`, which then calls `SortMergeJoinExec.outputPartitioning` and trigger this bug.

## How was this patch tested?

a new regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #18121 from cloud-fan/bug.

[https://issues.apache.org/jira/browse/SPARK-20875](https://issues.apache.org/jira/browse/SPARK-20875)
When the "deleteRecursively" method is invoked,spark doesn't print any log if the path was deleted.For example,spark only print "Removing directory" when the worker began cleaning spark.work.dir,but didn't print any log about "the path has been delete".So, I can't judge whether the path was deleted form the worker's logfile,If there is any accidents about Linux.

Author: liuzhaokun <liu.zhaokun@zte.com.cn>

Closes #18102 from liu-zhaokun/master_log.

## What changes were proposed in this pull request?

Add a `worker` configuration to set how long to wait before forcibly killing driver.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #18126 from zsxwing/SPARK-20843.

## What changes were proposed in this pull request?
Add built-in SQL function `CH[A]R`:
For `CHR(bigint|double n)`, returns the ASCII character having the binary equivalent to `n`. If n is larger than 256 the result is equivalent to CHR(n % 256)

## How was this patch tested?
unit tests

Author: Yuming Wang <wgyumg@gmail.com>

Closes #18019 from wangyum/SPARK-20748.

## What changes were proposed in this pull request?

This PR includes some minor improvement for the comments and tests in https://github.com/apache/spark/pull/16989

## How was this patch tested?

N/A

Author: Wenchen Fan <wenchen@databricks.com>

Closes #18117 from cloud-fan/follow.

## What changes were proposed in this pull request?

This PR makes spark-submit script download remote files to local file system for local/standalone client mode.

## How was this patch tested?

- Unit tests
- Manual tests by adding s3a jar and testing against file on s3.

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

Author: Yu Peng <loneknightpy@gmail.com>

Closes #18078 from loneknightpy/download-jar-in-spark-submit.

## What changes were proposed in this pull request?
Upon encountering an invalid columntype, the column type object is printed, rather than the type.
This  change improves this by outputting its name.

## How was this patch tested?
Added a simple  unit test to verify the contents of the raised exception

Author: setjet <rubenljanssen@gmail.com>

Closes #18097 from setjet/spark-20873.

## What changes were proposed in this pull request?

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

## How was this patch tested?

Manual tests, docs build.

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

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

## What changes were proposed in this pull request?

When the individual partition size in a spill is small, mergeSpillsWithTransferTo method does many small disk ios which is really inefficient. One way to improve the performance will be to use mergeSpillsWithFileStream method by turning off transfer to and using buffered file read/write to improve the io throughput.
However, the current implementation of mergeSpillsWithFileStream does not do a buffer read/write of the files and in addition to that it unnecessarily flushes the output files for each partitions.

## How was this patch tested?

Tested this change by running a job on the cluster and the map stage run time was reduced by around 20%.

Author: Sital Kedia <skedia@fb.com>

Closes #17343 from sitalkedia/upstream_mergeSpillsWithFileStream.

Now that Structured Streaming has been out for several Spark release and has large production use cases, the `Experimental` label is no longer appropriate.  I've left `InterfaceStability.Evolving` however, as I think we may make a few changes to the pluggable Source & Sink API in Spark 2.3.

Author: Michael Armbrust <michael@databricks.com>

Closes #18065 from marmbrus/streamingGA.

[SPARK-20835][CORE] It should exit directly when the --total-executor-cores parameter is setted less than 0 when submit a application

## What changes were proposed in this pull request?
In my test, the submitted app running with out an error when the --total-executor-cores less than 0
and given the warnings:
"2017-05-22 17:19:36,319 WARN org.apache.spark.scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources";

It should exit directly when the --total-executor-cores parameter is setted less than 0 when submit a application
(Please fill in changes proposed in this fix)

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

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

Author: 10129659 <chen.yanshan@zte.com.cn>

Closes #18060 from eatoncys/totalcores.

