## What changes were proposed in this pull request?
Add a output mode parameter to `flatMapGroupsWithState` and just define `mapGroupsWithState` as `flatMapGroupsWithState(Update)`.
`UnsupportedOperationChecker` is modified to disallow unsupported cases.
- Batch mapGroupsWithState or flatMapGroupsWithState is always allowed.
- For streaming (map/flatMap)GroupsWithState, see the following table:
| Operators | Supported Query Output Mode |
| ------------- | ------------- |
| flatMapGroupsWithState(Update) without aggregation | Update |
| flatMapGroupsWithState(Update) with aggregation | None |
| flatMapGroupsWithState(Append) without aggregation | Append |
| flatMapGroupsWithState(Append) before aggregation | Append, Update, Complete |
| flatMapGroupsWithState(Append) after aggregation | None |
| Multiple flatMapGroupsWithState(Append)s | Append |
| Multiple mapGroupsWithStates | None |
| Mxing mapGroupsWithStates and flatMapGroupsWithStates | None |
| Other cases of multiple flatMapGroupsWithState | None |
## How was this patch tested?
The added unit tests. Here are the tests related to (map/flatMap)GroupsWithState:
```
[info] - batch plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation: supported (1 millisecond)
[info] - batch plan - flatMapGroupsWithState - multiple flatMapGroupsWithState(Append)s on batch relation: supported (0 milliseconds)
[info] - batch plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation: supported (0 milliseconds)
[info] - batch plan - flatMapGroupsWithState - multiple flatMapGroupsWithState(Update)s on batch relation: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in update mode: supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in append mode: not supported (7 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in complete mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Append mode: not supported (11 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Update mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Complete mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation without aggregation in append mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation without aggregation in update mode: not supported (6 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Append mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Update mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Complete mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation after aggregation in Append mode: not supported (6 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation after aggregation in Update mode: not supported (4 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation in complete mode: not supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation inside streaming relation in Append output mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation inside streaming relation in Update output mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation inside streaming relation in Append output mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation inside streaming relation in Update output mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - multiple flatMapGroupsWithStates on streaming relation and all are in append mode: supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - multiple flatMapGroupsWithStates on s streaming relation but some are not in append mode: not supported (7 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation without aggregation in append mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation without aggregation in complete mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Append mode: not supported (6 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Update mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Complete mode: not supported (4 milliseconds)
[info] - streaming plan - mapGroupsWithState - multiple mapGroupsWithStates on streaming relation and all are in append mode: not supported (4 milliseconds)
[info] - streaming plan - mapGroupsWithState - mixing mapGroupsWithStates and flatMapGroupsWithStates on streaming relation: not supported (4 milliseconds)
```
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17197 from zsxwing/mapgroups-check.
## What changes were proposed in this pull request?
Fix for SQL round function that modifies original column when underlying data frame is created from a local product.
import org.apache.spark.sql.functions._
case class NumericRow(value: BigDecimal)
val df = spark.createDataFrame(Seq(NumericRow(BigDecimal("1.23456789"))))
df.show()
+--------------------+
| value|
+--------------------+
|1.234567890000000000|
+--------------------+
df.withColumn("value_rounded", round('value)).show()
// before
+--------------------+-------------+
| value|value_rounded|
+--------------------+-------------+
|1.000000000000000000| 1|
+--------------------+-------------+
// after
+--------------------+-------------+
| value|value_rounded|
+--------------------+-------------+
|1.234567890000000000| 1|
+--------------------+-------------+
## How was this patch tested?
New unit test added to existing suite `org.apache.spark.sql.MathFunctionsSuite`
Author: Wojtek Szymanski <wk.szymanski@gmail.com>
Closes#17075 from wojtek-szymanski/SPARK-19727.
## What changes were proposed in this pull request?
Currently there are lots of places to make the path qualified, it is better to provide a function to do this, then the code will be more simple.
## How was this patch tested?
N/A
Author: windpiger <songjun@outlook.com>
Closes#17204 from windpiger/addQualifiledPathUtil.
### What changes were proposed in this pull request?
Observed by felixcheung in https://github.com/apache/spark/pull/16739, when users use the shuffle-enabled `repartition` API, they expect the partition they got should be the exact number they provided, even if they call shuffle-disabled `coalesce` later.
Currently, `CollapseRepartition` rule does not consider whether shuffle is enabled or not. Thus, we got the following unexpected result.
```Scala
val df = spark.range(0, 10000, 1, 5)
val df2 = df.repartition(10)
assert(df2.coalesce(13).rdd.getNumPartitions == 5)
assert(df2.coalesce(7).rdd.getNumPartitions == 5)
assert(df2.coalesce(3).rdd.getNumPartitions == 3)
```
This PR is to fix the issue. We preserve shuffle-enabled Repartition.
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#16933 from gatorsmile/CollapseRepartition.
