## What changes were proposed in this pull request?

- Remove Scala 2.10 build profiles and support
- Replace some 2.10 support in scripts with commented placeholders for 2.12 later
- Remove deprecated API calls from 2.10 support
- Remove usages of deprecated context bounds where possible
- Remove Scala 2.10 workarounds like ScalaReflectionLock
- Other minor Scala warning fixes

## How was this patch tested?

Existing tests

Author: Sean Owen <sowen@cloudera.com>

Closes #17150 from srowen/SPARK-19810.

## What changes were proposed in this pull request?

`ClassLoader` will preferentially load class from `parent`. Only when `parent` is null or the load failed, that it will call the overridden `findClass` function. To avoid the potential issue caused by loading class using inappropriate class loader, we should set the `parent` of `ClassLoader` to null, so that we can fully control which class loader is used.

This is take over of #17074,  the primary author of this PR is taroplus .

Should close #17074 after this PR get merged.

## How was this patch tested?

Add test case in `ExecutorClassLoaderSuite`.

Author: Kohki Nishio <taroplus@me.com>
Author: Xingbo Jiang <xingbo.jiang@databricks.com>

Closes #18614 from jiangxb1987/executor_classloader.

## What changes were proposed in this pull request?

Currently, `RowDataSourceScanExec` and `FileSourceScanExec` rely on a "metadata" string map to implement equality comparison, since the RDDs they depend on cannot be directly compared. This has resulted in a number of correctness bugs around exchange reuse, e.g. SPARK-17673 and SPARK-16818.

To make these comparisons less brittle, we should refactor these classes to compare constructor parameters directly instead of relying on the metadata map.

This PR refactors `RowDataSourceScanExec`, `FileSourceScanExec` will be fixed in the follow-up PR.

## How was this patch tested?

existing tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #18600 from cloud-fan/minor.

[SPARK-18619][ML] Make QuantileDiscretizer/Bucketizer/StringIndexer/RFormula inherit from HasHandleInvalid

## What changes were proposed in this pull request?
1, HasHandleInvaild support override
2, Make QuantileDiscretizer/Bucketizer/StringIndexer/RFormula inherit from HasHandleInvalid

## How was this patch tested?
existing tests

[JIRA](https://issues.apache.org/jira/browse/SPARK-18619)

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #18582 from zhengruifeng/heritate_HasHandleInvalid.

## What changes were proposed in this pull request?
 Add  SQL function - RIGHT && LEFT, same as MySQL:
https://dev.mysql.com/doc/refman/5.7/en/string-functions.html#function_left
https://dev.mysql.com/doc/refman/5.7/en/string-functions.html#function_right

## How was this patch tested?
unit test

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

Closes #18228 from 10110346/lx-wip-0607.

## What changes were proposed in this pull request?

Many ML/MLLIB algorithms use native BLAS (like Intel MKL, ATLAS, OpenBLAS) to improvement the performance.
Many popular Native BLAS, like Intel MKL, OpenBLAS, use multi-threading technology, which will conflict with Spark.  Spark should provide options to disable multi-threading of Native BLAS.

https://github.com/xianyi/OpenBLAS/wiki/faq#multi-threaded
https://software.intel.com/en-us/articles/recommended-settings-for-calling-intel-mkl-routines-from-multi-threaded-applications

## How was this patch tested?
The existing UT.

Author: Peng Meng <peng.meng@intel.com>

Closes #18551 from mpjlu/optimzeBLAS.

### What changes were proposed in this pull request?
Hive 1.2.2 release is available. Below is the list of bugs fixed in 1.2.2

https://issues.apache.org/jira/secure/ReleaseNote.jspa?version=12332952&styleName=Text&projectId=12310843

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

Author: Xiao Li <gatorsmile@gmail.com>

Closes #18063 from gatorsmile/upgradeHiveClientTo1.2.2.

