## What changes were proposed in this pull request?

Currently PipedRDD internally uses PrintWriter to write data to the stdin of the piped process, which by default uses a BufferedWriter of buffer size 8k. In our experiment, we have seen that 8k buffer size is too small and the job spends significant amount of CPU time in system calls to copy the data. We should have a way to configure the buffer size for the writer.

## How was this patch tested?
Ran PipedRDDSuite tests.

Author: Sital Kedia <skedia@fb.com>

Closes #12309 from sitalkedia/bufferedPipedRDD.

#### What changes were proposed in this pull request?
This PR is to address a few existing issues in `EXPLAIN`:
- The `EXPLAIN` options `LOGICAL | FORMATTED | EXTENDED | CODEGEN` should not be 0 or more match. It should 0 or one match. Parser does not allow users to use more than one option in a single command.
- The option `LOGICAL` is not supported. Issue an exception when users specify this option in the command.
- The output of `EXPLAIN ` contains a weird empty line when the output of analyzed plan is empty. We should remove it. For example:
  ```
  == Parsed Logical Plan ==
  CreateTable CatalogTable(`t`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io.  HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(col,int,true,None)),List(),List(),List(),-1,,1462725171656,-1,Map(),None,None,None), false

  == Analyzed Logical Plan ==

  CreateTable CatalogTable(`t`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io.  HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(col,int,true,None)),List(),List(),List(),-1,,1462725171656,-1,Map(),None,None,None), false

  == Optimized Logical Plan ==
  CreateTable CatalogTable(`t`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io.  HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(col,int,true,None)),List(),List(),List(),-1,,1462725171656,-1,Map(),None,None,None), false
  ...
  ```

#### How was this patch tested?
Added and modified a few test cases

Author: gatorsmile <gatorsmile@gmail.com>

Closes #12991 from gatorsmile/explainCreateTable.

#### What changes were proposed in this pull request?
In Hive Metastore, dropping default database is not allowed. However, in `InMemoryCatalog`, this is allowed.

This PR is to disallow users to drop default database.

#### How was this patch tested?
Previously, we already have a test case in HiveDDLSuite. Now, we also add the same one in DDLSuite

Author: gatorsmile <gatorsmile@gmail.com>

Closes #12962 from gatorsmile/dropDefaultDB.

## What changes were proposed in this pull request?
Our case sensitivity support is different from what ANSI SQL standards support. Postgres' behavior is that if an identifier is quoted, then it is treated as case sensitive; otherwise it is folded to lowercase. We will likely need to revisit this in the future and change our behavior. For now, the safest change to do for Spark 2.0 is to make the case sensitive option internal and discourage users from turning it on, effectively making Spark always case insensitive.

## How was this patch tested?
N/A - a small config documentation change.

Author: Reynold Xin <rxin@databricks.com>

Closes #13011 from rxin/SPARK-15229.

## What changes were proposed in this pull request?

Before:
```
scala> spark.catalog.listDatabases.show()
+--------------------+-----------+-----------+
|                name|description|locationUri|
+--------------------+-----------+-----------+
|Database[name='de...|
|Database[name='my...|
|Database[name='so...|
+--------------------+-----------+-----------+
```

After:
```
+-------+--------------------+--------------------+
|   name|         description|         locationUri|
+-------+--------------------+--------------------+
|default|Default Hive data...|file:/user/hive/w...|
|  my_db|  This is a database|file:/Users/andre...|
|some_db|                    |file:/private/var...|
+-------+--------------------+--------------------+
```

## How was this patch tested?

New test in `CatalogSuite`

Author: Andrew Or <andrew@databricks.com>

Closes #13015 from andrewor14/catalog-show.