## What changes were proposed in this pull request?
Since we have a `View` node now, we can remove the view identifier in `SubqueryAlias`, which was used to indicate a view node before.
## How was this patch tested?
Update the related test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#17210 from jiangxb1987/SubqueryAlias.
## What changes were proposed in this pull request?
Reorder the joins using a dynamic programming algorithm (Selinger paper):
First we put all items (basic joined nodes) into level 1, then we build all two-way joins at level 2 from plans at level 1 (single items), then build all 3-way joins from plans at previous levels (two-way joins and single items), then 4-way joins ... etc, until we build all n-way joins and pick the best plan among them.
When building m-way joins, we only keep the best plan (with the lowest cost) for the same set of m items. E.g., for 3-way joins, we keep only the best plan for items {A, B, C} among plans (A J B) J C, (A J C) J B and (B J C) J A. Thus, the plans maintained for each level when reordering four items A, B, C, D are as follows:
```
level 1: p({A}), p({B}), p({C}), p({D})
level 2: p({A, B}), p({A, C}), p({A, D}), p({B, C}), p({B, D}), p({C, D})
level 3: p({A, B, C}), p({A, B, D}), p({A, C, D}), p({B, C, D})
level 4: p({A, B, C, D})
```
where p({A, B, C, D}) is the final output plan.
For cost evaluation, since physical costs for operators are not available currently, we use cardinalities and sizes to compute costs.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#17138 from wzhfy/joinReorder.
## What changes were proposed in this pull request?
Make the `SET mapreduce.job.reduces` automatically converted to `spark.sql.shuffle.partitions`, it's similar to `SET mapred.reduce.tasks`.
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#17020 from wangyum/SPARK-19693.
## What changes were proposed in this pull request?
The new watermark should override the old one. Otherwise, we just pick up the first column which has a watermark, it may be unexpected.
## How was this patch tested?
The new test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17199 from zsxwing/SPARK-19859.
## What changes were proposed in this pull request?
`StreamingDeduplicateExec.watermarkPredicate` should filter based on keys. Otherwise, it may generate a wrong answer if the watermark column in `keyExpression` has a different position in the row.
`StateStoreSaveExec` has the same codes but its parent can makes sure the watermark column positions in `keyExpression` and `row` are the same.
## How was this patch tested?
The added test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17183 from zsxwing/SPARK-19841.
## What changes were proposed in this pull request?
Disallow cyclic view references, a cyclic view reference may be created by the following queries:
```
CREATE VIEW testView AS SELECT id FROM tbl
CREATE VIEW testView2 AS SELECT id FROM testView
ALTER VIEW testView AS SELECT * FROM testView2
```
In the above example, a reference cycle (testView -> testView2 -> testView) exsits.
We disallow cyclic view references by checking that in ALTER VIEW command, when the `analyzedPlan` contains the same `View` node with the altered view, we should prevent the behavior and throw an AnalysisException.
## How was this patch tested?
Test by `SQLViewSuite.test("correctly handle a cyclic view reference")`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#17152 from jiangxb1987/cyclic-view.
## What changes were proposed in this pull request?
When un-cache a table, we should not only remove the cache entry for this table, but also un-cache any other cached plans that refer to this table.
This PR also includes some refactors:
1. use `java.util.LinkedList` to store the cache entries, so that it's safer to remove elements while iterating
2. rename `invalidateCache` to `recacheByPlan`, which is more obvious about what it does.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17097 from cloud-fan/cache.
## What changes were proposed in this pull request?
This pr added entries in `FunctionRegistry` and supported `to_json` in SQL.
## How was this patch tested?
Added tests in `JsonFunctionsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#16981 from maropu/SPARK-19637.
## What changes were proposed in this pull request?
Currently in DynamicPartitionWriteTask, when we get the paritionPath of a parition, we just escape the partition value, not escape the partition name.
this will cause some problems for some special partition name situation, for example :
1) if the partition name contains '%' etc, there will be two partition path created in the filesytem, one is for escaped path like '/path/a%25b=1', another is for unescaped path like '/path/a%b=1'.
and the data inserted stored in unescaped path, while the show partitions table will return 'a%25b=1' which the partition name is escaped. So here it is not consist. And I think the data should be stored in the escaped path in filesystem, which Hive2.0.0 also have the same action.
2) if the partition name contains ':', there will throw exception that new Path("/path","a:b"), this is illegal which has a colon in the relative path.
```
java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: a:b
at org.apache.hadoop.fs.Path.initialize(Path.java:205)
at org.apache.hadoop.fs.Path.<init>(Path.java:171)
at org.apache.hadoop.fs.Path.<init>(Path.java:88)
... 48 elided
Caused by: java.net.URISyntaxException: Relative path in absolute URI: a:b
at java.net.URI.checkPath(URI.java:1823)
at java.net.URI.<init>(URI.java:745)
at org.apache.hadoop.fs.Path.initialize(Path.java:202)
... 50 more
```
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes#17173 from windpiger/fixDatasourceSpecialCharPartitionName.
