This PR adds the `relativeError` param to PySpark's `QuantileDiscretizer` to match Scala.

Also cleaned up a duplication of `numBuckets` where the param is both a class and instance attribute (I removed the instance attr to match the style of params throughout `ml`).

Finally, cleaned up the docs for `QuantileDiscretizer` to reflect that it now uses `approxQuantile`.

## How was this patch tested?

A little doctest and built API docs locally to check HTML doc generation.

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

Closes #13228 from MLnick/SPARK-15442-py-relerror-param.

## What changes were proposed in this pull request?

in hive, `locate("aa", "aaa", 0)` would yield 0, `locate("aa", "aaa", 1)` would yield 1 and `locate("aa", "aaa", 2)` would yield 2, while in Spark, `locate("aa", "aaa", 0)` would yield 1,  `locate("aa", "aaa", 1)` would yield 2 and  `locate("aa", "aaa", 2)` would yield 0. This results from the different understanding of the third parameter in udf `locate`. It means the starting index and starts from 1, so when we use 0, the return would always be 0.

## How was this patch tested?

tested with modified `StringExpressionsSuite` and `StringFunctionsSuite`

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #13186 from adrian-wang/locate.

This reverts commit fa244e5a.

## What changes were proposed in this pull request?

This PR splits the generated code for ```SafeProjection.apply``` by using ```ctx.splitExpressions()```. This is because the large code body for ```NewInstance``` may grow beyond 64KB bytecode size for ```apply()``` method.

## How was this patch tested?

Added new tests

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

Closes #13243 from kiszk/SPARK-15285.

#### What changes were proposed in this pull request?
So far, the page Configuration in the official documentation does not have a section for Spark SQL.
http://spark.apache.org/docs/latest/configuration.html

For Spark users, the information and default values of these public configuration parameters are very useful. This PR is to add this missing section to the configuration.html.

rxin yhuai marmbrus

#### How was this patch tested?
Below is the generated webpage.
<img width="924" alt="screenshot 2016-05-23 11 35 57" src="https://cloud.githubusercontent.com/assets/11567269/15480492/b08fefc4-20da-11e6-9fa2-7cd5b699ed35.png">
<img width="914" alt="screenshot 2016-05-23 11 37 38" src="https://cloud.githubusercontent.com/assets/11567269/15480499/c5f9482e-20da-11e6-95ff-10821add1af4.png">
<img width="923" alt="screenshot 2016-05-23 11 36 11" src="https://cloud.githubusercontent.com/assets/11567269/15480506/cbd81644-20da-11e6-9d27-effb716b2fac.png">
<img width="920" alt="screenshot 2016-05-23 11 36 18" src="https://cloud.githubusercontent.com/assets/11567269/15480511/d013e332-20da-11e6-854a-cf8813c46f36.png">

Author: gatorsmile <gatorsmile@gmail.com>

Closes #13263 from gatorsmile/configurationSQL.

[SPARK-15464][ML][MLLIB][SQL][TESTS] Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code

## What changes were proposed in this pull request?

Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code.

## How was this patch tested?

Existing test.

Author: WeichenXu <WeichenXu123@outlook.com>

Closes #13242 from WeichenXu123/python_doctest_update_sparksession.

#### What changes were proposed in this pull request?
So far, when using In-Memory Catalog, we allow DDL operations for the tables. However, the corresponding DML operations are not supported for the tables that are neither temporary nor data source tables. For example,
```SQL
CREATE TABLE tabName(i INT, j STRING)
SELECT * FROM tabName
INSERT OVERWRITE TABLE tabName SELECT 1, 'a'
```
In the above example, before this PR fix, we will get very confusing exception messages for either `SELECT` or `INSERT`
```
org.apache.spark.sql.AnalysisException: unresolved operator 'SimpleCatalogRelation default, CatalogTable(`default`.`tbl`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(i,int,true,None), CatalogColumn(j,string,true,None)),List(),List(),List(),-1,,1463928681802,-1,Map(),None,None,None,List()), None;
```

This PR is to issue appropriate exceptions in this case. The message will be like
```
org.apache.spark.sql.AnalysisException: Please enable Hive support when operating non-temporary tables: `tbl`;
```
#### How was this patch tested?
Added a test case in `DDLSuite`.

Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>

Closes #13093 from gatorsmile/selectAfterCreate.