[SPARK-21370][SS] Add test for state reliability when one read-only state store aborts after read-write state store commits

## What changes were proposed in this pull request?

During Streaming Aggregation, we have two StateStores per task, one used as read-only in
`StateStoreRestoreExec`, and one read-write used in `StateStoreSaveExec`. `StateStore.abort`
will be called for these StateStores if they haven't committed their results. We need to
make sure that `abort` in read-only store after a `commit` in the read-write store doesn't
accidentally lead to the deletion of state.

This PR adds a test for this condition.

## How was this patch tested?

This PR adds a test.

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #18603 from brkyvz/ss-test.

## What changes were proposed in this pull request?

Adding the default UncaughtExceptionHandler to the Worker.

## How was this patch tested?

I verified it manually, when any of the worker thread gets uncaught exceptions then the default UncaughtExceptionHandler will handle those exceptions.

Author: Devaraj K <devaraj@apache.org>

Closes #18357 from devaraj-kavali/SPARK-21146.

[https://issues.apache.org/jira/browse/SPARK-21382](https://issues.apache.org/jira/browse/SPARK-21382)
There should be "Note that support for Scala 2.10 is deprecated as of Spark 2.1.0 and may be removed in Spark 2.3.0",right?

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

Closes #18606 from liu-zhaokun/new07120923.

## What changes were proposed in this pull request?
Hive interprets regular expression, e.g., `(a)?+.+` in query specification. This PR enables spark to support this feature when hive.support.quoted.identifiers is set to true.

## How was this patch tested?

- Add unittests in SQLQuerySuite.scala
- Run spark-shell tested the original failed query:
scala> hc.sql("SELECT `(a|b)?+.+` from test1").collect.foreach(println)

Author: Jane Wang <janewang@fb.com>

Closes #18023 from janewangfb/support_select_regex.

### What changes were proposed in this pull request?
This PR is to implement UDF0. `UDF0` is needed when users need to implement a JAVA UDF with no argument.

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

Author: gatorsmile <gatorsmile@gmail.com>

Closes #18598 from gatorsmile/udf0.

Currently the code monitoring the launch of the client AM uses the value of
spark.yarn.report.interval as the interval for polling the RM; if someone
has that value to a really large interval, it would take that long to detect
that the client AM has started, which is not expected.

Instead, have a separate config for the interval to use when the client AM is
starting. The other config is still used in cluster mode, and to detect the
status of the client AM after it is already running.

Tested by running client and cluster mode apps with a modified value of
spark.yarn.report.interval, verifying client AM launch is detected before
that interval elapses.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #18380 from vanzin/SPARK-16019.

## What changes were proposed in this pull request?

This PR deals with four points as below:

- Reuse existing DDL parser APIs rather than reimplementing within PySpark

- Support DDL formatted string, `field type, field type`.

- Support case-insensitivity for parsing.

- Support nested data types as below:

  **Before**
  ```
  >>> spark.createDataFrame([[[1]]], "struct<a: struct<b: int>>").show()
  ...
  ValueError: The strcut field string format is: 'field_name:field_type', but got: a: struct<b: int>
  ```

  ```
  >>> spark.createDataFrame([[[1]]], "a: struct<b: int>").show()
  ...
  ValueError: The strcut field string format is: 'field_name:field_type', but got: a: struct<b: int>
  ```

  ```
  >>> spark.createDataFrame([[1]], "a int").show()
  ...
  ValueError: Could not parse datatype: a int
  ```

  **After**
  ```
  >>> spark.createDataFrame([[[1]]], "struct<a: struct<b: int>>").show()
  +---+
  |  a|
  +---+
  |[1]|
  +---+
  ```

  ```
  >>> spark.createDataFrame([[[1]]], "a: struct<b: int>").show()
  +---+
  |  a|
  +---+
  |[1]|
  +---+
  ```

  ```
  >>> spark.createDataFrame([[1]], "a int").show()
  +---+
  |  a|
  +---+
  |  1|
  +---+
  ```

## How was this patch tested?

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18590 from HyukjinKwon/deduplicate-python-ddl.

## What changes were proposed in this pull request?

Add sql test for window functions, also remove uncecessary test cases in `WindowQuerySuite`.

## How was this patch tested?

Added `window.sql` and the corresponding output file.

