### What changes were proposed in this pull request?
This PR adds the version that added pandas API on Spark in PySpark documentation.
### Why are the changes needed?
To document the version added.
### Does this PR introduce _any_ user-facing change?
No to end user. Spark 3.2 is not released yet.
### How was this patch tested?
Linter and documentation build.
Closes#33473 from HyukjinKwon/SPARK-36253.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit f3e29574d9)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds optimization for scalar and lateral subqueries with OneRowRelation as leaf nodes. It inlines such subqueries before decorrelation to avoid rewriting them as left outer joins. It also introduces a flag to turn on/off this optimization: `spark.sql.optimizer.optimizeOneRowRelationSubquery` (default: True).
For example:
```sql
select (select c1) from t
```
Analyzed plan:
```
Project [scalar-subquery#17 [c1#18] AS scalarsubquery(c1)#22]
: +- Project [outer(c1#18)]
: +- OneRowRelation
+- LocalRelation [c1#18, c2#19]
```
Optimized plan before this PR:
```
Project [c1#18#25 AS scalarsubquery(c1)#22]
+- Join LeftOuter, (c1#24 <=> c1#18)
:- LocalRelation [c1#18]
+- Aggregate [c1#18], [c1#18 AS c1#18#25, c1#18 AS c1#24]
+- LocalRelation [c1#18]
```
Optimized plan after this PR:
```
LocalRelation [scalarsubquery(c1)#22]
```
### Why are the changes needed?
To optimize query plans.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added new unit tests.
Closes#33284 from allisonwang-db/spark-36063-optimize-subquery-one-row-relation.
Authored-by: allisonwang-db <allison.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit de8e4be92c)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR partially backports the fix in the script at https://github.com/apache/spark/pull/33410 to make the branch-3.2 build pass at https://github.com/apache/spark/actions/workflows/build_and_test.yml?query=event%3Aschedule
### Why are the changes needed?
To make the Scala 2.13 periodical job pass
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
It is a logically non-conflicting backport.
Closes#33472 from HyukjinKwon/SPARK-36251.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR upgrades `zstd-jni` from `1.5.0-2` to `1.5.0-3`.
`1.5.0-3` was released few days ago.
This release resolves an issue about buffer size calculation, which can affect usage in Spark.
https://github.com/luben/zstd-jni/releases/tag/v1.5.0-3
### Why are the changes needed?
It might be a corner case that skipping length is greater than `2^31 - 1` but it's possible to affect Spark.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI.
Closes#33464 from sarutak/upgrade-zstd-jni-1.5.0-3.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit dcb7db5370)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add categories setter to `CategoricalAccessor` and `CategoricalIndex`.
### Why are the changes needed?
We should implement categories setter in `CategoricalAccessor` and `CategoricalIndex`.
### Does this PR introduce _any_ user-facing change?
Yes, users will be able to use categories setter.
### How was this patch tested?
Added some tests.
Closes#33448 from ueshin/issues/SPARK-36188/categories_setter.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
(cherry picked from commit d506815a92)
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This PR changes `BaseScriptTransformationExec` for `SparkScriptTransformationExec` to support ANSI interval types.
### Why are the changes needed?
`SparkScriptTransformationExec` support `CalendarIntervalType` so it's better to support ANSI interval types as well.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Authored-by: Kousuke Saruta <sarutakoss.nttdata.com>
Signed-off-by: Max Gekk <max.gekkgmail.com>
(cherry picked from commit f56c7b71ff)
Signed-off-by: Max Gekk <max.gekkgmail.com>
Closes#33463 from MaxGekk/sarutak_script-transformation-interval-3.2.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Remove TimestampNTZ type support in the production code of Spark 3.2.
To archive the goal, this PR adds the check "Utils.isTesting" in the following code branches:
- keyword "timestamp_ntz" and "timestamp_ltz" in parser
- New expressions from https://issues.apache.org/jira/browse/SPARK-35662
- Using java.time.localDateTime as the external type for TimestampNTZType
- `SQLConf.timestampType` which determines the default timestamp type of Spark SQL.
This is to minimize the code difference between the master branch. So that future users won't think TimestampNTZ is already available in Spark 3.2.