## What changes were proposed in this pull request?

`ConfigBuilder` builds `ConfigEntry` which can only read value with one key, if we wanna change the config name but still keep the old one, it's hard to do.

This PR introduce `ConfigBuilder.withAlternative`, to support reading config value with alternative keys. And also rename `spark.scheduler.listenerbus.eventqueue.size` to `spark.scheduler.listenerbus.eventqueue.capacity` with this feature, according to https://github.com/apache/spark/pull/14269#discussion_r118432313

## How was this patch tested?

a new test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #18110 from cloud-fan/config.

## What changes were proposed in this pull request?

Include documentation of the fact that the updateFunc is sometimes called with no new values. This is documented in the main documentation here: https://spark.apache.org/docs/latest/streaming-programming-guide.html#updatestatebykey-operation however from the docs included with the code it is not clear that this is the case.

## How was this patch tested?

PR only changes comments. Confirmed code still builds.

Author: Wil Selwood <wil.selwood@sa.catapult.org.uk>

Closes #18088 from wselwood/note-edge-case-in-docs.

## What changes were proposed in this pull request?

Long time ago we fixed a [bug](https://issues.apache.org/jira/browse/SPARK-3948) in shuffle writer about `FileChannel.transferTo`. We were not very confident about that fix, so we added a position check after the writing, try to discover the bug earlier.

 However this checking is missing in the new `UnsafeShuffleWriter`, this PR adds it.

https://issues.apache.org/jira/browse/SPARK-18105 maybe related to that `FileChannel.transferTo` bug, hopefully we can find out the root cause after adding this position check.

## How was this patch tested?

N/A

Author: Wenchen Fan <wenchen@databricks.com>

Closes #18091 from cloud-fan/shuffle.

## What changes were proposed in this pull request?
1, add an example for sparkr `decisionTree`
2, document it in user guide

## How was this patch tested?
local submit

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #18067 from zhengruifeng/dt_example.

## What changes were proposed in this pull request?

It is reported that there is performance downgrade when applying ML pipeline for dataset with many columns but few rows.

A big part of the performance downgrade comes from some operations (e.g., `select`) on DataFrame/Dataset which re-create new DataFrame/Dataset with a new `LogicalPlan`. The cost can be ignored in the usage of SQL, normally.

However, it's not rare to chain dozens of pipeline stages in ML. When the query plan grows incrementally during running those stages, the total cost spent on re-creation of DataFrame grows too. In particular, the `Analyzer` will go through the big query plan even most part of it is analyzed.

By eliminating part of the cost, the time to run the example code locally is reduced from about 1min to about 30 secs.

In particular, the time applying the pipeline locally is mostly spent on calling transform of the 137 `Bucketizer`s. Before the change, each call of `Bucketizer`'s transform can cost about 0.4 sec. So the total time spent on all `Bucketizer`s' transform is about 50 secs. After the change, each call only costs about 0.1 sec.

<del>We also make `boundEnc` as lazy variable to reduce unnecessary running time.</del>

### Performance improvement

The codes and datasets provided by Barry Becker to re-produce this issue and benchmark can be found on the JIRA.

Before this patch: about 1 min
After this patch: about 20 secs

## How was this patch tested?

Existing tests.

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

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

Closes #17770 from viirya/SPARK-20392.

## What changes were proposed in this pull request?
When handling strings, the category dropped by RFormula and R are different:
- RFormula drops the least frequent level
- R drops the first level after ascending alphabetical ordering

This PR supports different string ordering types in StringIndexer #17879 so that RFormula can drop the same level as R when handling strings using`stringOrderType = "alphabetDesc"`.

## How was this patch tested?
new tests

Author: Wayne Zhang <actuaryzhang@uber.com>

Closes #17967 from actuaryzhang/RFormula.

## What changes were proposed in this pull request?

from_json function required to take in a java.util.Hashmap. For other functions, a java wrapper is provided which casts a java hashmap to a scala map. Only a java function is provided in this case, forcing scala users to pass in a java.util.Hashmap.