## What changes were proposed in this pull request?
Before this pr, LocalLimit/GlobalLimit/Sample propagates the same row count and column stats from its child, which is incorrect.
We can get the correct rowCount in Statistics for GlobalLimit/Sample whether cbo is enabled or not.
We don't know the rowCount for LocalLimit because we don't know the partition number at that time. Column stats should not be propagated because we don't know the distribution of columns after Limit or Sample.
## How was this patch tested?
Added test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16696 from wzhfy/limitEstimation.
## What changes were proposed in this pull request?
Bugfix for reading empty file with CSV data source. Instead of throwing `NoSuchElementException`, an empty data frame is returned.
## How was this patch tested?
Added new unit test in `org.apache.spark.sql.execution.datasources.csv.CSVSuite`
Author: Wojtek Szymanski <wk.szymanski@gmail.com>
Closes#17068 from wojtek-szymanski/SPARK-19709.
## What changes were proposed in this pull request?
Currently we don't explicitly forbid the following behaviors:
1. The statement CREATE VIEW AS INSERT INTO throws the following exception:
```
scala> spark.sql("CREATE VIEW testView AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: org.apache.hadoop.hive.ql.metadata.HiveException: at least one column must be specified for the table;
scala> spark.sql("CREATE VIEW testView(a, b) AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: The number of columns produced by the SELECT clause (num: `0`) does not match the number of column names specified by CREATE VIEW (num: `2`).;
```
2. The statement INSERT INTO view VALUES throws the following exception from checkAnalysis:
```
scala> spark.sql("INSERT INTO testView VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: Inserting into an RDD-based table is not allowed.;;
'InsertIntoTable View (`default`.`testView`, [a#16,b#17]), false, false
+- LocalRelation [col1#14, col2#15]
```
After this PR, the behavior changes to:
```
scala> spark.sql("CREATE VIEW testView AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.catalyst.parser.ParseException: Operation not allowed: CREATE VIEW ... AS INSERT INTO;
scala> spark.sql("CREATE VIEW testView(a, b) AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.catalyst.parser.ParseException: Operation not allowed: CREATE VIEW ... AS INSERT INTO;
scala> spark.sql("INSERT INTO testView VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: `default`.`testView` is a view, inserting into a view is not allowed;
```
## How was this patch tested?
Add a new test case in `SparkSqlParserSuite`;
Update the corresponding test case in `SQLViewSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#17125 from jiangxb1987/insert-with-view.
## What changes were proposed in this pull request?
Currently we treat the location of table/partition/database as URI string.
It will be safer if we can make the type of location as java.net.URI.
In this PR, there are following classes changes:
**1. CatalogDatabase**
```
case class CatalogDatabase(
name: String,
description: String,
locationUri: String,
properties: Map[String, String])
--->
case class CatalogDatabase(
name: String,
description: String,
locationUri: URI,
properties: Map[String, String])
```
**2. CatalogStorageFormat**
```
case class CatalogStorageFormat(
locationUri: Option[String],
inputFormat: Option[String],
outputFormat: Option[String],
serde: Option[String],
compressed: Boolean,
properties: Map[String, String])
---->
case class CatalogStorageFormat(
locationUri: Option[URI],
inputFormat: Option[String],
outputFormat: Option[String],
serde: Option[String],
compressed: Boolean,
properties: Map[String, String])
```
Before and After this PR, it is transparent for user, there is no change that the user should concern. The `String` to `URI` just happened in SparkSQL internally.
Here list some operation related location:
**1. whitespace in the location**
e.g. `/a/b c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b c/d`,
and the real path in the FileSystem also show `/a/b c/d`
**2. colon(:) in the location**
e.g. `/a/b:c/d`
For both table location and partition location,
when `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b:c/d'` ,
**In linux file system**
`DESC EXTENDED t ` show the location is `/a/b:c/d`,
and the real path in the FileSystem also show `/a/b:c/d`
**in HDFS** throw exception:
`java.lang.IllegalArgumentException: Pathname /a/b:c/d from hdfs://iZbp1151s8hbnnwriekxdeZ:9000/a/b:c/d is not a valid DFS filename.`
**while** After `INSERT INTO TABLE t PARTITION(a="a:b") SELECT 1`
then `DESC EXTENDED t ` show the location is `/xxx/a=a%3Ab`,
and the real path in the FileSystem also show `/xxx/a=a%3Ab`
**3. percent sign(%) in the location**
e.g. `/a/b%c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b%c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b%c/d`,
and the real path in the FileSystem also show `/a/b%c/d`
**4. encoded(%25) in the location**
e.g. `/a/b%25c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b%25c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b%25c/d`,
and the real path in the FileSystem also show `/a/b%25c/d`
**while** After `INSERT INTO TABLE t PARTITION(a="%25") SELECT 1`
then `DESC EXTENDED t ` show the location is `/xxx/a=%2525`,
and the real path in the FileSystem also show `/xxx/a=%2525`
**Additionally**, except the location, there are two other factors will affect the location of the table/partition. one is the table name which does not allowed to have special characters, and the other is `partition name` which have the same actions with `partition value`, and `partition name` with special character situation has add some testcase and resolve a bug in [PR](https://github.com/apache/spark/pull/17173)
### Summary:
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION path`,
the path which we get from `DESC TABLE` and `real path in FileSystem` are all the same with the `CREATE TABLE` command(different filesystem has different action that allow what kind of special character to create the path, e.g. HDFS does not allow colon, but linux filesystem allow it ).
