## What changes were proposed in this pull request?

Currently Spark SQL doesn't support sorting columns in descending order. However, the parser accepts the syntax and silently drops sorting directions. This PR fixes this by throwing an exception if `DESC` is specified as sorting direction of a sorting column.

## How was this patch tested?

A test case is added to test the invalid sorting order by checking exception message.

Author: Cheng Lian <lian@databricks.com>

Closes #12759 from liancheng/spark-14981.

## What changes were proposed in this pull request?
We recently inlined Hive's thrift server code in SPARK-15004. This patch removes the code related to zookeeper service discovery, Tez, and Hive on Spark, since they are irrelevant.

## How was this patch tested?
N/A - removing dead code

Author: Reynold Xin <rxin@databricks.com>

Closes #12780 from rxin/SPARK-15004.

This test always fail with sbt's hadoop 2.3 and 2.4 tests. Let'e disable it for now and investigate the problem.

Author: Yin Huai <yhuai@databricks.com>

Closes #12783 from yhuai/SPARK-15011-ignore.

## What changes were proposed in this pull request?

pyspark.ml API for LDA
* LDA, LDAModel, LocalLDAModel, DistributedLDAModel
* includes persistence

This replaces [https://github.com/apache/spark/pull/10242]

## How was this patch tested?

* doc test for LDA, including Param setters
* unit test for persistence

Author: Joseph K. Bradley <joseph@databricks.com>
Author: Jeff Zhang <zjffdu@apache.org>

Closes #12723 from jkbradley/zjffdu-SPARK-11940.

## What changes were proposed in this pull request?

Deprecated model field in LinearRegressionSummary

Removed unnecessary Since annotations

## How was this patch tested?

Existing tests

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

Closes #12763 from jkbradley/lr-summary-api.

SparkR ```glm``` and ```kmeans``` model persistence.

Unit tests.

Author: Yanbo Liang <ybliang8@gmail.com>
Author: Gayathri Murali <gayathri.m.softie@gmail.com>

Closes #12778 from yanboliang/spark-14311.
Closes #12680
Closes #12683

## What changes were proposed in this pull request?

The `catalog` and `conf` APIs were exposed in `SparkSession` in #12713 and #12669. This patch adds those to the python API.

## How was this patch tested?

Python tests.

Author: Andrew Or <andrew@databricks.com>

Closes #12765 from andrewor14/python-spark-session-more.

## What changes were proposed in this pull request?

This PR copy the thrift-server from hive-service-1.2 (including  TCLIService.thrift and generated Java source code) into sql/hive-thriftserver, so we can do further cleanup and improvements.

## How was this patch tested?

Existing tests.

Author: Davies Liu <davies@databricks.com>

Closes #12764 from davies/thrift_server.

## What changes were proposed in this pull request?

Add log instrumentation for parameters:
rank, numUserBlocks, numItemBlocks, implicitPrefs, alpha,
userCol, itemCol, ratingCol, predictionCol, maxIter,
regParam, nonnegative, checkpointInterval, seed

Add log instrumentation for numUserFeatures and numItemFeatures

## How was this patch tested?

Manual test: Set breakpoint in intellij and run def testALS(). Single step debugging and check the log method is called.

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

Closes #12560 from wangmiao1981/log.

## What changes were proposed in this pull request?

This PR removes duplicate implementation of compute in LogisticGradient class

## How was this patch tested?

unit tests

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

Closes #12747 from dding3/master.

## What changes were proposed in this pull request?
In the past, genjavadoc had issues with package private members which led the spark project to use a forked version. This issue has been fixed upstream (typesafehub/genjavadoc#70) and a release is available for scala versions 2.10, 2.11 **and 2.12**, hence a forked version for spark is no longer necessary.
This pull request updates the build configuration to use the newest upstream genjavadoc.

## How was this patch tested?
The build was run `sbt unidoc`. During the process javadoc emits some errors on the generated java stubs, however these errors were also present before the upgrade. Furthermore, the produced html is fine.

Author: Jakob Odersky <jakob@odersky.com>

Closes #12707 from jodersky/SPARK-14511-genjavadoc.

## What changes were proposed in this pull request?
This patch removes executionHive from HiveSessionState and HiveSharedState.

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

Author: Reynold Xin <rxin@databricks.com>
Author: Yin Huai <yhuai@databricks.com>

Closes #12770 from rxin/SPARK-14994.

## What changes were proposed in this pull request?

This PR adds support for easily running and benchmarking a set of common TPCDS queries locally in SparkSQL.