Added the missing wrapper.

## How was this patch tested?
Added a unit test for passing in a scala map

Author: setjet <rubenljanssen@gmail.com>

Closes #18094 from setjet/spark-20775.

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

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

## What changes were proposed in this pull request?

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

Author: Michael Allman <michael@videoamp.com>

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

## What changes were proposed in this pull request?

Add Structured Streaming Kafka Source to the `examples` project so that people can run `bin/run-example StructuredKafkaWordCount ...`.

## How was this patch tested?

manually tested it.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #18101 from zsxwing/add-missing-example-dep.

[SPARK-19707][SPARK-18922][TESTS][SQL][CORE] Fix test failures/the invalid path check for sc.addJar on Windows

## What changes were proposed in this pull request?

This PR proposes two things:

- A follow up for SPARK-19707 (Improving the invalid path check for sc.addJar on Windows as well).

```
org.apache.spark.SparkContextSuite:
 - add jar with invalid path *** FAILED *** (32 milliseconds)
   2 was not equal to 1 (SparkContextSuite.scala:309)
   ...
```

- Fix path vs URI related test failures on Windows.

```
org.apache.spark.storage.LocalDirsSuite:
 - SPARK_LOCAL_DIRS override also affects driver *** FAILED *** (0 milliseconds)
   new java.io.File("/NONEXISTENT_PATH").exists() was true (LocalDirsSuite.scala:50)
   ...

 - Utils.getLocalDir() throws an exception if any temporary directory cannot be retrieved *** FAILED *** (15 milliseconds)
   Expected exception java.io.IOException to be thrown, but no exception was thrown. (LocalDirsSuite.scala:64)
   ...
```

```
org.apache.spark.sql.hive.HiveSchemaInferenceSuite:
 - orc: schema should be inferred and saved when INFER_AND_SAVE is specified *** FAILED *** (203 milliseconds)
   java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-dae61ab3-a851-4dd3-bf4e-be97c501f254
   ...

 - parquet: schema should be inferred and saved when INFER_AND_SAVE is specified *** FAILED *** (203 milliseconds)
   java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-fa3aff89-a66e-4376-9a37-2a9b87596939
   ...

 - orc: schema should be inferred but not stored when INFER_ONLY is specified *** FAILED *** (141 milliseconds)
   java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-fb464e59-b049-481b-9c75-f53295c9fc2c
   ...

 - parquet: schema should be inferred but not stored when INFER_ONLY is specified *** FAILED *** (125 milliseconds)
   java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-9487568e-80a4-42b3-b0a5-d95314c4ccbc
   ...

 - orc: schema should not be inferred when NEVER_INFER is specified *** FAILED *** (156 milliseconds)
   java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-0d2dfa45-1b0f-4958-a8be-1074ed0135a
   ...

 - parquet: schema should not be inferred when NEVER_INFER is specified *** FAILED *** (547 milliseconds)
   java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-6d95d64e-613e-4a59-a0f6-d198c5aa51ee
   ...
```

```
org.apache.spark.sql.execution.command.DDLSuite:
 - create temporary view using *** FAILED *** (15 milliseconds)
   org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark	arget	mpspark-3881d9ca-561b-488d-90b9-97587472b853	mp;
   ...

 - insert data to a data source table which has a non-existing location should succeed *** FAILED *** (109 milliseconds)
   file:/C:projectsspark%09arget%09mpspark-4cad3d19-6085-4b75-b407-fe5e9d21df54 did not equal file:///C:/projects/spark/target/tmp/spark-4cad3d19-6085-4b75-b407-fe5e9d21df54 (DDLSuite.scala:1869)
   ...

 - insert into a data source table with a non-existing partition location should succeed *** FAILED *** (94 milliseconds)
   file:/C:projectsspark%09arget%09mpspark-4b52e7de-e3aa-42fd-95d4-6d4d58d1d95d did not equal file:///C:/projects/spark/target/tmp/spark-4b52e7de-e3aa-42fd-95d4-6d4d58d1d95d (DDLSuite.scala:1910)
   ...

 - read data from a data source table which has a non-existing location should succeed *** FAILED *** (93 milliseconds)
   file:/C:projectsspark%09arget%09mpspark-f8c281e2-08c2-4f73-abbf-f3865b702c34 did not equal file:///C:/projects/spark/target/tmp/spark-f8c281e2-08c2-4f73-abbf-f3865b702c34 (DDLSuite.scala:1937)
   ...

 - read data from a data source table with non-existing partition location should succeed *** FAILED *** (110 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - create datasource table with a non-existing location *** FAILED *** (94 milliseconds)
   file:/C:projectsspark%09arget%09mpspark-387316ae-070c-4e78-9b78-19ebf7b29ec8 did not equal file:///C:/projects/spark/target/tmp/spark-387316ae-070c-4e78-9b78-19ebf7b29ec8 (DDLSuite.scala:1982)