`DataBase` also have the same logic with `CREATE TABLE`
while if the `partition value` has some special character like `%` `:` `#` etc, then we will get the path with encoded `partition value` like `/xxx/a=A%25B` from `DESC TABLE` and `real path in FileSystem`
In this PR, the core change code is using `new Path(str).toUri` and `new Path(uri).toString`
which transfrom `str to uri `or `uri to str`.
for example:
```
val str = '/a/b c/d'
val uri = new Path(str).toUri --> '/a/b%20c/d'
val strFromUri = new Path(uri).toString -> '/a/b c/d'
```
when we restore table/partition from metastore, or get the location from `CREATE TABLE` command, we can use it as above to change string to uri `new Path(str).toUri `
## How was this patch tested?
unit test added.
The `current master branch` also `passed all the test cases` added in this PR by a litter change.
https://github.com/apache/spark/pull/17149/files#diff-b7094baa12601424a5d19cb930e3402fR1764
here `toURI` -> `toString` when test in master branch.
This can show that this PR is transparent for user.
Author: windpiger <songjun@outlook.com>
Closes#17149 from windpiger/changeStringToURI.
## What changes were proposed in this pull request?
This PR proposes to both,
**Do not allow json arrays with multiple elements and return null in `from_json` with `StructType` as the schema.**
Currently, it only reads the single row when the input is a json array. So, the codes below:
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = StructType(StructField("a", IntegerType) :: Nil)
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("struct").select(from_json(col("struct"), schema)).show()
```
prints
```
+--------------------+
|jsontostruct(struct)|
+--------------------+
| [1]|
+--------------------+
```
This PR simply suggests to print this as `null` if the schema is `StructType` and input is json array.with multiple elements
```
+--------------------+
|jsontostruct(struct)|
+--------------------+
| null|
+--------------------+
```
**Support json arrays in `from_json` with `ArrayType` as the schema.**
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil))
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("array").select(from_json(col("array"), schema)).show()
```
prints
```
+-------------------+
|jsontostruct(array)|
+-------------------+
| [[1], [2]]|
+-------------------+
```
## How was this patch tested?
Unit test in `JsonExpressionsSuite`, `JsonFunctionsSuite`, Python doctests and manual test.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16929 from HyukjinKwon/disallow-array.
## What changes were proposed in this pull request?
This pr is to support Seq, Map, and Struct in functions.lit; it adds a new IF named `lit2` with `TypeTag` for avoiding type erasure.
## How was this patch tested?
Added tests in `LiteralExpressionSuite`
Author: Takeshi Yamamuro <yamamuro@apache.org>
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#16610 from maropu/SPARK-19254.
## What changes were proposed in this pull request?
"DataFrameCallbackSuite.execute callback functions when a DataFrame action failed" sets the log level to "fatal" but doesn't recover it. Hence, tests running after it won't output any logs except fatal logs.
This PR uses `testQuietly` instead to avoid changing the log level.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17156 from zsxwing/SPARK-19816.
## What changes were proposed in this pull request?
[SPARK-19617](https://issues.apache.org/jira/browse/SPARK-19617) changed `HDFSMetadataLog` to enable interrupts when using the local file system. However, now we hit [HADOOP-12074](https://issues.apache.org/jira/browse/HADOOP-12074): `Shell.runCommand` converts `InterruptedException` to `new IOException(ie.toString())` before Hadoop 2.8. This is the Hadoop patch to fix HADOOP-1207: 95c73d49b1
This PR adds new logic to handle the following cases related to `InterruptedException`.
- Check if the message of IOException starts with `java.lang.InterruptedException`. If so, treat it as `InterruptedException`. This is for pre-Hadoop 2.8.
- Treat `InterruptedIOException` as `InterruptedException`. This is for Hadoop 2.8+ and other places that may throw `InterruptedIOException` when the thread is interrupted.
## How was this patch tested?
The new unit test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17044 from zsxwing/SPARK-19718.
## What changes were proposed in this pull request?
This is a follow-up pr of #16308 and #16750.
This pr enables timezone support in partition values.
We should use `timeZone` option introduced at #16750 to parse/format partition values of the `TimestampType`.