## What changes were proposed in this pull request?
Currently command `ADD FILE|JAR <filepath | jarpath>` is supported natively in SparkSQL. However, when this command is run, the file/jar is added to the resources that can not be looked up by `LIST FILE(s)|JAR(s)` command because the `LIST` command is passed to Hive command processor in Spark-SQL or simply not supported in Spark-shell. There is no way users can find out what files/jars are added to the spark context.
Refer to [Hive commands](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Cli)

This PR is to support following commands:
`LIST (FILE[s] [filepath ...] | JAR[s] [jarfile ...])`

### For example:
##### LIST FILE(s)
```
scala> spark.sql("add file hdfs://bdavm009.svl.ibm.com:8020/tmp/test.txt")
res1: org.apache.spark.sql.DataFrame = []
scala> spark.sql("add file hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt")
res2: org.apache.spark.sql.DataFrame = []

scala> spark.sql("list file hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt").show(false)
+----------------------------------------------+
|result                                        |
+----------------------------------------------+
|hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt|
+----------------------------------------------+

scala> spark.sql("list files").show(false)
+----------------------------------------------+
|result                                        |
+----------------------------------------------+
|hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt|
|hdfs://bdavm009.svl.ibm.com:8020/tmp/test.txt |
+----------------------------------------------+
```

##### LIST JAR(s)
```
scala> spark.sql("add jar /Users/xinwu/spark/core/src/test/resources/TestUDTF.jar")
res9: org.apache.spark.sql.DataFrame = [result: int]

scala> spark.sql("list jar TestUDTF.jar").show(false)
+---------------------------------------------+
|result                                       |
+---------------------------------------------+
|spark://192.168.1.234:50131/jars/TestUDTF.jar|
+---------------------------------------------+

scala> spark.sql("list jars").show(false)
+---------------------------------------------+
|result                                       |
+---------------------------------------------+
|spark://192.168.1.234:50131/jars/TestUDTF.jar|
+---------------------------------------------+
```
## How was this patch tested?
New test cases are added for Spark-SQL, Spark-Shell and SparkContext API code path.

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

Closes #13212 from xwu0226/list_command.

## What changes were proposed in this pull request?

This PR adds the description for running unit tests in Windows.

## How was this patch tested?

On a bare machine (Window 7, 32bits), this was manually built and tested.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #13217 from HyukjinKwon/minor-r-doc.

[SPARK-15315][SQL] Adding error check to  the CSV datasource writer for unsupported complex data types.

## What changes were proposed in this pull request?

Adds error handling to the CSV writer  for unsupported complex data types.  Currently garbage gets written to the output csv files if the data frame schema has complex data types.

## How was this patch tested?

Added new unit test case.

Author: sureshthalamati <suresh.thalamati@gmail.com>

Closes #13105 from sureshthalamati/csv_complex_types_SPARK-15315.

## What changes were proposed in this pull request?

Spark assumes that UDF functions are deterministic. This PR adds explicit notes about that.

## How was this patch tested?

It's only about docs.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13087 from dongjoon-hyun/SPARK-15282.

## What changes were proposed in this pull request?

The user may do something like:
```
CREATE TABLE my_tab ROW FORMAT SERDE 'anything' STORED AS PARQUET
CREATE TABLE my_tab ROW FORMAT SERDE 'anything' STORED AS ... SERDE 'myserde'
CREATE TABLE my_tab ROW FORMAT DELIMITED ... STORED AS ORC
CREATE TABLE my_tab ROW FORMAT DELIMITED ... STORED AS ... SERDE 'myserde'
```
None of these should be allowed because the SerDe's conflict. As of this patch:
- `ROW FORMAT DELIMITED` is only compatible with `TEXTFILE`
- `ROW FORMAT SERDE` is only compatible with `TEXTFILE`, `RCFILE` and `SEQUENCEFILE`

## How was this patch tested?

New tests in `DDLCommandSuite`.

Author: Andrew Or <andrew@databricks.com>

Closes #13068 from andrewor14/row-format-conflict.

## What changes were proposed in this pull request?

1. simplify the logic of deserializing option type.
2. simplify the logic of serializing array type, and remove silentSchemaFor
3. remove some unnecessary code.

## How was this patch tested?

existing tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #13250 from cloud-fan/encoder.

## What changes were proposed in this pull request?

Currently, we create an CSVWriter for every row, it's very expensive and memory hungry, took about 15 seconds to write out 1 mm rows (two columns).

This PR will write the rows in batch mode, create a CSVWriter for every 1k rows, which could write out 1 mm rows in about 1 seconds (15X faster).

## How was this patch tested?

Manually benchmark it.

Author: Davies Liu <davies@databricks.com>

Closes #13229 from davies/csv_writer.