   ...

 - CTAS for external data source table with a non-existing location *** FAILED *** (16 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - CTAS for external data source table with a existed location *** FAILED *** (15 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - data source table:partition column name containing a b *** FAILED *** (125 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - data source table:partition column name containing a:b *** FAILED *** (143 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - data source table:partition column name containing a%b *** FAILED *** (109 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - data source table:partition column name containing a,b *** FAILED *** (109 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - location uri contains a b for datasource table *** FAILED *** (94 milliseconds)
   file:/C:projectsspark%09arget%09mpspark-5739cda9-b702-4e14-932c-42e8c4174480a%20b did not equal file:///C:/projects/spark/target/tmp/spark-5739cda9-b702-4e14-932c-42e8c4174480/a%20b (DDLSuite.scala:2084)
   ...

 - location uri contains a:b for datasource table *** FAILED *** (78 milliseconds)
   file:/C:projectsspark%09arget%09mpspark-9bdd227c-840f-4f08-b7c5-4036638f098da:b did not equal file:///C:/projects/spark/target/tmp/spark-9bdd227c-840f-4f08-b7c5-4036638f098d/a:b (DDLSuite.scala:2084)
   ...

 - location uri contains a%b for datasource table *** FAILED *** (78 milliseconds)
   file:/C:projectsspark%09arget%09mpspark-62bb5f1d-fa20-460a-b534-cb2e172a3640a%25b did not equal file:///C:/projects/spark/target/tmp/spark-62bb5f1d-fa20-460a-b534-cb2e172a3640/a%25b (DDLSuite.scala:2084)
   ...

 - location uri contains a b for database *** FAILED *** (16 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - location uri contains a:b for database *** FAILED *** (15 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - location uri contains a%b for database *** FAILED *** (0 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...
```

```
org.apache.spark.sql.hive.execution.HiveDDLSuite:
 - create hive table with a non-existing location *** FAILED *** (16 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - CTAS for external hive table with a non-existing location *** FAILED *** (16 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - CTAS for external hive table with a existed location *** FAILED *** (16 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - partition column name of parquet table containing a b *** FAILED *** (156 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - partition column name of parquet table containing a:b *** FAILED *** (94 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - partition column name of parquet table containing a%b *** FAILED *** (125 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - partition column name of parquet table containing a,b *** FAILED *** (110 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...

 - partition column name of hive table containing a b *** FAILED *** (15 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - partition column name of hive table containing a:b *** FAILED *** (16 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - partition column name of hive table containing a%b *** FAILED *** (16 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - partition column name of hive table containing a,b *** FAILED *** (0 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - hive table: location uri contains a b *** FAILED *** (0 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - hive table: location uri contains a:b *** FAILED *** (0 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...

 - hive table: location uri contains a%b *** FAILED *** (0 milliseconds)
   org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
   ...
```

```
org.apache.spark.sql.sources.PathOptionSuite:
 - path option also exist for write path *** FAILED *** (94 milliseconds)
   file:/C:projectsspark%09arget%09mpspark-2870b281-7ac0-43d6-b6b6-134e01ab6fdc did not equal file:///C:/projects/spark/target/tmp/spark-2870b281-7ac0-43d6-b6b6-134e01ab6fdc (PathOptionSuite.scala:98)
   ...
```

```
org.apache.spark.sql.CachedTableSuite:
 - SPARK-19765: UNCACHE TABLE should un-cache all cached plans that refer to this table *** FAILED *** (110 milliseconds)
   java.lang.IllegalArgumentException: Can not create a Path from an empty string
   ...
```

```
org.apache.spark.sql.execution.DataSourceScanExecRedactionSuite:
 - treeString is redacted *** FAILED *** (250 milliseconds)
   "file:/C:/projects/spark/target/tmp/spark-3ecc1fa4-3e76-489c-95f4-f0b0500eae28" did not contain "C:\projects\spark\target\tmp\spark-3ecc1fa4-3e76-489c-95f4-f0b0500eae28" (DataSourceScanExecRedactionSuite.scala:46)
   ...
```