The downside is that users can still find TimestampNTZType under package `org.apache.spark.sql.types`. There should be nothing left other than this.
### Why are the changes needed?
As of now, there are some blockers for delivering the TimestampNTZ project in Spark 3.2:
- In the Hive Thrift server, both TimestampType and TimestampNTZType are mapped to the same timestamp type, which can cause confusion for users.
- For the Parquet data source, the new written TimestampNTZType Parquet columns will be read as TimestampType in old Spark releases. Also, we need to decide the merge schema for files mixed with TimestampType and TimestampNTZ type.
- The type coercion rules for TimestampNTZType are incomplete. For example, what should the data type of the in clause "IN(Timestamp'2020-01-01 00:00:00', TimestampNtz'2020-01-01 00:00:00') be.
- It is tricky to support TimestampNTZType in JSON/CSV data readers. We need to avoid regressions as possible as we can.
There are 10 days left for the expected 3.2 RC date. So, I propose to **release the TimestampNTZ type in Spark 3.3 instead of Spark 3.2**. So that we have enough time to make considerate designs for the issues.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing Unit tests + manual tests from spark-shell to validate the changes are gone.
New functions
```
spark.sql("select to_timestamp_ntz'2021-01-01 00:00:00'").show()
spark.sql("select to_timestamp_ltz'2021-01-01 00:00:00'").show()
spark.sql("select make_timestamp_ntz(1,1,1,1,1,1)").show()
spark.sql("select make_timestamp_ltz(1,1,1,1,1,1)").show()
spark.sql("select localtimestamp()").show()
```
The SQL configuration `spark.sql.timestampType` should not work in 3.2
```
spark.conf.set("spark.sql.timestampType", "TIMESTAMP_NTZ")
spark.sql("select make_timestamp(1,1,1,1,1,1)").schema
spark.sql("select to_timestamp('2021-01-01 00:00:00')").schema
spark.sql("select timestamp'2021-01-01 00:00:00'").schema
Seq((1, java.sql.Timestamp.valueOf("2021-01-01 00:00:00"))).toDF("i", "ts").write.partitionBy("ts").parquet("/tmp/test")
spark.read.parquet("/tmp/test").schema
```
LocalDateTime is not supported as a built-in external type:
```
Seq(LocalDateTime.now()).toDF()
org.apache.spark.sql.catalyst.expressions.Literal(java.time.LocalDateTime.now())
org.apache.spark.sql.catalyst.expressions.Literal(0L, TimestampNTZType)
```
Closes#33444 from gengliangwang/banNTZ.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This fixes a case sensitivity issue for desc table commands with partition specified.
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
yes, but it's a bugfix
### How was this patch tested?
new tests
#### before
```
+-- !query
+DESC EXTENDED t PARTITION (C='Us', D=1)
+-- !query schema
+struct<>
+-- !query output
+org.apache.spark.sql.AnalysisException
+Partition spec is invalid. The spec (C, D) must match the partition spec (c, d) defined in table '`default`.`t`'
+
```
#### after
https://github.com/apache/spark/pull/33424/files#diff-554189c49950974a948f99fa9b7436f615052511660c6a0ae3062fa8ca0a327cR328Closes#33424 from yaooqinn/SPARK-36213.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Kent Yao <yao@apache.org>
(cherry picked from commit 4cd6cfc773)
Signed-off-by: Kent Yao <yao@apache.org>
### What changes were proposed in this pull request?
For non-datasource Hive tables, e.g. tables written outside of Spark (through Hive or Trino), we have certain optimzations in Spark where we use Spark ORC and Parquet datasources to read these tables ([Ref](fbf53dee37/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala (L128))) rather than using the Hive serde.
If such a table contains a `path` property, Spark will try to list this path property in addition to the table location when creating an `InMemoryFileIndex`. ([Ref](fbf53dee37/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala (L575))) This can lead to wrong data if `path` property points to a directory location or an error if `path` is not a location. A concrete example is provided in [SPARK-28266 (comment)](https://issues.apache.org/jira/browse/SPARK-28266?focusedCommentId=17380170&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-17380170).