## How was this patch tested?

N/A

Author: Sameer Agarwal <sameer@databricks.com>

Closes #12771 from sameeragarwal/tpcds-2.

#### What changes were proposed in this pull request?
Replaces a logical `Except` operator with a `Left-anti Join` operator. This way, we can take advantage of all the benefits of join implementations (e.g. managed memory, code generation, broadcast joins).
```SQL
  SELECT a1, a2 FROM Tab1 EXCEPT SELECT b1, b2 FROM Tab2
  ==>  SELECT DISTINCT a1, a2 FROM Tab1 LEFT ANTI JOIN Tab2 ON a1<=>b1 AND a2<=>b2
```
 Note:
 1. This rule is only applicable to EXCEPT DISTINCT. Do not use it for EXCEPT ALL.
 2. This rule has to be done after de-duplicating the attributes; otherwise, the enerated
    join conditions will be incorrect.

This PR also corrects the existing behavior in Spark. Before this PR, the behavior is like
```SQL
  test("except") {
    val df_left = Seq(1, 2, 2, 3, 3, 4).toDF("id")
    val df_right = Seq(1, 3).toDF("id")

    checkAnswer(
      df_left.except(df_right),
      Row(2) :: Row(2) :: Row(4) :: Nil
    )
  }
```
After this PR, the result is corrected. We strictly follow the SQL compliance of `Except Distinct`.

#### How was this patch tested?
Modified and added a few test cases to verify the optimization rule and the results of operators.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #12736 from gatorsmile/exceptByAntiJoin.

## What changes were proposed in this pull request?

Handle case where number of predictions is less than label set, k in nDCG computation

## How was this patch tested?

New unit test; existing tests

Author: Sean Owen <sowen@cloudera.com>

Closes #12756 from srowen/SPARK-14886.

## What changes were proposed in this pull request?
Minor typo fixes

## How was this patch tested?
local build

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12755 from zhengruifeng/fix_doc_dataset.

## What changes were proposed in this pull request?
According to the [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829), deprecate API of LogisticRegression and LinearRegression using SGD

## How was this patch tested?
manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12596 from zhengruifeng/deprecate_sgd.

## What changes were proposed in this pull request?

This PR adds a new function in SparkR called `sparkLapply(list, function)`. This function implements a distributed version of `lapply` using Spark as a backend.

TODO:
 - [x] check documentation
 - [ ] check tests

Trivial example in SparkR:

```R
sparkLapply(1:5, function(x) { 2 * x })
```

Output:

```
[[1]]
[1] 2

[[2]]
[1] 4

[[3]]
[1] 6

[[4]]
[1] 8

[[5]]
[1] 10
```

Here is a slightly more complex example to perform distributed training of multiple models. Under the hood, Spark broadcasts the dataset.

```R
library("MASS")
data(menarche)
families <- c("gaussian", "poisson")
train <- function(family){glm(Menarche ~ Age  , family=family, data=menarche)}
results <- sparkLapply(families, train)
```

## How was this patch tested?

This PR was tested in SparkR. I am unfamiliar with R and SparkR, so any feedback on style, testing, etc. will be much appreciated.

cc falaki davies

Author: Timothy Hunter <timhunter@databricks.com>

Closes #12426 from thunterdb/7264.

## What changes were proposed in this pull request?
This patch removes HiveNativeCommand, so we can continue to remove the dependency on Hive. This pull request also removes the ability to generate golden result file using Hive.

## How was this patch tested?
Updated tests to reflect this.

Author: Reynold Xin <rxin@databricks.com>

Closes #12769 from rxin/SPARK-14991.

## What changes were proposed in this pull request?

`AccumulatorContext` is not thread-safe, that's why all of its methods are synchronized. However, there is one exception: the `AccumulatorContext.originals`. `NewAccumulator` use it to check if it's registered, which is wrong as it's not synchronized.

This PR mark `AccumulatorContext.originals` as `private` and now all access to `AccumulatorContext` is synchronized.

## How was this patch tested?

I verified it locally. To be safe, we can let jenkins test it many times to make sure this problem is gone.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #12773 from cloud-fan/debug.

This reverts commit dae538a4.

## What changes were proposed in this pull request?

<copy form JIRA>

Currently if neither `spark.yarn.jars` nor `spark.yarn.archive` is set (by default), Spark on yarn code will upload all the jars in the folder separately into distributed cache, this is quite time consuming, and very verbose, instead of upload jars separately into distributed cache, here changes to zip all the jars first, and then put into distributed cache.