## What changes were proposed in this pull request?

In order to prevent users from inadvertently writing queries with cartesian joins, this patch introduces a new conf `spark.sql.crossJoin.enabled` (set to `false` by default) that if not set, results in a `SparkException` if the query contains one or more cartesian products.

## How was this patch tested?

Added a test to verify the new behavior in `JoinSuite`. Additionally, `SQLQuerySuite` and `SQLMetricsSuite` were modified to explicitly enable cartesian products.

Author: Sameer Agarwal <sameer@databricks.com>

Closes #13209 from sameeragarwal/disallow-cartesian.

## What changes were proposed in this pull request?

When invalid date string like "2015-02-29 00:00:00" are cast as date or timestamp using spark sql, it used to not return null but another valid date (2015-03-01 in this case).
In this pr, invalid date string like "2016-02-29" and "2016-04-31" are returned as null when cast as date or timestamp.

## How was this patch tested?

Unit tests are added.

(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Author: wangyang <wangyang@haizhi.com>

Closes #13169 from wangyang1992/invalid_date.

## What changes were proposed in this pull request?
More than 100 chars in line.

## How was this patch tested?

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #13249 from techaddict/fix-1.

## What changes were proposed in this pull request?

Fix some typos while browsing the codes.

## How was this patch tested?

None and obvious.

Author: Bo Meng <mengbo@hotmail.com>
Author: bomeng <bmeng@us.ibm.com>

Closes #13246 from bomeng/typo.

## What changes were proposed in this pull request?

In `ListAccumulator` we create an unmodifiable view for underlying list. However, it doesn't prevent the underlying to be modified further. So as we access the unmodifiable list, the underlying list can be modified in the same time. It could cause `java.util.ConcurrentModificationException`. We can observe such exception in recent tests.

To fix it, we can copy a list of the underlying list and then create the unmodifiable view of this list instead.

## How was this patch tested?
The exception might be difficult to test. Existing tests should be passed.

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

Closes #13211 from viirya/fix-concurrentmodify.

## What changes were proposed in this pull request?

Incrementalizing plans of with multiple streaming aggregation is tricky and we dont have the necessary support for "delta" to implement correctly. So disabling the support for multiple streaming aggregations.

## How was this patch tested?
Additional unit tests

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

Closes #13210 from tdas/SPARK-15428.

## What changes were proposed in this pull request?
This patch simplifies the implementation of Range operator and make the explain string consistent between logical plan and physical plan. To do this, I changed RangeExec to embed a Range logical plan in it.

Before this patch (note that the logical Range and physical Range actually output different information):
```
== Optimized Logical Plan ==
Range 0, 100, 2, 2, [id#8L]

== Physical Plan ==
*Range 0, 2, 2, 50, [id#8L]
```

After this patch:
If step size is 1:
```
== Optimized Logical Plan ==
Range(0, 100, splits=2)

== Physical Plan ==
*Range(0, 100, splits=2)
```

If step size is not 1:
```
== Optimized Logical Plan ==
Range (0, 100, step=2, splits=2)

== Physical Plan ==
*Range (0, 100, step=2, splits=2)
```

## How was this patch tested?
N/A

Author: Reynold Xin <rxin@databricks.com>

Closes #13239 from rxin/SPARK-15459.

#### What changes were proposed in this pull request?
When there are duplicate keys in the partition specs or table properties, we always use the last value and ignore all the previous values. This is caused by the function call `toMap`.

partition specs or table properties are widely used in multiple DDL statements.

This PR is to detect the duplicates and issue an exception if found.

#### How was this patch tested?
Added test cases in DDLSuite

Author: gatorsmile <gatorsmile@gmail.com>

Closes #13095 from gatorsmile/detectDuplicate.

#### What changes were proposed in this pull request?
The `hive.metastore.warehouse.dir` property in hive-site.xml is deprecated since Spark 2.0.0. Users might not be able to connect to the existing metastore if they do not use the new conf parameter `spark.sql.warehouse.dir`.

This PR is to update the document and example for explaining the latest changes in the configuration of default location of database.

Below is the screenshot of the latest generated docs:

<img width="681" alt="screenshot 2016-05-20 08 38 10" src="https://cloud.githubusercontent.com/assets/11567269/15433296/a05c4ace-1e66-11e6-8d2b-73682b32e9c2.png">

<img width="789" alt="screenshot 2016-05-20 08 53 26" src="https://cloud.githubusercontent.com/assets/11567269/15433734/645dc42e-1e68-11e6-9476-effc9f8721bb.png">

<img width="789" alt="screenshot 2016-05-20 08 53 37" src="https://cloud.githubusercontent.com/assets/11567269/15433738/68569f92-1e68-11e6-83d3-ef5bb221a8d8.png">

No change is made in the R's example.