Since these tables were not written through Spark, Spark should not interpret this `path` property as it can be set by an external system with a different meaning.
### Why are the changes needed?
For better compatibility with Hive tables generated by other platforms (non-Spark)
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added unit test
Closes#33328 from shardulm94/spark-28266.
Authored-by: Shardul Mahadik <smahadik@linkedin.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 685c3fd05b)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Sometimes, AQE skew join optimization can fail with NPE. This is because AQE tries to get the shuffle block sizes, but some map outputs are missing due to the executor lost or something.
This PR fixes this bug by skipping skew join handling if some map outputs are missing in the `MapOutputTracker`.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
a new UT
Closes#33445 from cloud-fan/bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 9c8a3d3975)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Spark 3.2.0 will use parquet-mr.1.12.0 version (or higher), that contains the column encryption feature which can be called from Spark SQL. The aim of this PR is to document the use of Parquet encryption in Spark.
### Why are the changes needed?
- To provide information on how to use Parquet column encryption
### Does this PR introduce _any_ user-facing change?
Yes, documents a new feature.
### How was this patch tested?
bundle exec jekyll build
Closes#32895 from ggershinsky/parquet-encryption-doc.
Authored-by: Gidon Gershinsky <ggershinsky@apple.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Update trasform's doc to latest code.
### Why are the changes needed?
keep consistence
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No
Closes#33362 from AngersZhuuuu/SPARK-36153.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/33222 .
https://github.com/apache/spark/pull/33222 made a mistake that, `RemoveRedundantProjects` may lose the `LOGICAL_PLAN_TAG` tag, even though the logical plan link is retained. This was actually caught by the test `LogicalPlanTagInSparkPlanSuite`, but was not being taken care of.
There is no problem so far, but losing information can always lead to potential bugs.
### Why are the changes needed?
fix a mistake
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing test
Closes#33442 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 94aece4325)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Adding support for accepting an initial state with flatMapGroupsWithState in batch mode.
### Why are the changes needed?
SPARK-35897 added support for accepting an initial state for streaming queries using flatMapGroupsWithState. the code flow is separate for batch and streaming and required a different PR.
### Does this PR introduce _any_ user-facing change?
Yes as discussed above flatMapGroupsWithState in batch mode can accept an initialState, previously this would throw an UnsupportedOperationException
### How was this patch tested?
Added relevant unit tests in FlatMapGroupsWithStateSuite and modified the tests `JavaDatasetSuite`
Closes#33336 from rahulsmahadev/flatMapGroupsWithStateBatch.
Authored-by: Rahul Mahadev <rahul.mahadev@databricks.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
(cherry picked from commit efcce23b91)
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
### What changes were proposed in this pull request?
Removes `FileFormatDataWriterMetricSuite` which duplicated.
### Why are the changes needed?
`FileFormatDataWriterMetricSuite` should be renamed to `InMemoryTableMetricSuite`. But it was wrongly copied.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#33454 from viirya/SPARK-36030-followup-3.2.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR avoid using procedure syntax deprecated in Scala 2.13.
https://github.com/apache/spark/runs/3120481756?check_suite_focus=true
```
[error] /home/runner/work/spark/spark/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/FileFormatDataWriterMetricSuite.scala:44:90: procedure syntax is deprecated: instead, add `: Unit =` to explicitly declare `testMetricOnDSv2`'s return type
[error] private def testMetricOnDSv2(func: String => Unit, checker: Map[Long, String] => Unit) {
[error] ^
[error] /home/runner/work/spark/spark/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/InMemoryTableMetricSuite.scala:44:90: procedure syntax is deprecated: instead, add `: Unit =` to explicitly declare `testMetricOnDSv2`'s return type
[error] private def testMetricOnDSv2(func: String => Unit, checker: Map[Long, String] => Unit) {
[error] ^
[warn] 100 warnings found
[error] two errors found
[error] (sql / Test / compileIncremental) Compilation failed
[error] Total time: 579 s (09:39), completed Jul 21, 2021 4:14:26 AM
```
### Why are the changes needed?
To make the build compatible with Scala 2.13 in Spark.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
Manually tested:
```bash
./dev/change-scala-version.sh 2.13
./build/mvn -DskipTests -Phive-2.3 -Phive clean package -Pscala-2.13
```
Closes#33452 from HyukjinKwon/SPARK-36030.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 99006e515b)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
We add the interface for DS v2 metrics in SPARK-34366. It is only added for reading path, though. This patch extends the metrics interface to writing path.