## What changes were proposed in this pull request?
The issue is that when the user provides the path option with uppercase "PATH" key, `options` contains `PATH` key and will get into the non-external case in the following code in `createDataSourceTables.scala`, where a new key "path" is created with a default path.
```
val optionsWithPath =
      if (!options.contains("path")) {
        isExternal = false
        options + ("path" -> sessionState.catalog.defaultTablePath(tableIdent))
      } else {
        options
      }
```
So before creating hive table, serdeInfo.parameters will contain both "PATH" and "path" keys and different directories. and Hive table's dataLocation contains the value of "path".

The fix in this PR is to convert `options` in the code above to `CaseInsensitiveMap` before checking for containing "path" key.

## How was this patch tested?
A testcase is added

Author: xin Wu <xinwu@us.ibm.com>

Closes #12804 from xwu0226/SPARK-15025.

## What changes were proposed in this pull request?

This patch fixes an escaping bug in the Web UI's event timeline that caused Javascript errors when displaying timeline entries whose descriptions include single quotes.

The original bug can be reproduced by running

```scala
sc.setJobDescription("double quote: \" ")
sc.parallelize(1 to 10).count()

sc.setJobDescription("single quote: ' ")
sc.parallelize(1 to 10).count()
```

and then browsing to the driver UI. Previously, this resulted in an "Uncaught SyntaxError" because the single quote from the description was not escaped and ended up closing a Javascript string literal too early.

The fix implemented here is to change the relevant Javascript to define its string literals using double-quotes. Our escaping logic already properly escapes double quotes in the description, so this is safe to do.

## How was this patch tested?

Tested manually in `spark-shell` using the following cases:

```scala
sc.setJobDescription("double quote: \" ")
sc.parallelize(1 to 10).count()

sc.setJobDescription("single quote: ' ")
sc.parallelize(1 to 10).count()

sc.setJobDescription("ampersand: &")
sc.parallelize(1 to 10).count()

sc.setJobDescription("newline: \n text after newline ")
sc.parallelize(1 to 10).count()

sc.setJobDescription("carriage return: \r text after return ")
sc.parallelize(1 to 10).count()
```

/cc sarutak for review.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #12995 from JoshRosen/SPARK-15209.

This patch improves the performance of `InferSchema.compatibleType` and `inferField`. The net result of this patch is a 6x speedup in local benchmarks running against cached data with a massive nested schema.

The key idea is to remove unnecessary sorting in `compatibleType`'s `StructType` merging code. This code takes two structs, merges the fields with matching names, and copies over the unique fields, producing a new schema which is the union of the two structs' schemas. Previously, this code performed a very inefficient `groupBy()` to match up fields with the same name, but this is unnecessary because `inferField` already sorts structs' fields by name: since both lists of fields are sorted, we can simply merge them in a single pass.

This patch also speeds up the existing field sorting in `inferField`: the old sorting code allocated unnecessary intermediate collections, while the new code uses mutable collects and performs in-place sorting.

I rewrote inefficient `equals()` implementations in `StructType` and `Metadata`, significantly reducing object allocations in those methods.

Finally, I replaced a `treeAggregate` call with `fold`: I doubt that `treeAggregate` will benefit us very much because the schemas would have to be enormous to realize large savings in network traffic. Since most schemas are probably fairly small in serialized form, they should typically fit within a direct task result and therefore can be incrementally merged at the driver as individual tasks finish. This change eliminates an entire (short) scheduler stage.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #12750 from JoshRosen/schema-inference-speedups.

When we parse `CREATE TABLE USING`, we should build a `CreateTableUsing` plan with the `managedIfNoPath` set to true. Then we will add default table path to options when write it to hive.

new test in `SQLQuerySuite`

Author: Wenchen Fan <wenchen@databricks.com>

Closes #12949 from cloud-fan/bug.

## What changes were proposed in this pull request?

Removed blockTransferService and sparkFilesDir from SparkEnv since they're rarely used and don't need to be in stored in the env. Edited their few usages to accommodate the change.