This will significantly improve the speed of starting time.

## How was this patch tested?

Unit test and local integrated test is done.

Verified with SparkPi both in spark cluster and client mode.

Author: jerryshao <sshao@hortonworks.com>

Closes #12597 from jerryshao/SPARK-14836.

## What changes were proposed in this pull request?

Updated Classifier, DecisionTreeClassifier, RandomForestClassifier, GBTClassifier to not require input column metadata.
* They first check for metadata.
* If numClasses is not specified in metadata, they identify the largest label value (up to a limit).

This functionality is implemented in a new Classifier.getNumClasses method.

Also
* Updated Classifier.extractLabeledPoints to (a) check label values and (b) include a second version which takes a numClasses value for validity checking.

## How was this patch tested?

* Unit tests in ClassifierSuite for helper methods
* Unit tests for DecisionTreeClassifier, RandomForestClassifier, GBTClassifier with toy datasets lacking label metadata

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

Closes #12663 from jkbradley/trees-no-metadata.

## What changes were proposed in this pull request?

This PR adds `since` tag into the matrix and vector classes in spark-mllib-local.

## How was this patch tested?

Scala-style checks passed.

Author: Pravin Gadakh <prgadakh@in.ibm.com>

Closes #12416 from pravingadakh/SPARK-14613.

## What changes were proposed in this pull request?

This PR adds Python APIs for:
 - `ContinuousQueryManager`
 - `ContinuousQueryException`

The `ContinuousQueryException` is a very basic wrapper, it doesn't provide the functionality that the Scala side provides, but it follows the same pattern for `AnalysisException`.

For `ContinuousQueryManager`, all APIs are provided except for registering listeners.

This PR also attempts to fix test flakiness by stopping all active streams just before tests.

## How was this patch tested?

Python Doc tests and unit tests

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #12673 from brkyvz/pyspark-cqm.

## What changes were proposed in this pull request?
support avgMetrics in CrossValidatorModel with Python
## How was this patch tested?
Doctest and `test_save_load` in `pyspark/ml/test.py`
[JIRA](https://issues.apache.org/jira/browse/SPARK-12810)

Author: Kai Jiang <jiangkai@gmail.com>

Closes #12464 from vectorijk/spark-12810.

[SPARK-14970][SQL] Prevent DataSource from enumerates all files in a directory if there is user specified schema

## What changes were proposed in this pull request?
The FileCatalog object gets created even if the user specifies schema, which means files in the directory is enumerated even thought its not necessary. For large directories this is very slow. User would want to specify schema in such scenarios of large dirs, and this defeats the purpose quite a bit.

## How was this patch tested?
Hard to test this with unit test.

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

Closes #12748 from tdas/SPARK-14970.

## What changes were proposed in this pull request?

jira: https://issues.apache.org/jira/browse/SPARK-14916
FreqItemset as the result of FPGrowth should have a more friendly toString(), to help users and developers.
sample:
{a, b}: 5
{x, y, z}: 4

## How was this patch tested?

existing unit tests.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #12698 from hhbyyh/freqtos.

## What changes were proposed in this pull request?

This splits GeneralizedLinearRegressionSummary into 2 summary types:
* GeneralizedLinearRegressionSummary, which does not store info from fitting (diagInvAtWA)
* GeneralizedLinearRegressionTrainingSummary, which is a subclass of GeneralizedLinearRegressionSummary and stores info from fitting

This also add a method evaluate() which can produce a GeneralizedLinearRegressionSummary on a new dataset.

The summary no longer provides the model itself as a public val.

Also:
* Fixes bug where GeneralizedLinearRegressionTrainingSummary was created with model, not summaryModel.
* Adds hasSummary method.
* Renames findSummaryModelAndPredictionCol -> getSummaryModel and simplifies that method.
* In summary, extract values from model immediately in case user later changes those (e.g., predictionCol).
* Pardon the style fixes; that is IntelliJ being obnoxious.

## How was this patch tested?

Existing unit tests + updated test for evaluate and hasSummary

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

Closes #12624 from jkbradley/model-summary-api.

## What changes were proposed in this pull request?

Fix to ScalaDoc for StructType.

## How was this patch tested?

Built locally.

Author: Gregory Hart <greg.hart@thinkbiganalytics.com>

Closes #12758 from freastro/hotfix/SPARK-14965.