### Why are the changes needed?
Complete DS v2 metrics interface support in writing path.
### Does this PR introduce _any_ user-facing change?
No. For developer, yes, as this adds metrics support at DS v2 writing path.
### How was this patch tested?
Added test.
Closes#33239 from viirya/v2-write-metrics.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit 2653201b0a)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
1. add "closeStreams" to FileAppender and RollingFileAppender
2. set "closeStreams" to "true" in ExecutorRunner
### Why are the changes needed?
The executor will hang when due disk full or other exceptions which happened in writting to outputStream: the root cause is the "inputStream" is not closed after the error happens:
1. ExecutorRunner creates two files appenders for pipe: one for stdout, one for stderr
2. FileAppender.appendStreamToFile exits the loop when writing to outputStream
3. FileAppender closes the outputStream, but left the inputStream which refers the pipe's stdout and stderr opened
4. The executor will hang when printing the log message if the pipe is full (no one consume the outputs)
5. From the driver side, you can see the task can't be completed for ever
With this fix, the step 4 will throw an exception, the driver can catch up the exception and reschedule the failed task to other executors.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add new tests for the "closeStreams" in FileAppenderSuite
Closes#33263 from jhu-chang/SPARK-35027.
Authored-by: Jie <gt.hu.chang@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
(cherry picked from commit 1a8c6755a1)
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR documents a new feature "native support of session window" into Structured Streaming guide doc.
Screenshots are following:
### Why are the changes needed?
This change is needed to explain a new feature to the end users.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Documentation changes.
Closes#33433 from HeartSaVioR/SPARK-36172.
Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
(cherry picked from commit 0eb31a06d6)
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Add `as_ordered`/`as_unordered` to `CategoricalAccessor` and `CategoricalIndex`.
### Why are the changes needed?
We should implement `as_ordered`/`as_unordered` in `CategoricalAccessor` and `CategoricalIndex` yet.
### Does this PR introduce _any_ user-facing change?
Yes, users will be able to use `as_ordered`/`as_unordered`.
### How was this patch tested?
Added some tests.
Closes#33400 from ueshin/issues/SPARK-36186/as_ordered_unordered.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
(cherry picked from commit 376fadc89c)
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
The current implement of `Sequence` expression not support step by days for dates.
```
spark-sql> select sequence(date'2021-07-01', date'2021-07-10', interval '3' day);
Error in query: cannot resolve 'sequence(DATE '2021-07-01', DATE '2021-07-10', INTERVAL '3' DAY)' due to data type mismatch:
sequence uses the wrong parameter type. The parameter type must conform to:
1. The start and stop expressions must resolve to the same type.
2. If start and stop expressions resolve to the 'date' or 'timestamp' type
then the step expression must resolve to the 'interval' or
'interval year to month' or 'interval day to second' type,
otherwise to the same type as the start and stop expressions.
; line 1 pos 7;
'Project [unresolvedalias(sequence(2021-07-01, 2021-07-10, Some(INTERVAL '3' DAY), Some(Europe/Moscow)), None)]
+- OneRowRelation
```
### Why are the changes needed?
`DayTimeInterval` has day granularity should as step for dates.
### Does this PR introduce _any_ user-facing change?
'Yes'.
Sequence expression will supports step by `DayTimeInterval` has day granularity for dates.
### How was this patch tested?
New tests.
Closes#33439 from beliefer/SPARK-36222.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit c0d84e6cf1)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Preserve the insertion order of columns in Dataset.withColumns
### Why are the changes needed?
It is the expected behavior. We preserve insertion order in all other places.
### Does this PR introduce _any_ user-facing change?
No. Currently Dataset.withColumns is not actually used anywhere to insert more than one column. This change is to make sure it behaves as expected when it is used for that purpose in future.
### How was this patch tested?
Added test in DatasetSuite
Closes#33423 from koertkuipers/feat-withcolumns-preserve-order.