Author: Xingbo Jiang <xingbo.jiang@databricks.com>

Closes #18591 from jiangxb1987/window.

## What changes were proposed in this pull request?

This PR proposes to remove `NumberFormat.parse` use to disallow a case of partially parsed data. For example,

```
scala> spark.read.schema("a DOUBLE").option("mode", "FAILFAST").csv(Seq("10u12").toDS).show()
+----+
|   a|
+----+
|10.0|
+----+
```

## How was this patch tested?

Unit tests added in `UnivocityParserSuite` and `CSVSuite`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18532 from HyukjinKwon/SPARK-21263.

(Link to Jira: https://issues.apache.org/jira/browse/SPARK-20331)

## What changes were proposed in this pull request?

Spark 2.1 introduced scalable support for Hive tables with huge numbers of partitions. Key to leveraging this support is the ability to prune unnecessary table partitions to answer queries. Spark supports a subset of the class of partition pruning predicates that the Hive metastore supports. If a user writes a query with a partition pruning predicate that is *not* supported by Spark, Spark falls back to loading all partitions and pruning client-side. We want to broaden Spark's current partition pruning predicate pushdown capabilities.

One of the key missing capabilities is support for disjunctions. For example, for a table partitioned by date, writing a query with a predicate like

    date = 20161011 or date = 20161014

will result in Spark fetching all partitions. For a table partitioned by date and hour, querying a range of hours across dates can be quite difficult to accomplish without fetching all partition metadata.

The current partition pruning support supports only comparisons against literals. We can expand that to foldable expressions by evaluating them at planning time.

We can also implement support for the "IN" comparison by expanding it to a sequence of "OR"s.

## How was this patch tested?

The `HiveClientSuite` and `VersionsSuite` were refactored and simplified to make Hive client-based, version-specific testing more modular and conceptually simpler. There are now two Hive test suites: `HiveClientSuite` and `HivePartitionFilteringSuite`. These test suites have a single-argument constructor taking a `version` parameter. As such, these test suites cannot be run by themselves. Instead, they have been bundled into "aggregation" test suites which run each suite for each Hive client version. These aggregation suites are called `HiveClientSuites` and `HivePartitionFilteringSuites`. The `VersionsSuite` and `HiveClientSuite` have been refactored into each of these aggregation suites, respectively.

`HiveClientSuite` and `HivePartitionFilteringSuite` subclass a new abstract class, `HiveVersionSuite`. `HiveVersionSuite` collects functionality related to testing a single Hive version and overrides relevant test suite methods to display version-specific information.

A new trait, `HiveClientVersions`, has been added with a sequence of Hive test versions.

Author: Michael Allman <michael@videoamp.com>

Closes #17633 from mallman/spark-20331-enhanced_partition_pruning_pushdown.

## What changes were proposed in this pull request?

This PR proposes to simply ignore the results in examples that are timezone-dependent in `unix_timestamp` and `from_unixtime`.

```
Failed example:
    time_df.select(unix_timestamp('dt', 'yyyy-MM-dd').alias('unix_time')).collect()
Expected:
    [Row(unix_time=1428476400)]
Got:unix_timestamp
    [Row(unix_time=1428418800)]
```

```
Failed example:
    time_df.select(from_unixtime('unix_time').alias('ts')).collect()
Expected:
    [Row(ts=u'2015-04-08 00:00:00')]
Got:
    [Row(ts=u'2015-04-08 16:00:00')]
```

## How was this patch tested?

Manually tested and `./run-tests --modules pyspark-sql`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18597 from HyukjinKwon/SPARK-20456.

[SPARK-21315][SQL] Skip some spill files when generateIterator(startIndex) in ExternalAppendOnlyUnsafeRowArray.

## What changes were proposed in this pull request?

In current code, it is expensive to use `UnboundedFollowingWindowFunctionFrame`, because it is iterating from the start to lower bound every time calling `write` method. When traverse the iterator, it's possible to skip some spilled files thus to save some time.

## How was this patch tested?

Added unit test

Did a small test for benchmark:

Put 2000200 rows into `UnsafeExternalSorter`-- 2 spill files(each contains 1000000 rows) and inMemSorter contains 200 rows.
Move the iterator forward to index=2000001.