## How was this patch tested?

ran dev/run-tests locally

Author: Alex Bozarth <ajbozart@us.ibm.com>

Closes #12970 from ajbozarth/spark10653.

## What changes were proposed in this pull request?

This also simplifies the code being moved.

## How was this patch tested?

Existing tests.

Author: Andrew Or <andrew@databricks.com>

Closes #12941 from andrewor14/move-code.

add DeveloperApi annotation for `AbstractDataType` `MapType` `UserDefinedType`

local build

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12982 from zhengruifeng/types_devapi.

## What changes were proposed in this pull request?
Add hyperlink to "running application" and "completed application", so user can jump to application table directly, In my environment, I set up 1000+ works and it's painful to scroll down to skip worker list.

## How was this patch tested?
manual tested

(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
![sceenshot](https://cloud.githubusercontent.com/assets/13216322/15105718/97e06768-15f6-11e6-809d-3574046751a9.png)

Author: mwws <wei.mao@intel.com>

Closes #12997 from mwws/SPARK_UI.

Enhance the exception message when `checkpointLocation` is not set, previously the message is:

```
java.util.NoSuchElementException: None.get
  at scala.None$.get(Option.scala:347)
  at scala.None$.get(Option.scala:345)
  at org.apache.spark.sql.DataFrameWriter$$anonfun$8.apply(DataFrameWriter.scala:338)
  at org.apache.spark.sql.DataFrameWriter$$anonfun$8.apply(DataFrameWriter.scala:338)
  at scala.collection.MapLike$class.getOrElse(MapLike.scala:128)
  at scala.collection.AbstractMap.getOrElse(Map.scala:59)
  at org.apache.spark.sql.DataFrameWriter.startStream(DataFrameWriter.scala:337)
  at org.apache.spark.sql.DataFrameWriter.startStream(DataFrameWriter.scala:277)
  ... 48 elided
```

This is not so meaningful, so changing to make it more specific.

Local verified.

Author: jerryshao <sshao@hortonworks.com>

Closes #12998 from jerryshao/improve-exception-message.

## What changes were proposed in this pull request?

Look for MaxPermSize arguments anywhere in an arg, to account for quoted args. See JIRA for discussion.

## How was this patch tested?

Jenkins tests

Author: Sean Owen <sowen@cloudera.com>

Closes #12985 from srowen/SPARK-15067.

`Encoder`'s doc mentions `sqlContext.implicits._`. We should use `sparkSession.implicits._` instead now.

Only doc update.

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

Closes #13002 from viirya/encoder-doc.

## What changes were proposed in this pull request?

The configuration setting `spark.executor.logs.rolling.size.maxBytes` was changed to `spark.executor.logs.rolling.maxSize` in 1.4 or so.

This commit fixes a remaining reference to the old name in the documentation.

Also the description for `spark.executor.logs.rolling.maxSize` was edited to clearly state that the unit for the size is bytes.

## How was this patch tested?

no tests

Author: Philipp Hoffmann <mail@philipphoffmann.de>

Closes #13001 from philipphoffmann/patch-3.

This PR removes `sqlContext` in examples. Actual usage was all replaced in https://github.com/apache/spark/pull/12809 but there are some in comments.

Manual style checking.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #13006 from HyukjinKwon/minor-docs.

## What changes were proposed in this pull request?

This is a follow-up of PR #12844. It makes the newly updated `DescribeTableCommand` to support data sources tables.

## How was this patch tested?

A test case is added to check `DESC [EXTENDED | FORMATTED] <table>` output.

Author: Cheng Lian <lian@databricks.com>

Closes #12934 from liancheng/spark-14127-desc-table-follow-up.

#### What changes were proposed in this pull request?
As Hive and the major RDBMS behave, the built-in functions are not allowed to drop. In the current implementation, users can drop the built-in functions. However, after dropping the built-in functions, users are unable to add them back.

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

Author: gatorsmile <gatorsmile@gmail.com>

Closes #12975 from gatorsmile/dropBuildInFunction.