## What changes were proposed in this pull request?

```
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.0.0-SNAPSHOT
      /_/

Using Python version 2.7.5 (default, Mar  9 2014 22:15:05)
SparkSession available as 'spark'.
>>> spark
<pyspark.sql.session.SparkSession object at 0x101f3bfd0>
>>> spark.sql("SHOW TABLES").show()
...
+---------+-----------+
|tableName|isTemporary|
+---------+-----------+
|      src|      false|
+---------+-----------+

>>> spark.range(1, 10, 2).show()
+---+
| id|
+---+
|  1|
|  3|
|  5|
|  7|
|  9|
+---+
```
**Note**: This API is NOT complete in its current state. In particular, for now I left out the `conf` and `catalog` APIs, which were added later in Scala. These will be added later before 2.0.

## How was this patch tested?

Python tests.

Author: Andrew Or <andrew@databricks.com>

Closes #12746 from andrewor14/python-spark-session.

https://issues.apache.org/jira/browse/SPARK-14935

In DistributedSuite, the "local-cluster format" test actually launches a bunch of clusters, but this doesn't seem necessary for what should just be a unit test of a regex. We should clean up the code so that this is testable without actually launching a cluster, which should buy us about 20 seconds per build.

Passed unit test on my local machine

Author: Xin Ren <iamshrek@126.com>

Closes #12744 from keypointt/SPARK-14935.

## What changes were proposed in this pull request?

Add simple clarification that Spark can be cross-built for other Scala versions.

## How was this patch tested?

Automated doc build

Author: Sean Owen <sowen@cloudera.com>

Closes #12757 from srowen/SPARK-14882.

This work is based on twinkle-sachdeva 's proposal. In parallel to such mechanism for AM failures, here add similar mechanism for executor failure tracking, this is useful for long running Spark service to mitigate the executor failure problems.

Please help to review, tgravescs sryza and vanzin

Author: jerryshao <sshao@hortonworks.com>

Closes #10241 from jerryshao/SPARK-6735.

Make the behavior of mutate more consistent with that in dplyr, besides support for replacing existing columns.
1. Throw error message when there are duplicated column names in the DataFrame being mutated.
2. when there are duplicated column names in specified columns by arguments, the last column of the same name takes effect.

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

Closes #10220 from sun-rui/SPARK-12235.

## What changes were proposed in this pull request?
This is a proposal to print the Spark Driver UI link when spark-shell is launched.

## How was this patch tested?
Launched spark-shell in local mode and cluster mode. Spark-shell console output included following line:
"Spark context Web UI available at <Spark web url>"

Author: Ergin Seyfe <eseyfe@fb.com>

Closes #12341 from seyfe/spark_console_display_webui_link.

## What changes were proposed in this pull request?

Currently we use `SQLUserDefinedType` annotation to register UDTs for user classes. However, by doing this, we add Spark dependency to user classes.

For some user classes, it is unnecessary to add such dependency that will increase deployment difficulty.

We should provide alternative approach to register UDTs for user classes without `SQLUserDefinedType` annotation.

## How was this patch tested?

`UserDefinedTypeSuite`

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

Closes #12259 from viirya/improve-sql-usertype.

## What changes were proposed in this pull request?

This PR introduces a new accumulator API  which is much simpler than before:

1. the type hierarchy is simplified, now we only have an `Accumulator` class
2. Combine `initialValue` and `zeroValue` concepts into just one concept: `zeroValue`
3. there in only one `register` method, the accumulator registration and cleanup registration are combined.
4. the `id`,`name` and `countFailedValues` are combined into an `AccumulatorMetadata`, and is provided during registration.

`SQLMetric` is a good example to show the simplicity of this new API.

What we break:

1. no `setValue` anymore. In the new API, the intermedia type can be different from the result type, it's very hard to implement a general `setValue`
2. accumulator can't be serialized before registered.

Problems need to be addressed in follow-ups:

1. with this new API, `AccumulatorInfo` doesn't make a lot of sense, the partial output is not partial updates, we need to expose the intermediate value.
2. `ExceptionFailure` should not carry the accumulator updates. Why do users care about accumulator updates for failed cases? It looks like we only use this feature to update the internal metrics, how about we sending a heartbeat to update internal metrics after the failure event?
3. the public event `SparkListenerTaskEnd` carries a `TaskMetrics`. Ideally this `TaskMetrics` don't need to carry external accumulators, as the only method of `TaskMetrics` that can access external accumulators is `private[spark]`. However, `SQLListener` use it to retrieve sql metrics.

## How was this patch tested?

existing tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #12612 from cloud-fan/acc.