*With this change*:
`getIterator(2000001)`, it will cost almost 0ms~1ms;
*Without this change*:
`for(int i=0; i<2000001; i++)geIterator().loadNext()`, it will cost 300ms.

Author: jinxing <jinxing6042@126.com>

Closes #18541 from jinxing64/SPARK-21315.

## What changes were proposed in this pull request?

Remove all usages of Scala Tuple2 from common/network-* projects. Otherwise, Yarn users cannot use `spark.reducer.maxReqSizeShuffleToMem`.

## How was this patch tested?

Jenkins.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #18593 from zsxwing/SPARK-21369.

### What changes were proposed in this pull request?
Users get a very confusing error when users specify a wrong number of parameters.
```Scala
    val df = spark.emptyDataFrame
    spark.udf.register("foo", (_: String).length)
    df.selectExpr("foo(2, 3, 4)")
```
```
org.apache.spark.sql.UDFSuite$$anonfun$9$$anonfun$apply$mcV$sp$12 cannot be cast to scala.Function3
java.lang.ClassCastException: org.apache.spark.sql.UDFSuite$$anonfun$9$$anonfun$apply$mcV$sp$12 cannot be cast to scala.Function3
	at org.apache.spark.sql.catalyst.expressions.ScalaUDF.<init>(ScalaUDF.scala:109)
```

This PR is to capture the exception and issue an error message that is consistent with what we did for built-in functions. After the fix, the error message is improved to
```
Invalid number of arguments for function foo; line 1 pos 0
org.apache.spark.sql.AnalysisException: Invalid number of arguments for function foo; line 1 pos 0
	at org.apache.spark.sql.catalyst.analysis.SimpleFunctionRegistry.lookupFunction(FunctionRegistry.scala:119)
```

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

Author: gatorsmile <gatorsmile@gmail.com>

Closes #18574 from gatorsmile/statsCheck.

## What changes were proposed in this pull request?
This pr added `unionByName` in `DataSet`.
Here is how to use:
```
val df1 = Seq((1, 2, 3)).toDF("col0", "col1", "col2")
val df2 = Seq((4, 5, 6)).toDF("col1", "col2", "col0")
df1.unionByName(df2).show

// output:
// +----+----+----+
// |col0|col1|col2|
// +----+----+----+
// |   1|   2|   3|
// |   6|   4|   5|
// +----+----+----+
```

## How was this patch tested?
Added tests in `DataFrameSuite`.

Author: Takeshi Yamamuro <yamamuro@apache.org>

Closes #18300 from maropu/SPARK-21043-2.

## What changes were proposed in this pull request?
At example of repartitionAndSortWithinPartitions at rdd.py, third argument should be True or False.
I proposed fix of example code.

## How was this patch tested?
* I rename test_repartitionAndSortWithinPartitions to test_repartitionAndSortWIthinPartitions_asc to specify boolean argument.
* I added test_repartitionAndSortWithinPartitions_desc to test False pattern at third argument.

(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: chie8842 <chie8842@gmail.com>

Closes #18586 from chie8842/SPARK-21358.

## What changes were proposed in this pull request?
Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`.  This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process.  The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame.  Data types except complex, date, timestamp, and decimal  are currently supported, otherwise an `UnsupportedOperation` exception is thrown.

Additions to Spark include a Scala package private method `Dataset.toArrowPayload` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served.  A package private class/object `ArrowConverters` that provide data type mappings and conversion routines.  In Python, a private method `DataFrame._collectAsArrow` is added to collect Arrow payloads and a SQLConf "spark.sql.execution.arrow.enable" can be used in `toPandas()` to enable using Arrow (uses the old conversion by default).

## How was this patch tested?
Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types.  The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data.  This will ensure that the schema and data has been converted correctly.

Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow.  A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas.

Author: Bryan Cutler <cutlerb@gmail.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: Li Jin <li.jin@twosigma.com>
Author: Wes McKinney <wes.mckinney@twosigma.com>

Closes #18459 from BryanCutler/toPandas_with_arrow-SPARK-13534.