Authored-by: Koert Kuipers <koert@tresata.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit bf680bf25a)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Forces the selectivity estimate for null-based filters to be in the range `[0,1]`.
### Why are the changes needed?
I noticed in a few TPC-DS query tests that the column statistic null count can be higher than the table statistic row count. In the current implementation, the selectivity estimate for `IsNotNull` is negative.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#33286 from karenfeng/bound-selectivity-est.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit ddc61e62b9)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR follows https://github.com/apache/spark/pull/33299 and implement `prettyName` for `MakeTimestampNTZ` and `MakeTimestampLTZ` based on the discussion show below
https://github.com/apache/spark/pull/33299/files#r668423810
### Why are the changes needed?
This PR fix the incorrect alias usecase.
### Does this PR introduce _any_ user-facing change?
'No'.
Modifications are transparent to users.
### How was this patch tested?
Jenkins test.
Closes#33430 from beliefer/SPARK-36046-followup.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit 033a5731b4)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
When inner field have wrong schema filed name should check field name too.
### Why are the changes needed?
Early check early faield
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#33409 from AngersZhuuuu/SPARK-36201.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 251885772d)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
* Add non-empty partition check in `CustomShuffleReaderExec`
* Make sure `OptimizeLocalShuffleReader` doesn't return empty partition
### Why are the changes needed?
Since SPARK-32083, AQE coalesce always return at least one partition, it should be robust to add non-empty check in `CustomShuffleReaderExec`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
not need
Closes#33431 from ulysses-you/non-empty-partition.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit b70c25881c)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR documents about unicode literals added in SPARK-34051 (#31096) and a past PR in `sql-ref-literals.md`.
### Why are the changes needed?
Notice users about the literals.
### Does this PR introduce _any_ user-facing change?
Yes, but just add a sentence.
### How was this patch tested?
Built the document and confirmed the result.
```
SKIP_API=1 bundle exec jekyll build
```
Closes#33434 from sarutak/unicode-literal-doc.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit ba1294ea5a)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is one of the patches for SPIP SPARK-30602 which is needed for push-based shuffle.
### Summary of the change:
When Executor registers with Shuffle Service, it will encode the merged shuffle dir created and also the application attemptId into the ShuffleManagerMeta into Json. Then in Shuffle Service, it will decode the Json string and get the correct merged shuffle dir and also the attemptId. If the registration comes from a newer attempt, the merged shuffle information will be updated to store the information from the newer attempt.
This PR also refactored the management of the merged shuffle information to avoid concurrency issues.
### Why are the changes needed?
Refer to the SPIP in SPARK-30602.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit tests.
The reference PR with the consolidated changes covering the complete implementation is also provided in SPARK-30602.
We have already verified the functionality and the improved performance as documented in the SPIP doc.
Closes#33078 from zhouyejoe/SPARK-35546.
Authored-by: Ye Zhou <yezhou@linkedin.com>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
(cherry picked from commit c77acf0bbc)
Signed-off-by: Mridul Muralidharan <mridulatgmail.com>
### What changes were proposed in this pull request?
Test is flaky (https://github.com/apache/spark/runs/3109815586):
```
Traceback (most recent call last):
File "/__w/spark/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 391, in test_parameter_convergence
eventually(condition, catch_assertions=True)
File "/__w/spark/spark/python/pyspark/testing/utils.py", line 91, in eventually
raise lastValue
File "/__w/spark/spark/python/pyspark/testing/utils.py", line 82, in eventually
lastValue = condition()
File "/__w/spark/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 387, in condition
self.assertEqual(len(model_weights), len(batches))
AssertionError: 9 != 10
```
Should probably increase timeout
### Why are the changes needed?
To avoid flakiness in the test.
### Does this PR introduce _any_ user-facing change?
Nope, dev-only.
### How was this patch tested?
CI should test it out.
Closes#33427 from HyukjinKwon/SPARK-36216.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit d6b974f8ce)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Support TimestampNTZType in SparkGetColumnsOperation
### Why are the changes needed?
TimestampNTZType coverage
### Does this PR introduce _any_ user-facing change?
yes, jdbc end-users will be aware of TimestampNTZType
### How was this patch tested?
add new test
Closes#33393 from yaooqinn/SPARK-36179.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 0c76fb9c01)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a backport of #33377.