<img width="860" alt="screenshot 2016-05-20 08 54 38" src="https://cloud.githubusercontent.com/assets/11567269/15433779/965b8312-1e68-11e6-8bc4-53c88ceacde2.png">

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

Author: gatorsmile <gatorsmile@gmail.com>

Closes #13225 from gatorsmile/document.

## What changes were proposed in this pull request?

This PR makes BroadcastHint more deterministic by using a special isBroadcastable property
instead of setting the sizeInBytes to 1.

See https://issues.apache.org/jira/browse/SPARK-15415

## How was this patch tested?

Added testcases to test if the broadcast hash join is included in the plan when the BroadcastHint is supplied and also tests for propagation of the joins.

Author: Jurriaan Pruis <email@jurriaanpruis.nl>

Closes #13244 from jurriaan/broadcast-hint.

## What changes were proposed in this pull request?
Add new test cases for including distinct aggregate in having clause in 2.0 branch.
This is a followup PR for [#12974](https://github.com/apache/spark/pull/12974), which is for 1.6 branch.

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

Closes #12984 from xwu0226/SPARK-15206.

#### What changes were proposed in this pull request?
Like `Set` Command in Hive, `Reset` is also supported by Hive. See the link: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Cli

Below is the related Hive JIRA: https://issues.apache.org/jira/browse/HIVE-3202

This PR is to implement such a command for resetting the SQL-related configuration to the default values. One of the use case shown in HIVE-3202 is listed below:

> For the purpose of optimization we set various configs per query. It's worthy but all those configs should be reset every time for next query.

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

Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>

Closes #13121 from gatorsmile/resetCommand.

## What changes were proposed in this pull request?
Refactoring: Separated ORC serialization logic from OrcOutputWriter and moved to a new class called OrcSerializer.

## How was this patch tested?
Manual tests & existing tests.

Author: Ergin Seyfe <eseyfe@fb.com>

Closes #13066 from seyfe/orc_serializer.

## What changes were proposed in this pull request?
The Aggregator API was introduced in 2.0 for Dataset. All typed Dataset APIs should still be marked as experimental in 2.0.

## How was this patch tested?
N/A - annotation only change.

Author: Reynold Xin <rxin@databricks.com>

Closes #13226 from rxin/SPARK-15452.

## What changes were proposed in this pull request?

Generate a shorter default alias for `AggregateExpression `, In this PR, aggregate function name along with a index is used for generating the alias name.

```SQL
val ds = Seq(1, 3, 2, 5).toDS()
ds.select(typed.sum((i: Int) => i), typed.avg((i: Int) => i)).show()
```

Output before change.
```SQL
+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------+
|typedsumdouble(unresolveddeserializer(upcast(input[0, int], IntegerType, - root class: "scala.Int"), value#1), upcast(value))|typedaverage(unresolveddeserializer(upcast(input[0, int], IntegerType, - root class: "scala.Int"), value#1), newInstance(class scala.Tuple2))|
+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------+
|                                                                                                                         11.0|                                                                                                                                         2.75|
+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------+
```
Output after change:
```SQL
+-----------------+---------------+
|typedsumdouble_c1|typedaverage_c2|
+-----------------+---------------+
|             11.0|           2.75|
+-----------------+---------------+
```

Note: There is one test in ParquetSuites.scala which shows that that the system picked alias
name is not usable and is rejected.  [test](https://github.com/apache/spark/blob/master/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala#L672-#L687)
## How was this patch tested?

A new test was added in DataSetAggregatorSuite.

Author: Dilip Biswal <dbiswal@us.ibm.com>

Closes #13045 from dilipbiswal/spark-15114.

## What changes were proposed in this pull request?

In only `catalyst` module, there exists 8 evaluation test cases on unresolved expressions. But, in real-world situation, those cases doesn't happen since they occurs exceptions before evaluations.
```scala
scala> sql("select format_number(null, 3)")
res0: org.apache.spark.sql.DataFrame = [format_number(CAST(NULL AS DOUBLE), 3): string]
scala> sql("select format_number(cast(null as NULL), 3)")
org.apache.spark.sql.catalyst.parser.ParseException:
DataType null() is not supported.(line 1, pos 34)
```

This PR makes those testcases more realistic.
```scala
-    checkEvaluation(FormatNumber(Literal.create(null, NullType), Literal(3)), null)
+    assert(FormatNumber(Literal.create(null, NullType), Literal(3)).resolved === false)
```
Also, this PR also removes redundant `resolved` checking in `FoldablePropagation` optimizer.