## What changes were proposed in this pull request?

This PR supports schema in a DDL formatted string for `from_json` in R/Python and `dapply` and `gapply` in R, which are commonly used and/or consistent with Scala APIs.

Additionally, this PR exposes `structType` in R to allow working around in other possible corner cases.

**Python**

`from_json`

```python
from pyspark.sql.functions import from_json

data = [(1, '''{"a": 1}''')]
df = spark.createDataFrame(data, ("key", "value"))
df.select(from_json(df.value, "a INT").alias("json")).show()
```

**R**

`from_json`

```R
df <- sql("SELECT named_struct('name', 'Bob') as people")
df <- mutate(df, people_json = to_json(df$people))
head(select(df, from_json(df$people_json, "name STRING")))
```

`structType.character`

```R
structType("a STRING, b INT")
```

`dapply`

```R
dapply(createDataFrame(list(list(1.0)), "a"), function(x) {x}, "a DOUBLE")
```

`gapply`

```R
gapply(createDataFrame(list(list(1.0)), "a"), "a", function(key, x) { x }, "a DOUBLE")
```

## How was this patch tested?

Doc tests for `from_json` in Python and unit tests `test_sparkSQL.R` in R.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18498 from HyukjinKwon/SPARK-21266.

## What changes were proposed in this pull request?

Updating numOutputRows metric was missing from one return path of LeftAnti SortMergeJoin.

## How was this patch tested?

Non-zero output rows manually seen in metrics.

Author: Juliusz Sompolski <julek@databricks.com>

Closes #18494 from juliuszsompolski/SPARK-21272.

## What changes were proposed in this pull request?

When `RetryingBlockFetcher` retries fetching blocks. There could be two `DownloadCallback`s download the same content to the same target file. It could cause `ShuffleBlockFetcherIterator` reading a partial result.

This pr proposes to create and delete the tmp files in `OneForOneBlockFetcher`

Author: jinxing <jinxing6042@126.com>
Author: Shixiong Zhu <zsxwing@gmail.com>

Closes #18565 from jinxing64/SPARK-21342.

## What changes were proposed in this pull request?
This pr made it more consistent to handle column name duplication. In the current master, error handling is different when hitting column name duplication:
```
// json
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("""{"a":1, "a":1}"""""").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("json").schema(schema).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be: a#12, a#13.;
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)

scala> spark.read.format("json").load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Duplicate column(s) : "a" found, cannot save to JSON format;
  at org.apache.spark.sql.execution.datasources.json.JsonDataSource.checkConstraints(JsonDataSource.scala:81)
  at org.apache.spark.sql.execution.datasources.json.JsonDataSource.inferSchema(JsonDataSource.scala:63)
  at org.apache.spark.sql.execution.datasources.json.JsonFileFormat.inferSchema(JsonFileFormat.scala:57)
  at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:176)
  at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:176)

// csv
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("a,a", "1,1").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("csv").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be: a#41, a#42.;
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:152)

// If `inferSchema` is true, a CSV format is duplicate-safe (See SPARK-16896)
scala> spark.read.format("csv").option("header", true).load("/tmp/data").show
+---+---+
| a0| a1|
+---+---+
|  1|  1|
+---+---+

// parquet
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq((1, 1)).toDF("a", "b").coalesce(1).write.mode("overwrite").parquet("/tmp/data")
scala> spark.read.format("parquet").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be: a#110, a#111.;
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:152)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
```
When this patch applied, the results change to;
```

// json
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("""{"a":1, "a":1}"""""").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("json").schema(schema).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
  at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
  at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
  at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
  at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)

scala> spark.read.format("json").load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
  at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
  at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
  at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
  at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
  at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
  at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:156)

// csv
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("a,a", "1,1").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("csv").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
  at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
  at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
  at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
  at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
  at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)

scala> spark.read.format("csv").option("header", true).load("/tmp/data").show
+---+---+
| a0| a1|
+---+---+
|  1|  1|
+---+---+

// parquet
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq((1, 1)).toDF("a", "b").coalesce(1).write.mode("overwrite").parquet("/tmp/data")
scala> spark.read.format("parquet").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
  at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
  at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
  at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
  at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
```

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

Author: Takeshi Yamamuro <yamamuro@apache.org>

Closes #17758 from maropu/SPARK-20460.