Revisit and manage `InternalField` in more places.
### Why are the changes needed?
There are other places we can manage `InternalField`, and we can keep extension dtypes or `CategoricalDtype`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added some tests.
Closes#33384 from ueshin/issues/SPARK-36167/3.2/internal_field.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Support comparison between a Categorical and a scalar.
There are 3 main changes:
- Modify `==` and `!=` from comparing **codes** of the Categorical to the scalar to comparing **actual values** of the Categorical to the scalar.
- Support `<`, `<=`, `>`, `>=` between a Categorical and a scalar.
- TypeError message fix.
### Why are the changes needed?
pandas supports comparison between a Categorical and a scalar, we should follow pandas' behaviors.
### Does this PR introduce _any_ user-facing change?
Yes.
Before:
```py
>>> import pyspark.pandas as ps
>>> import pandas as pd
>>> from pandas.api.types import CategoricalDtype
>>> pser = pd.Series(pd.Categorical([1, 2, 3], categories=[3, 2, 1], ordered=True))
>>> psser = ps.from_pandas(pser)
>>> psser == 2
0 True
1 False
2 False
dtype: bool
>>> psser <= 1
Traceback (most recent call last):
...
NotImplementedError: <= can not be applied to categoricals.
```
After:
```py
>>> import pyspark.pandas as ps
>>> import pandas as pd
>>> from pandas.api.types import CategoricalDtype
>>> pser = pd.Series(pd.Categorical([1, 2, 3], categories=[3, 2, 1], ordered=True))
>>> psser = ps.from_pandas(pser)
>>> psser == 2
0 False
1 True
2 False
dtype: bool
>>> psser <= 1
0 True
1 True
2 True
dtype: bool
```
### How was this patch tested?
Unit tests.
Closes#33373 from xinrong-databricks/categorical_eq.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
(cherry picked from commit 8dd43351d5)
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This is a followup PR for SPARK-36166 (#33411), which adds `BLOCK_SCALA_VERSION` to `sparktestssupport/__init__.py`.
### Why are the changes needed?
The following command fails due to the definition is missing.
```
SCALA_PROFILE=scala2.12 dev/run-tests.py
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
The command shown above works.
Closes#33421 from sarutak/followup-SPARK-36166.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit c7ccc602db)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The current implement of `TimeWindow` only supports `TimestampType`. Spark added a new type `TimestampNTZType`, so we should support `TimestampNTZType` in expression `TimeWindow`.
### Why are the changes needed?
`TimestampNTZType` similar to `TimestampType`, we should support `TimestampNTZType` in expression `TimeWindow`.
### Does this PR introduce _any_ user-facing change?
'Yes'.
`TimeWindow` will accepts `TimestampNTZType`.
### How was this patch tested?
New tests.
Closes#33341 from beliefer/SPARK-36091.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit 7aa01798c5)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
The `DataFrame.to_csv` has `mode` arguments both in pandas and pandas API on Spark.
However, pandas allows the string "w", "w+", "a", "a+" where as pandas-on-Spark allows "append", "overwrite", "ignore", "error" or "errorifexists".
We should map them while `mode` can still accept the existing parameters("append", "overwrite", "ignore", "error" or "errorifexists") as well.
### Why are the changes needed?
APIs in pandas-on-Spark should follows the behavior of pandas for preventing the existing pandas code break.
### Does this PR introduce _any_ user-facing change?
`DataFrame.to_csv` now can accept "w", "w+", "a", "a+" as well, same as pandas.
### How was this patch tested?
Add the unit test and manually write the file with the new acceptable strings.
Closes#33414 from itholic/SPARK-35806.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 2f42afc53a)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Updating the pyspark sql readwriter documentation to the level of detail provided by the scala documentation
### Why are the changes needed?
documentation clarity
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Only documentation change
Closes#33394 from dominikgehl/feature/SPARK-36181.
Authored-by: Dominik Gehl <dog@open.ch>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 2ef8ced27a)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The pyspark.sql.catalog APIs were missing from the documentation. PR fixes this omission.