For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT` which will be used for partition values, the values written by the default timezone option, which is `"GMT"` because the session local timezone is `"GMT"` here, are:
```scala
scala> spark.conf.set("spark.sql.session.timeZone", "GMT")
scala> val df = Seq((1, new java.sql.Timestamp(1451606400000L))).toDF("i", "ts")
df: org.apache.spark.sql.DataFrame = [i: int, ts: timestamp]
scala> df.show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2016-01-01 00:00:00|
+---+-------------------+
scala> df.write.partitionBy("ts").save("/path/to/gmtpartition")
```
```sh
$ ls /path/to/gmtpartition/
_SUCCESS ts=2016-01-01 00%3A00%3A00
```
whereas setting the option to `"PST"`, they are:
```scala
scala> df.write.option("timeZone", "PST").partitionBy("ts").save("/path/to/pstpartition")
```
```sh
$ ls /path/to/pstpartition/
_SUCCESS ts=2015-12-31 16%3A00%3A00
```
We can properly read the partition values if the session local timezone and the timezone of the partition values are the same:
```scala
scala> spark.read.load("/path/to/gmtpartition").show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2016-01-01 00:00:00|
+---+-------------------+
```
And even if the timezones are different, we can properly read the values with setting corrent timezone option:
```scala
// wrong result
scala> spark.read.load("/path/to/pstpartition").show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2015-12-31 16:00:00|
+---+-------------------+
// correct result
scala> spark.read.option("timeZone", "PST").load("/path/to/pstpartition").show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2016-01-01 00:00:00|
+---+-------------------+
```
## How was this patch tested?
Existing tests and added some tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#17053 from ueshin/issues/SPARK-18939.
## What changes were proposed in this pull request?
We call stop() on a Structured Streaming Source only when the stream is shutdown when a user calls streamingQuery.stop(). We should actually stop all sources when the stream fails as well, otherwise we may leak resources, e.g. connections to Kafka.
## How was this patch tested?
Unit tests in `StreamingQuerySuite`.
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#17107 from brkyvz/close-source.
## What changes were proposed in this pull request?
Changes to SQLQueryTests to make the order of the results constant.
Where possible ORDER BY has been added to match the existing expected output
## How was this patch tested?
Test runs on x86, zLinux (big endian), ppc (big endian)
Author: Pete Robbins <robbinspg@gmail.com>
Closes#17039 from robbinspg/SPARK-19710.
## What changes were proposed in this pull request?
When we resolve inline tables in analyzer, we will evaluate the expressions of inline tables.
When it evaluates a `TimeZoneAwareExpression` expression, an error will happen because the `TimeZoneAwareExpression` is not associated with timezone yet.
So we need to resolve these `TimeZoneAwareExpression`s with time zone when resolving inline tables.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17114 from viirya/resolve-timeawareexpr-inline-table.
## What changes were proposed in this pull request?
This PR suggests adding some comments in `UnivocityParser` logics to explain what happens. Also, it proposes, IMHO, a little bit cleaner (at least easy for me to explain).
## How was this patch tested?
Unit tests in `CSVSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17142 from HyukjinKwon/SPARK-18699.
## What changes were proposed in this pull request?
Currently when we resolveRelation for a `FileFormat DataSource` without providing user schema, it will execute `listFiles` twice in `InMemoryFileIndex` during `resolveRelation`.
This PR add a `FileStatusCache` for DataSource, this can avoid listFiles twice.
But there is a bug in `InMemoryFileIndex` see:
[SPARK-19748](https://github.com/apache/spark/pull/17079)
[SPARK-19761](https://github.com/apache/spark/pull/17093),
so this pr should be after SPARK-19748/ SPARK-19761.
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes#17081 from windpiger/resolveDataSourceScanFilesTwice.
## What changes were proposed in this pull request?
[SPARK-19779](https://issues.apache.org/jira/browse/SPARK-19779)
The PR (https://github.com/apache/spark/pull/17012) can to fix restart a Structured Streaming application using hdfs as fileSystem, but also exist a problem that a tmp file of delta file is still reserved in hdfs. And Structured Streaming don't delete the tmp file generated when restart streaming job in future.
## How was this patch tested?
unit tests
Author: guifeng <guifengleaf@gmail.com>
Closes#17124 from gf53520/SPARK-19779.
## What changes were proposed in this pull request?
- Add tests covering different scenarios with qualified column names
- Please see Section 2 in the design doc for the various test scenarios [here](https://issues.apache.org/jira/secure/attachment/12854681/Design_ColResolution_JIRA19602.pdf)
- As part of SPARK-19602, changes are made to support three part column name. In order to aid in the review and to reduce the diff, the test scenarios are separated out into this PR.
## How was this patch tested?
- This is a **test only** change. The individual test suites were run successfully.
Author: Sunitha Kambhampati <skambha@us.ibm.com>
Closes#17067 from skambha/colResolutionTests.