## What changes were proposed in this pull request?

Since this document became obsolete, we had better remove this for Apache Spark 2.3.0. The original document is removed via SPARK-12735 on January 2016, and currently it's just redirection page. The only reference in Apache Spark website will go directly to the destination in https://github.com/apache/spark-website/pull/54.

## How was this patch tested?

N/A. This is a removal of documentation.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #18578 from dongjoon-hyun/SPARK-REMOVE-EC2.

## What changes were proposed in this pull request?

There's a race condition in the current TaskSetManager where a failed task is added for retry (addPendingTask), and can asynchronously be assigned to an executor *prior* to the blacklist state (updateBlacklistForFailedTask), the result is the task might re-execute on the same executor.  This is particularly problematic if the executor is shutting down since the retry task immediately becomes a lost task (ExecutorLostFailure).  Another side effect is that the actual failure reason gets obscured by the retry task which never actually executed.  There are sample logs showing the issue in the https://issues.apache.org/jira/browse/SPARK-21219

The fix is to change the ordering of the addPendingTask and updatingBlackListForFailedTask calls in TaskSetManager.handleFailedTask

## How was this patch tested?

Implemented a unit test that verifies the task is black listed before it is added to the pending task.  Ran the unit test without the fix and it fails.  Ran the unit test with the fix and it passes.

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

Author: Eric Vandenberg <ericvandenberg@fb.com>

Closes #18427 from ericvandenbergfb/blacklistFix.

## What changes were proposed in this pull request?

Some code cleanup and adding comments to make the code more readable. Changed the way to generate result rows, to be more clear.

## How was this patch tested?

existing tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #18570 from cloud-fan/summary.

## What changes were proposed in this pull request?

Spark provides several ways to set configurations, either from configuration file, or from `spark-submit` command line options, or programmatically through `SparkConf` class. It may confuses beginners why some configurations set through `SparkConf` cannot take affect. So here add some docs to address this problems and let beginners know how to correctly set configurations.

## How was this patch tested?

N/A

Author: jerryshao <sshao@hortonworks.com>

Closes #18552 from jerryshao/improve-doc.

[SPARK-18016][SQL][FOLLOWUP] merge declareAddedFunctions, initNestedClasses and declareNestedClasses

## What changes were proposed in this pull request?

These 3 methods have to be used together, so it makes more sense to merge them into one method and then the caller side only need to call one method.

## How was this patch tested?

existing tests.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #18579 from cloud-fan/minor.

## What changes were proposed in this pull request?

This is a retry for #18320. This PR was reverted due to unexpected test failures with -10 error code.

I was unable to reproduce in MacOS, CentOS and Ubuntu but only in Jenkins. So, the tests proceeded to verify this and revert the past try here - https://github.com/apache/spark/pull/18456

This new approach was tested in https://github.com/apache/spark/pull/18463.

**Test results**:

- With the part of suspicious change in the past try (https://github.com/apache/spark/pull/18463/commits/466325d3fd353668583f3bde38ae490d9db0b189)

  Tests ran 4 times and 2 times passed and 2 time failed.

- Without the part of suspicious change in the past try (https://github.com/apache/spark/pull/18463/commits/466325d3fd353668583f3bde38ae490d9db0b189)

  Tests ran 5 times and they all passed.

- With this new approach (https://github.com/apache/spark/pull/18463/commits/0a7589c09f53dfc2094497d8d3e59d6407569417)

  Tests ran 5 times and they all passed.