### Why are the changes needed?
Documentation consistency
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Documentation change only.
Closes#33392 from dominikgehl/feature/SPARK-36178.
Authored-by: Dominik Gehl <dog@open.ch>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit fe4db74da4)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is more a cleanup. It removes unused `sync-branch` id in some steps, and use `set-env` instead of `set-output` to set an env.
This can be backported to branch-3.2 too.
### Why are the changes needed?
Cleanup.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
CI in this PR should test it out.
Closes#33412 from HyukjinKwon/minor-cleanup.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit c92790a101)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is a simple followup from https://github.com/apache/spark/pull/33376:
- It simplifies a bit by removing the default Scala version in the testing script (so we don't have to change here in the future when we change the Scala default version).
- Call `change-scala-version.sh` script (when `SCALA_PROFILE` is explicitly specified)
### Why are the changes needed?
More refactoring. In addition, this change will be used at https://github.com/apache/spark/pull/33410
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
CI in this PR should test it out.
Closes#33411 from HyukjinKwon/SPARK-36166.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 8ee199ef42)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
RemoveRedundantAliases may change DataWritingCommand's parameter's attribute name.
In the UT's case before RemoveRedundantAliases the partitionColumns is `CAL_DT`, and change by RemoveRedundantAliases and change to `cal_dt` then case the error case
### Why are the changes needed?
Fix bug
### Does this PR introduce _any_ user-facing change?
For below SQL case
```
sql("create table t1(cal_dt date) using parquet")
sql("insert into t1 values (date'2021-06-27'),(date'2021-06-28'),(date'2021-06-29'),(date'2021-06-30')")
sql("create view t1_v as select * from t1")
sql("CREATE TABLE t2 USING PARQUET PARTITIONED BY (CAL_DT) AS SELECT 1 AS FLAG,CAL_DT FROM t1_v WHERE CAL_DT BETWEEN '2021-06-27' AND '2021-06-28'")
sql("INSERT INTO t2 SELECT 2 AS FLAG,CAL_DT FROM t1_v WHERE CAL_DT BETWEEN '2021-06-29' AND '2021-06-30'")
```
Before this pr
```
sql("SELECT * FROM t2 WHERE CAL_DT BETWEEN '2021-06-29' AND '2021-06-30'").show
+----+------+
|FLAG|CAL_DT|
+----+------+
+----+------+
sql("SELECT * FROM t2 ").show
+----+----------+
|FLAG| CAL_DT|
+----+----------+
| 1|2021-06-27|
| 1|2021-06-28|
+----+----------+
```
After this pr
```
sql("SELECT * FROM t2 WHERE CAL_DT BETWEEN '2021-06-29' AND '2021-06-30'").show
+----+------+
|FLAG|CAL_DT|
+----+------+
| 2|2021-06-29|
| 2|2021-06-30|
+----+------+
sql("SELECT * FROM t2 ").show
+----+----------+
|FLAG| CAL_DT|
+----+----------+
| 1|2021-06-27|
| 1|2021-06-28|
| 2|2021-06-29|
| 2|2021-06-30|
+----+----------+
```
### How was this patch tested?
Added UT
Closes#33324 from AngersZhuuuu/SPARK-36093.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 313f3c5460)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
A hive partition can have different `PartitionDesc`s from `TableDesc` for describing Serde/InputFormatClass/OutputFormatClass, for a hive partitioned table, we shall respect those in `PartitionDesc`.
### Why are the changes needed?
in many cases, that Spark reads hive tables could result in surprise because of this issue.
### Does this PR introduce _any_ user-facing change?
yes, hive partition table that contains different serde/input/output could be recognized by Spark
### How was this patch tested?
new test added
Closes#33406 from yaooqinn/SPARK-36197.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Kent Yao <yao@apache.org>
(cherry picked from commit ef80356614)
Signed-off-by: Kent Yao <yao@apache.org>
### What changes were proposed in this pull request?
Currently, two AQE rules `OptimizeLocalShuffleReader` and `OptimizeSkewedJoin` run `EnsureRequirements` at the end to check if there are extra shuffles in the optimized plan and revert the optimization if extra shuffles are introduced.