## What changes were proposed in this pull request?
Update doc for R, programming guide. Clarify default behavior for all languages.
## How was this patch tested?
manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17128 from felixcheung/jsonwholefiledoc.
## What changes were proposed in this pull request?
```
spark.sql(
s"""
|CREATE TABLE t
|USING parquet
|PARTITIONED BY(a, b)
|LOCATION '$dir'
|AS SELECT 3 as a, 4 as b, 1 as c, 2 as d
""".stripMargin)
```
Failed with the error message:
```
path file:/private/var/folders/6r/15tqm8hn3ldb3rmbfqm1gf4c0000gn/T/spark-195cd513-428a-4df9-b196-87db0c73e772 already exists.;
org.apache.spark.sql.AnalysisException: path file:/private/var/folders/6r/15tqm8hn3ldb3rmbfqm1gf4c0000gn/T/spark-195cd513-428a-4df9-b196-87db0c73e772 already exists.;
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:102)
```
while hive table is ok ,so we should fix it for datasource table.
The reason is that the SaveMode check is put in `InsertIntoHadoopFsRelationCommand` , and the SaveMode check actually use `path`, this is fine when we use `DataFrameWriter.save()`, because this situation of SaveMode act on `path`.
While when we use `CreateDataSourceAsSelectCommand`, the situation of SaveMode act on table, and
we have already do SaveMode check in `CreateDataSourceAsSelectCommand` for table , so we should not do SaveMode check in the following logic in `InsertIntoHadoopFsRelationCommand` for path, this is redundant and wrong logic for `CreateDataSourceAsSelectCommand`
After this PR, the following DDL will succeed, when the location has been created we will append it or overwrite it.
```
CREATE TABLE ... (PARTITIONED BY ...) LOCATION path AS SELECT ...
```
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes#16938 from windpiger/CTASDataSourceWitLocation.
## What changes were proposed in this pull request?
If we create a InMemoryFileIndex with an empty rootPaths when set PARALLEL_PARTITION_DISCOVERY_THRESHOLD to zero, it will throw an exception:
```
Positive number of slices required
java.lang.IllegalArgumentException: Positive number of slices required
at org.apache.spark.rdd.ParallelCollectionRDD$.slice(ParallelCollectionRDD.scala:119)
at org.apache.spark.rdd.ParallelCollectionRDD.getPartitions(ParallelCollectionRDD.scala:97)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2084)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex$.org$apache$spark$sql$execution$datasources$PartitioningAwareFileIndex$$bulkListLeafFiles(PartitioningAwareFileIndex.scala:357)
at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex.listLeafFiles(PartitioningAwareFileIndex.scala:256)
at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.refresh0(InMemoryFileIndex.scala:74)
at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.<init>(InMemoryFileIndex.scala:50)
at org.apache.spark.sql.execution.datasources.FileIndexSuite$$anonfun$9$$anonfun$apply$mcV$sp$2.apply$mcV$sp(FileIndexSuite.scala:186)
at org.apache.spark.sql.test.SQLTestUtils$class.withSQLConf(SQLTestUtils.scala:105)
at org.apache.spark.sql.execution.datasources.FileIndexSuite.withSQLConf(FileIndexSuite.scala:33)
at org.apache.spark.sql.execution.datasources.FileIndexSuite$$anonfun$9.apply$mcV$sp(FileIndexSuite.scala:185)
at org.apache.spark.sql.execution.datasources.FileIndexSuite$$anonfun$9.apply(FileIndexSuite.scala:185)
at org.apache.spark.sql.execution.datasources.FileIndexSuite$$anonfun$9.apply(FileIndexSuite.scala:185)
at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
```
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes#17093 from windpiger/fixEmptiPathInBulkListFiles.
## What changes were proposed in this pull request?
`Catalog.refreshByPath` can refresh the cache entry and the associated metadata for all dataframes (if any), that contain the given data source path.
However, `CacheManager.invalidateCachedPath` doesn't clear all cached plans with the specified path. It causes some strange behaviors reported in SPARK-15678.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17064 from viirya/fix-refreshByPath.
## What changes were proposed in this pull request?
Right now file source always uses `InMemoryFileIndex` to scan files from a given path.
But when reading the outputs from another streaming query, the file source should use `MetadataFileIndex` to list files from the sink log. This patch adds this support.
## `MetadataFileIndex` or `InMemoryFileIndex`
```scala
spark
.readStream
.format(...)
.load("/some/path") // for a non-glob path:
// - use `MetadataFileIndex` when `/some/path/_spark_meta` exists
// - fall back to `InMemoryFileIndex` otherwise
```
```scala
spark
.readStream
.format(...)
.load("/some/path/*/*") // for a glob path: always use `InMemoryFileIndex`
```
## How was this patch tested?
two newly added tests
Author: Liwei Lin <lwlin7@gmail.com>
Closes#16987 from lw-lin/source-read-from-sink.