## What changes were proposed in this pull request?

following operations have file system operation now:

1. CREATE DATABASE: create a dir
2. DROP DATABASE: delete the dir
3. CREATE TABLE: create a dir
4. DROP TABLE: delete the dir
5. RENAME TABLE: rename the dir
6. CREATE PARTITIONS: create a dir
7. RENAME PARTITIONS: rename the dir
8. DROP PARTITIONS: drop the dir

## How was this patch tested?

new tests in `ExternalCatalogSuite`

Author: Wenchen Fan <wenchen@databricks.com>

Closes #12871 from cloud-fan/catalog.

## What changes were proposed in this pull request?
* Since Spark has supported native csv reader, it does not necessary to use the third party ```spark-csv``` in ```examples/src/main/r/data-manipulation.R```. Meanwhile, remove all ```spark-csv``` usage in SparkR.
* Running R applications through ```sparkR``` is not supported as of Spark 2.0, so we change to use ```./bin/spark-submit``` to run the example.

## How was this patch tested?
Offline test.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #13005 from yanboliang/r-df-examples.

## What changes were proposed in this pull request?

This detects a relation's partitioning and adds checks to the analyzer.
If an InsertIntoTable node has no partitioning, it is replaced by the
relation's partition scheme and input columns are correctly adjusted,
placing the partition columns at the end in partition order. If an
InsertIntoTable node has partitioning, it is checked against the table's
reported partitions.

These changes required adding a PartitionedRelation trait to the catalog
interface because Hive's MetastoreRelation doesn't extend
CatalogRelation.

This commit also includes a fix to InsertIntoTable's resolved logic,
which now detects that all expected columns are present, including
dynamic partition columns. Previously, the number of expected columns
was not checked and resolved was true if there were missing columns.

## How was this patch tested?

This adds new tests to the InsertIntoTableSuite that are fixed by this PR.

Author: Ryan Blue <blue@apache.org>

Closes #12239 from rdblue/SPARK-14459-detect-hive-partitioning.

It's a minor bug in test case. `val testDir = null` will keep be `null` as it's immutable, so in finally block, nothing will be cleaned. Another `testDir` variable created in try block is only visible in try block.

## How was this patch tested?
Run existing test case and passed.

Author: mwws <wei.mao@intel.com>

Closes #12999 from mwws/SPARK_MINOR.

[SPARK-15172][ML] Explicitly tell user initial coefficients is ignored when size mismatch happened in LogisticRegression

## What changes were proposed in this pull request?
Explicitly tell user initial coefficients is ignored if its size doesn't match expected size in LogisticRegression

## How was this patch tested?
local build

Author: dding3 <dingding@dingding-ubuntu.sh.intel.com>

Closes #12948 from dding3/master.

## What changes were proposed in this pull request?

PyDoc links in ml are in non-standard format. Switch to standard sphinx link format for better formatted documentation. Also add a note about default value in one place. Copy some extended docs from scala for GBT

## How was this patch tested?

Built docs locally.

Author: Holden Karau <holden@us.ibm.com>

Closes #12918 from holdenk/SPARK-15137-linkify-pyspark-ml-classification.

## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-14814
fix a java compatibility function in mllib DecisionTreeModel. As synced in jira, other compatibility issues don't need fixes.

## How was this patch tested?

existing ut

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #12971 from hhbyyh/javacompatibility.

## What changes were proposed in this pull request?

We need to use `requiredSchema` in `LibSVMRelation` to project the fetch required columns when loading data from this data source. Otherwise, when users try to select `features` column, it will cause failure.

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

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

Closes #12986 from viirya/fix-libsvmrelation.

#### What changes were proposed in this pull request?
Currently, if we rename a temp table `Tab1` to another existent temp table `Tab2`. `Tab2` will be silently removed. This PR is to detect it and issue an exception message.