It looks the cause is as below (see https://github.com/apache/spark/pull/18463/commits/466325d3fd353668583f3bde38ae490d9db0b189):

```diff
+ exitCode <- 1
...
+   data <- parallel:::readChild(child)
+   if (is.raw(data)) {
+     if (unserialize(data) == exitCode) {
      ...
+     }
+   }

...

- parallel:::mcexit(0L)
+ parallel:::mcexit(0L, send = exitCode)
```

Two possibilities I think

 - `parallel:::mcexit(.. , send = exitCode)`

   https://stat.ethz.ch/R-manual/R-devel/library/parallel/html/mcfork.html

   > It sends send to the master (unless NULL) and then shuts down the child process.

   However, it looks possible that the parent attemps to terminate the child right after getting our custom exit code. So, the child gets terminated between "send" and "shuts down", failing to exit properly.

 - A bug between `parallel:::mcexit(..., send = ...)` and `parallel:::readChild`.

**Proposal**:

To resolve this, I simply decided to avoid both possibilities with this new approach here (https://github.com/apache/spark/pull/18465/commits/9ff89a7859cb9f427fc774f33c3521c7d962b723). To support this idea, I explained with some quotation of the documentation as below:

https://stat.ethz.ch/R-manual/R-devel/library/parallel/html/mcfork.html

> `readChild` and `readChildren` return a raw vector with a "pid" attribute if data were available, an integer vector of length one with the process ID if a child terminated or `NULL` if the child no longer exists (no children at all for `readChildren`).

`readChild` returns "an integer vector of length one with the process ID if a child terminated" so we can check if it is `integer` and the same selected "process ID". I believe this makes sure that the children are exited.

In case that children happen to send any data manually to parent (which is why we introduced the suspicious part of the change (https://github.com/apache/spark/pull/18463/commits/466325d3fd353668583f3bde38ae490d9db0b189)), this should be raw bytes and will be discarded (and then will try to read the next and check if it is `integer` in the next loop).

## How was this patch tested?

Manual tests and Jenkins tests.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18465 from HyukjinKwon/SPARK-21093-retry-1.

## What changes were proposed in this pull request?
Since we do not set active sessions when parsing the plan, we are unable to correctly use SQLConf.get to find the correct active session. Since https://github.com/apache/spark/pull/18531 breaks the build, I plan to revert it at first.

## How was this patch tested?
The existing test cases

Author: Xiao Li <gatorsmile@gmail.com>

Closes #18568 from gatorsmile/revert18531.

## What changes were proposed in this pull request?

In current code, reducer can break the old shuffle service when `spark.reducer.maxReqSizeShuffleToMem` is enabled. Let's refine document.

Author: jinxing <jinxing6042@126.com>

Closes #18566 from jinxing64/SPARK-21343.

This makes sures that listeners get updated task information; otherwise it's
possible to write incomplete task information into event logs, for example,
making the information in a replayed UI inconsistent with the original
application.

Added a new unit test to try to detect the problem, but it's not guaranteed
to fail since it's a race; but it fails pretty reliably for me without the
scheduler changes.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #18393 from vanzin/SPARK-20342.try2.

## What changes were proposed in this pull request?

We should be able to store zero size and row count after analyzing empty table.

This pr also enhances the test cases for re-analyzing tables.

## How was this patch tested?

Added a new test case and enhanced some test cases.

Author: Zhenhua Wang <wangzhenhua@huawei.com>

Closes #18292 from wzhfy/analyzeNewColumn.

## What changes were proposed in this pull request?

`SparkSessionBuilderSuite` should clean up stopped sessions. Otherwise, it leaves behind some stopped `SparkContext`s interfereing with other test suites using `ShardSQLContext`.

Recently, master branch fails consequtively.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/

## How was this patch tested?

Pass the Jenkins with a updated suite.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #18567 from dongjoon-hyun/SPARK-SESSION.

[SPARK-20609][MLLIB][TEST] manually cleared 'spark.local.dir' before/after a test in ALSCleanerSuite

## What changes were proposed in this pull request?

This PR is similar to #17869.
Once` 'spark.local.dir'` is set. Unless this is manually cleared before/after a test. it could return the same directory even if this property is configured.
and add before/after for each likewise in ALSCleanerSuite.

## How was this patch tested?
existing test.

Author: caoxuewen <cao.xuewen@zte.com.cn>

Closes #18537 from heary-cao/ALSCleanerSuite.