## What changes were proposed in this pull request?
SPARK-15236 do this for scala shell, this ticket is for sparkR shell. This is not only for sparkR itself, but can also benefit downstream project like livy which use shell.R for its interactive session. For now, livy has no control of whether enable hive or not.
## How was this patch tested?
Tested it manually, run `bin/sparkR --master local --conf spark.sql.catalogImplementation=in-memory` and verify hive is not enabled.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#16907 from zjffdu/SPARK-19572.
## What changes were proposed in this pull request?
This PR proposes the support for multiple lines for CSV by resembling the multiline supports in JSON datasource (in case of JSON, per file).
So, this PR introduces `wholeFile` option which makes the format not splittable and reads each whole file. Since Univocity parser can produces each row from a stream, it should be capable of parsing very large documents when the internal rows are fix in the memory.
## How was this patch tested?
Unit tests in `CSVSuite` and `tests.py`
Manual tests with a single 9GB CSV file in local file system, for example,
```scala
spark.read.option("wholeFile", true).option("inferSchema", true).csv("tmp.csv").count()
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16976 from HyukjinKwon/SPARK-19610.
## What changes were proposed in this pull request?
If we first cache a DataSource table, then we insert some data into the table, we should refresh the data in the cache after the insert command.
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes#16809 from windpiger/refreshCacheAfterInsert.
## What changes were proposed in this pull request?
HDFSBackedStateStoreProvider fails to rename files on HDFS but not on the local filesystem. According to the [implementation notes](https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-common/filesystem/filesystem.html) of `rename()`, the behavior of the local filesystem and HDFS varies:
> Destination exists and is a file
> Renaming a file atop an existing file is specified as failing, raising an exception.
> - Local FileSystem : the rename succeeds; the destination file is replaced by the source file.
> - HDFS : The rename fails, no exception is raised. Instead the method call simply returns false.
This patch ensures that `rename()` isn't called if the destination file already exists. It's still semantically correct because Structured Streaming requires that rerunning a batch should generate the same output.
## How was this patch tested?
This patch was tested by running `StateStoreSuite`.
Author: Roberto Agostino Vitillo <ra.vitillo@gmail.com>
Closes#17012 from vitillo/fix_rename.
## What changes were proposed in this pull request?
`MetastoreRelation` is used to represent table relation for hive tables, and provides some hive related information. We will resolve `SimpleCatalogRelation` to `MetastoreRelation` for hive tables, which is unnecessary as these 2 are the same essentially. This PR merges `SimpleCatalogRelation` and `MetastoreRelation`
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17015 from cloud-fan/table-relation.
## What changes were proposed in this pull request?
If we refresh a InMemoryFileIndex with a FileStatusCache, it will first use the FileStatusCache to re-generate the cachedLeafFiles etc, then call FileStatusCache.invalidateAll.
While the order to do these two actions is wrong, this lead to the refresh action does not take effect.
```
override def refresh(): Unit = {
refresh0()
fileStatusCache.invalidateAll()
}
private def refresh0(): Unit = {
val files = listLeafFiles(rootPaths)
cachedLeafFiles =
new mutable.LinkedHashMap[Path, FileStatus]() ++= files.map(f => f.getPath -> f)
cachedLeafDirToChildrenFiles = files.toArray.groupBy(_.getPath.getParent)
cachedPartitionSpec = null
}
```
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes#17079 from windpiger/fixInMemoryFileIndexRefresh.
## What changes were proposed in this pull request?
Name socket source with a meaningful name
## How was this patch tested?
Jenkins
Author: uncleGen <hustyugm@gmail.com>
Closes#17082 from uncleGen/SPARK-19749.
## What changes were proposed in this pull request?
This PR proposes to replace the deprecated `json(RDD[String])` usage to `json(Dataset[String])`.
This currently produces so many warnings.
## How was this patch tested?
Fixed tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17071 from HyukjinKwon/SPARK-15615-followup.
## What changes were proposed in this pull request?
This PR proposes to fix the lint-breaks as below:
```
[ERROR] src/test/java/org/apache/spark/network/TransportResponseHandlerSuite.java:[29,8] (imports) UnusedImports: Unused import - org.apache.spark.network.buffer.ManagedBuffer.
[ERROR] src/main/java/org/apache/spark/unsafe/types/UTF8String.java:[156,10] (modifier) ModifierOrder: 'Nonnull' annotation modifier does not precede non-annotation modifiers.
[ERROR] src/main/java/org/apache/spark/SparkFirehoseListener.java:[122] (sizes) LineLength: Line is longer than 100 characters (found 105).
[ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java:[164,78] (coding) OneStatementPerLine: Only one statement per line allowed.
[ERROR] src/test/java/test/org/apache/spark/JavaAPISuite.java:[1157] (sizes) LineLength: Line is longer than 100 characters (found 121).