## How was this patch tested?

Pass the modified Jenkins tests.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13241 from dongjoon-hyun/SPARK-15462.

## What changes were proposed in this pull request?
Using longValue() and then checking whether the value is in the range for a long manually.

## How was this patch tested?
Existing tests

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #13223 from techaddict/SPARK-15445.

## What changes were proposed in this pull request?
I initially asked to create a hivecontext-compatibility module to put the HiveContext there. But we are so close to Spark 2.0 release and there is only a single class in it. It seems overkill to have an entire package, which makes it more inconvenient, for a single class.

## How was this patch tested?
Tests were moved.

Author: Reynold Xin <rxin@databricks.com>

Closes #13207 from rxin/SPARK-15424.

## What changes were proposed in this pull request?

When PySpark shell cannot find HiveConf, it will fallback to create a SparkSession from a SparkContext.  This fixes a bug caused by using a variable to SparkContext before it was initialized.

## How was this patch tested?

Manually starting PySpark shell and using the SparkContext

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #13237 from BryanCutler/pyspark-shell-session-context-SPARK-15456.

## What changes were proposed in this pull request?
Use `SparkSession` according to [SPARK-15031](https://issues.apache.org/jira/browse/SPARK-15031)

`MLLLIB` is not recommended to use now, so examples in `MLLIB` are ignored in this PR.
`StreamingContext` can not be directly obtained from `SparkSession`, so example in `Streaming` are ignored too.

cc andrewor14

## How was this patch tested?
manual tests with spark-submit

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #13164 from zhengruifeng/use_sparksession_ii.

[SPARK-15273] YarnSparkHadoopUtil#getOutOfMemoryErrorArgument should respect OnOutOfMemoryError parameter given by user

## What changes were proposed in this pull request?

As Nirav reported in this thread:
http://search-hadoop.com/m/q3RTtdF3yNLMd7u

YarnSparkHadoopUtil#getOutOfMemoryErrorArgument previously specified 'kill %p' unconditionally.
We should respect the parameter given by user.

## How was this patch tested?

Existing tests

Author: tedyu <yuzhihong@gmail.com>

Closes #13057 from tedyu/master.

## What changes were proposed in this pull request?

Now that SparkSQL supports all TPC-DS queries, this patch adds all 99 benchmark queries inside SparkSQL.

## How was this patch tested?

Benchmark only

Author: Sameer Agarwal <sameer@databricks.com>

Closes #13188 from sameeragarwal/tpcds-all.

## What changes were proposed in this pull request?
Many other systems (e.g. Impala) uses _xxx as staging, and Spark should not be reading those files.

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

Author: Reynold Xin <rxin@databricks.com>

Closes #13227 from rxin/SPARK-15454.

## What changes were proposed in this pull request?

Currently, the explain of a query with whole-stage codegen looks like this
```
>>> df = sqlCtx.range(1000);df2 = sqlCtx.range(1000);df.join(pyspark.sql.functions.broadcast(df2), 'id').explain()
== Physical Plan ==
WholeStageCodegen
:  +- Project [id#1L]
:     +- BroadcastHashJoin [id#1L], [id#4L], Inner, BuildRight, None
:        :- Range 0, 1, 4, 1000, [id#1L]
:        +- INPUT
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint]))
   +- WholeStageCodegen
      :  +- Range 0, 1, 4, 1000, [id#4L]
```

The problem is that the plan looks much different than logical plan, make us hard to understand the plan (especially when the logical plan is not showed together).

This PR will change it to:

```
>>> df = sqlCtx.range(1000);df2 = sqlCtx.range(1000);df.join(pyspark.sql.functions.broadcast(df2), 'id').explain()
== Physical Plan ==
*Project [id#0L]
+- *BroadcastHashJoin [id#0L], [id#3L], Inner, BuildRight, None
   :- *Range 0, 1, 4, 1000, [id#0L]
   +- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, false]))
      +- *Range 0, 1, 4, 1000, [id#3L]
```

The `*`before the plan means that it's part of whole-stage codegen, it's easy to understand.

## How was this patch tested?

Manually ran some queries and check the explain.

Author: Davies Liu <davies@databricks.com>

Closes #13204 from davies/explain_codegen.

This reverts commit 8d05a7a9 from #12855, which seems to have caused regressions when working with empty DataFrames.

Author: Michael Armbrust <michael@databricks.com>

Closes #13181 from marmbrus/revert12855.