## How was this patch tested?

Tested via AppVeyor for each and checked it passed once each. These should be retested via AppVeyor in this PR.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17987 from HyukjinKwon/windows-20170515.

## What changes were proposed in this pull request?

Deleted generated JARs archive after distribution to HDFS

## How was this patch tested?

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

Author: Lior Regev <lioregev@gmail.com>

Closes #17986 from liorregev/master.

## What changes were proposed in this pull request?

Expose numPartitions (expert) param of PySpark FPGrowth.

## How was this patch tested?

+ [x] Pass all unit tests.

Author: Yan Facai (颜发才) <facai.yan@gmail.com>

Closes #18058 from facaiy/ENH/pyspark_fpg_add_num_partition.

## What changes were proposed in this pull request?
Follow-up for #17218, some minor fix for PySpark ```FPGrowth```.

## How was this patch tested?
Existing UT.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #18089 from yanboliang/spark-19281.

## What changes were proposed in this pull request?

Currently the whole block is fetched into memory(off heap by default) when shuffle-read. A block is defined by (shuffleId, mapId, reduceId). Thus it can be large when skew situations. If OOM happens during shuffle read, job will be killed and users will be notified to "Consider boosting spark.yarn.executor.memoryOverhead". Adjusting parameter and allocating more memory can resolve the OOM. However the approach is not perfectly suitable for production environment, especially for data warehouse.
Using Spark SQL as data engine in warehouse, users hope to have a unified parameter(e.g. memory) but less resource wasted(resource is allocated but not used). The hope is strong especially when migrating data engine to Spark from another one(e.g. Hive). Tuning the parameter for thousands of SQLs one by one is very time consuming.
It's not always easy to predict skew situations, when happen, it make sense to fetch remote blocks to disk for shuffle-read, rather than kill the job because of OOM.

In this pr, I propose to fetch big blocks to disk(which is also mentioned in SPARK-3019):

1. Track average size and also the outliers(which are larger than 2*avgSize) in MapStatus;
2. Request memory from `MemoryManager` before fetch blocks and release the memory to `MemoryManager` when `ManagedBuffer` is released.
3. Fetch remote blocks to disk when failing acquiring memory from `MemoryManager`, otherwise fetch to memory.

This is an improvement for memory control when shuffle blocks and help to avoid OOM in scenarios like below:
1. Single huge block;
2. Sizes of many blocks are underestimated in `MapStatus` and the actual footprint of blocks is much larger than the estimated.

## How was this patch tested?
Added unit test in `MapStatusSuite` and `ShuffleBlockFetcherIteratorSuite`.

Author: jinxing <jinxing6042@126.com>

Closes #16989 from jinxing64/SPARK-19659.