In addition, this PR also detects another issue in the rename table command. When the destination table identifier does have database name, we should not ignore them. That might mean users could rename a regular table.

#### How was this patch tested?
Added two related test cases

Author: gatorsmile <gatorsmile@gmail.com>

Closes #12959 from gatorsmile/rewriteTable.

[SPARK-15185][SQL] InMemoryCatalog: Silent Removal of an Existent Table/Function/Partitions by Rename

#### What changes were proposed in this pull request?
So far, in the implementation of InMemoryCatalog, we do not check if the new/destination table/function/partition exists or not. Thus, we just silently remove the existent table/function/partition.

This PR is to detect them and issue an appropriate exception.

#### How was this patch tested?
Added the related test cases. They also verify if HiveExternalCatalog also detects these errors.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #12960 from gatorsmile/renameInMemoryCatalog.

[SPARK-12479][SPARKR] sparkR collect on GroupedData throws R error "missing value where TRUE/FALSE needed"

## What changes were proposed in this pull request?

This PR is a workaround for NA handling in hash code computation.

This PR is on behalf of paulomagalhaes whose PR is https://github.com/apache/spark/pull/10436

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

Author: Sun Rui <sunrui2016@gmail.com>
Author: ray <ray@rays-MacBook-Air.local>

Closes #12976 from sun-rui/SPARK-12479.

## What changes were proposed in this pull request?
Remove LazyFileRegion instead use netty's DefaultFileRegion, since It was created so that we didn't create a file descriptor before having to send the file.

## How was this patch tested?
Existing tests

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #12977 from techaddict/SPARK-15178.

## What changes were proposed in this pull request?
Fixed some minor errors found when reviewing feature.ml user guide

## How was this patch tested?
built docs locally

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #12940 from BryanCutler/feature.ml-doc_fixes-DOCS-MINOR.

Cleans up ALS examples by removing unnecessary casts to double for `rating` and `prediction` columns, since `RegressionEvaluator` now supports `Double` & `Float` input types.

## How was this patch tested?

Manual compile and run with `run-example ml.ALSExample` and `spark-submit examples/src/main/python/ml/als_example.py`.

Author: Nick Pentreath <nickp@za.ibm.com>

Closes #12892 from MLnick/als-examples-cleanup.

## What changes were proposed in this pull request?
The official TPC-DS 41 query currently fails because it contains a scalar subquery with a disjunctive correlated predicate (the correlated predicates were nested in ORs). This makes the `Analyzer` pull out the entire predicate which is wrong and causes the following (correct) analysis exception: `The correlated scalar subquery can only contain equality predicates`

This PR fixes this by first simplifing (or normalizing) the correlated predicates before pulling them out of the subquery.

## How was this patch tested?
Manual testing on TPC-DS 41, and added a test to SubquerySuite.

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

Closes #12954 from hvanhovell/SPARK-15122.

## What changes were proposed in this pull request?

Currently when we create an alias against a TypedColumn from user-defined Aggregator(for example: agg(aggSum.toColumn as "a")), spark is using the alias' function from Column( as), the alias function will return a column contains a TypedAggregateExpression, which is unresolved because the inputDeserializer is not defined. Later the aggregator function (agg) will inject the inputDeserializer back to the TypedAggregateExpression, but only if the aggregate columns are TypedColumn, in the above case, the TypedAggregateExpression will remain unresolved because it is under column and caused the
problem reported by this jira [15051](https://issues.apache.org/jira/browse/SPARK-15051?jql=project%20%3D%20SPARK).

This PR propose to create an alias function for TypedColumn,  it will return a TypedColumn. It is using the similar code path  as Column's alia function.

For the spark build in aggregate function, like max, it is working with alias, for example

val df1 = Seq(1 -> "a", 2 -> "b", 3 -> "b").toDF("i", "j")
checkAnswer(df1.agg(max("j") as "b"), Row(3) :: Nil)

Thanks for comments.