[ERROR] src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java:[149] (sizes) LineLength: Line is longer than 100 characters (found 113).
[ERROR] src/test/java/test/org/apache/spark/streaming/Java8APISuite.java:[146] (sizes) LineLength: Line is longer than 100 characters (found 122).
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[32,8] (imports) UnusedImports: Unused import - org.apache.spark.streaming.Time.
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[611] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[1317] (sizes) LineLength: Line is longer than 100 characters (found 102).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetAggregatorSuite.java:[91] (sizes) LineLength: Line is longer than 100 characters (found 102).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[113] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[164] (sizes) LineLength: Line is longer than 100 characters (found 110).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[212] (sizes) LineLength: Line is longer than 100 characters (found 114).
[ERROR] src/test/java/org/apache/spark/mllib/tree/JavaDecisionTreeSuite.java:[36] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java:[26,8] (imports) UnusedImports: Unused import - com.amazonaws.regions.RegionUtils.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisStreamSuite.java:[20,8] (imports) UnusedImports: Unused import - com.amazonaws.regions.RegionUtils.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisStreamSuite.java:[94] (sizes) LineLength: Line is longer than 100 characters (found 103).
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java:[30,8] (imports) UnusedImports: Unused import - org.apache.spark.sql.api.java.UDF1.
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java:[72] (sizes) LineLength: Line is longer than 100 characters (found 104).
[ERROR] src/main/java/org/apache/spark/examples/mllib/JavaRankingMetricsExample.java:[121] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java:[28,8] (imports) UnusedImports: Unused import - org.apache.spark.api.java.JavaRDD.
[ERROR] src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java:[29,8] (imports) UnusedImports: Unused import - org.apache.spark.api.java.JavaSparkContext.
```
## How was this patch tested?
Manually via
```bash
./dev/lint-java
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17072 from HyukjinKwon/java-lint.
## What changes were proposed in this pull request?
currently if multiple streaming queries listeners exists, when a QueryTerminatedEvent is triggered, only one of the listeners will be invoked while the rest of the listeners will ignore the event.
this is caused since the the streaming queries listeners bus holds a set of running queries ids and when a termination event is triggered, after the first listeners is handling the event, the terminated query id is being removed from the set.
in this PR, the query id will be removed from the set only after all the listeners handles the event
## How was this patch tested?
a test with multiple listeners has been added to StreamingQueryListenerSuite
Author: Eyal Zituny <eyal.zituny@equalum.io>
Closes#16991 from eyalzit/master.
## What changes were proposed in this pull request?
After adding the tests for subquery, we now have multiple level of directories under "sql-tests/inputs". Some times on Mac while using Finder application it creates the meta data files called ".DS_Store". When these files are present at different levels in directory hierarchy, we get duplicate test exception while running the tests as we just use the file name as the test case name. In this PR, we use the relative file path from the base directory along with the test file as the test name. Also after this change, we can have the same test file name under different directory like exists/basic.sql , in/basic.sql. Here is the truncated output of the test run after the change.
```SQL
info] SQLQueryTestSuite:
[info] - arithmetic.sql (5 seconds, 235 milliseconds)
[info] - array.sql (536 milliseconds)
[info] - blacklist.sql !!! IGNORED !!!
[info] - cast.sql (550 milliseconds)
....
....
....
[info] - union.sql (315 milliseconds)
[info] - subquery/.DS_Store !!! IGNORED !!!
[info] - subquery/exists-subquery/.DS_Store !!! IGNORED !!!
[info] - subquery/exists-subquery/exists-aggregate.sql (2 seconds, 451 milliseconds)
....
....
[info] - subquery/in-subquery/in-group-by.sql (12 seconds, 264 milliseconds)
....
....
[info] - subquery/scalar-subquery/scalar-subquery-predicate.sql (7 seconds, 769 milliseconds)
[info] - subquery/scalar-subquery/scalar-subquery-select.sql (4 seconds, 119 milliseconds)
```
Since this is a simple change, i haven't created a JIRA for it.
## How was this patch tested?
Manually verified. This is change to test infrastructure
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#17060 from dilipbiswal/sqlquerytestsuite.
Spark executes SQL commands eagerly. It does this by creating an RDD which contains the command's results. The downside to this is that any action on this RDD triggers a Spark job which is expensive and is unnecessary.
This PR fixes this by avoiding the materialization of an `RDD` for `Command`s; it just materializes the result and puts them in a `LocalRelation`.
Added a regression test to `SQLQuerySuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17027 from hvanhovell/no-job-command.
### What changes were proposed in this pull request?
As explained in Hive JIRA https://issues.apache.org/jira/browse/HIVE-12224, HOLD_DDLTIME was broken as soon as it landed. Hive 2.0 removes HOLD_DDLTIME from the API. In Spark SQL, we always set it to FALSE. Like Hive, we should also remove it from our Catalog APIs.
### How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17063 from gatorsmile/removalHoldDDLTime.