## What changes were proposed in this pull request?

Currently, when a task is calling spill() but it receives a killing request from driver (e.g., speculative task), the `TaskMemoryManager` will throw an `OOM` exception.  And we don't catch `Fatal` exception when a error caused by `Thread.interrupt`. So for `ClosedByInterruptException`, we should throw `RuntimeException` instead of `OutOfMemoryError`.

https://issues.apache.org/jira/browse/SPARK-20250?jql=project%20%3D%20SPARK

## How was this patch tested?

Existing unit tests.

Author: Xianyang Liu <xianyang.liu@intel.com>

Closes #18090 from ConeyLiu/SPARK-20250.

## What changes were proposed in this pull request?

This is a follow-up to #18073. Taking a safer approach to shutdown the pool to prevent possible issue. Also using `ThreadUtils.newForkJoinPool` instead to set a better thread name.

## How was this patch tested?

Manually test.

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

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

Closes #18100 from viirya/SPARK-20848-followup.

## What changes were proposed in this pull request?
1.    add  instructions of  'cast'  function When using 'show functions'  and 'desc function cast'
       command in spark-sql
2.    Modify the  instructions of functions，such as
     boolean，tinyint，smallint，int，bigint，float，double，decimal，date，timestamp，binary，string

## How was this patch tested?
Before modification：
spark-sql>desc function boolean;
Function: boolean
Class: org.apache.spark.sql.catalyst.expressions.Cast
Usage: boolean(expr AS type) - Casts the value `expr` to the target data type `type`.

After modification：
spark-sql> desc function boolean;
Function: boolean
Class: org.apache.spark.sql.catalyst.expressions.Cast
Usage: boolean(expr) - Casts the value `expr` to the target data type `boolean`.

spark-sql> desc function cast
Function: cast
Class: org.apache.spark.sql.catalyst.expressions.Cast
Usage: cast(expr AS type) - Casts the value `expr` to the target data type `type`.

Author: liuxian <liu.xian3@zte.com.cn>

Closes #17698 from 10110346/wip_lx_0418.

[SPARK-16202][SQL][DOC] Follow-up to Correct The Description of CreatableRelationProvider's createRelation

## What changes were proposed in this pull request?

Follow-up to SPARK-16202:

1. Remove the duplication of the meaning of `SaveMode` (as one was in fact missing that had proven that the duplication may be incomplete in the future again)

2. Use standard scaladoc tags

/cc gatorsmile rxin yhuai (as they were involved previously)

## How was this patch tested?

local build

Author: Jacek Laskowski <jacek@japila.pl>

Closes #18026 from jaceklaskowski/CreatableRelationProvider-SPARK-16202.

## What changes were proposed in this pull request?

A one-liner change in `ShuffleExchange.nodeName` to cover the case when `coordinator` is `null`, so that the match expression is exhaustive.

Please refer to [SPARK-20872](https://issues.apache.org/jira/browse/SPARK-20872) for a description of the symptoms.
TL;DR is that inspecting a `ShuffleExchange` (directly or transitively) on the Executor side can hit a case where the `coordinator` field of a `ShuffleExchange` is null, and thus will trigger a `MatchError` in `ShuffleExchange.nodeName()`'s inexhaustive match expression.

Also changed two other match conditions in `ShuffleExchange` on the `coordinator` field to be consistent.

## How was this patch tested?

Manually tested this change with a case where the `coordinator` is null to make sure `ShuffleExchange.nodeName` doesn't throw a `MatchError` any more.

Author: Kris Mok <kris.mok@databricks.com>

Closes #18095 from rednaxelafx/shuffleexchange-nodename.

The DAGScheduler was sending a "stage submitted" event before it properly
updated the event's information. This meant that a listener (e.g. the
even logging listener) could record wrong information about the event.

This change sets the stage's submission time before the event is submitted,
when there are tasks to be executed in the stage.

Tested with existing unit tests.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #17925 from vanzin/SPARK-20205.