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)

Add test cases in DatasetAggregatorSuite.scala
run the sql related queries against this patch.

Author: Kevin Yu <qyu@us.ibm.com>

Closes #12893 from kevinyu98/spark-15051.

## What changes were proposed in this pull request?
Remove the Comment, since it not longer applies. see the discussion here(https://github.com/apache/spark/pull/12865#discussion-diff-61946906)

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #12953 from techaddict/SPARK-15087-FOLLOW-UP.

The main issue we are trying to solve is the memory bloat of the Driver when tasks request the map output statuses.  This means with a large number of tasks you either need a huge amount of memory on Driver or you have to repartition to smaller number.  This makes it really difficult to run over say 50000 tasks.

The main issues that cause the memory bloat are:
1) no flow control on sending the map output status responses.  We serialize the map status output  and then hand off to netty to send.  netty is sending asynchronously and it can't send them fast enough to keep up with incoming requests so we end up with lots of copies of the serialized map output statuses sitting there and this causes huge bloat when you have 10's of thousands of tasks and map output status is in the 10's of MB.
2) When initial reduce tasks are started up, they all request the map output statuses from the Driver. These requests are handled by multiple threads in parallel so even though we check to see if we have a cached version, initially when we don't have a cached version yet, many of initial requests can all end up serializing the exact same map output statuses.

This patch does a couple of things:
- When the map output status size is over a threshold (default 512K) then it uses broadcast to send the map statuses.  This means we no longer serialize a large map output status and thus we don't have issues with memory bloat.  the messages sizes are now in the 300-400 byte range and the map status output are broadcast. If its under the threadshold it sends it as before, the message contains the DIRECT indicator now.
- synchronize the incoming requests to allow one thread to cache the serialized output and broadcast the map output status  that can then be used by everyone else.  This ensures we don't create multiple broadcast variables when we don't need to.  To ensure this happens I added a second thread pool which the Dispatcher hands the requests to so that those threads can block without blocking the main dispatcher threads (which would cause things like heartbeats and such not to come through)

Note that some of design and code was contributed by mridulm

## How was this patch tested?

Unit tests and a lot of manually testing.
Ran with akka and netty rpc. Ran with both dynamic allocation on and off.

one of the large jobs I used to test this was a join of 15TB of data.  it had 200,000 map tasks, and  20,000 reduce tasks. Executors ranged from 200 to 2000.  This job ran successfully with 5GB of memory on the driver with these changes. Without these changes I was using 20GB and only had 500 reduce tasks.  The job has 50mb of serialized map output statuses and took roughly the same amount of time for the executors to get the map output statuses as before.

Ran a variety of other jobs, from large wordcounts to small ones not using broadcasts.

Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com>

Closes #12113 from tgravescs/SPARK-1239.

[SPARK-14997][SQL] Fixed FileCatalog to return correct set of files when there is no partitioning scheme in the given paths

## What changes were proposed in this pull request?
Lets says there are json files in the following directories structure
```
xyz/file0.json
xyz/subdir1/file1.json
xyz/subdir2/file2.json
xyz/subdir1/subsubdir1/file3.json
```
`sqlContext.read.json("xyz")` should read only file0.json according to behavior in Spark 1.6.1. However in current master, all the 4 files are read.

The fix is to make FileCatalog return only the children files of the given path if there is not partitioning detected (instead of all the recursive list of files).

Closes #12774

## How was this patch tested?

unit tests

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

Closes #12856 from tdas/SPARK-14997.

## What changes were proposed in this pull request?

This PR continues the work from #11871 with the following changes:
* load English stopwords as default
* covert stopwords to list in Python
* update some tests and doc

## How was this patch tested?

Unit tests.

Closes #11871

cc: burakkose srowen

Author: Burak Köse <burakks41@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Burak KOSE <burakks41@gmail.com>

Closes #12843 from mengxr/SPARK-14050.