This PR proposes to run `ValidateRequirements` instead, which is much simpler than `EnsureRequirements`. This PR also moves this check to `AdaptiveSparkPlanExec`, so that it's centralized instead of in each rule. After centralization, the batch name of optimizing the final stage is the same as normal stages, which makes more sense.
### Why are the changes needed?
`EnsureRequirements` is a big rule and even contains optimizations (remove unnecessary shuffles). `ValidateRequirements` is much faster to run and can avoid potential bugs as it has no optimization and is a pure check.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests.
Closes#33396 from cloud-fan/aqe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 8396a70ddc)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
According to the discussion on https://github.com/apache/spark/pull/32283 , this PR aims to limit the feature of SPARK-34674 to K8s environment only.
### Why are the changes needed?
To reduce the behavior change in non-K8s environment.
### Does this PR introduce _any_ user-facing change?
The change behavior is consistent with 3.1.1 and older Spark releases.
### How was this patch tested?
N/A
Closes#33403 from dongjoon-hyun/SPARK-36193.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit fd3e9ce0b9)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to upgrade scalatest-maven-plugin to version 2.0.2.
### Why are the changes needed?
2.0.2 supports build on JDK 11 officially.
- f45ce192f3
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CIs.
Closes#33408 from williamhyun/SMP.
Authored-by: William Hyun <william@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit df8bae0689)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
The `broadcast` functions in `pyspark.pandas` is duplicated to `DataFrame.spark.hint` with `"broadcast"`.
```python
# The below 2 lines are the same
df.spark.hint("broadcast")
ps.broadcast(df)
```
So, we should remove `broadcast` in the future, and show deprecation warning for now.
### Why are the changes needed?
For deduplication of functions
### Does this PR introduce _any_ user-facing change?
They see the deprecation warning when using `broadcast` in `pyspark.pandas`.
```python
>>> ps.broadcast(df)
FutureWarning: `broadcast` has been deprecated and will be removed in a future version. use `DataFrame.spark.hint` with 'broadcast' for `name` parameter instead.
warnings.warn(
```
### How was this patch tested?
Manually check the warning message and see the build passed.
Closes#33379 from itholic/SPARK-35810.
Lead-authored-by: itholic <haejoon.lee@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Co-authored-by: Haejoon Lee <44108233+itholic@users.noreply.github.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 67e6120a85)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to skip UNIDOC generation in PySpark GHA job.
### Why are the changes needed?
PySpark GHA jobs do not need to generate Java/Scala doc. This will save about 13 minutes in total.
-https://github.com/apache/spark/runs/3098268973?check_suite_focus=true
```
...
========================================================================
Building Unidoc API Documentation
========================================================================
[info] Building Spark unidoc using SBT with these arguments: -Phadoop-3.2 -Phive-2.3 -Pscala-2.12 -Phive-thriftserver -Pmesos -Pdocker-integration-tests -Phive -Pkinesis-asl -Pspark-ganglia-lgpl -Pkubernetes -Phadoop-cloud -Pyarn unidoc
...
[info] Main Java API documentation successful.
[success] Total time: 192 s (03:12), completed Jul 18, 2021 6:08:40 PM
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the GHA.
Closes#33407 from williamhyun/SKIP_UNIDOC.
Authored-by: William Hyun <william@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit c336f73ccd)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add reference to kubernetes-client's version
### Why are the changes needed?
Running Spark on Kubernetes potentially has upper limitation of Kubernetes version.
I think it is better for users to notice it because Kubernetes update speed is so fast that users tends to run Spark Jobs on unsupported version.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
SKIP_API=1 bundle exec jekyll build
Closes#33255 from yoda-mon/add-reference-kubernetes-client.
Authored-by: yoda-mon <yodal@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit eea69c122f)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
The current implement of `Sequence` accept `TimestampType`, `DateType` and `IntegralType`. This PR will let `Sequence` accepts `TimestampNTZType`.
### Why are the changes needed?
We can generate sequence for timestamp without time zone.
### Does this PR introduce _any_ user-facing change?
'Yes'.
This PR will let `Sequence` accepts `TimestampNTZType`.
### How was this patch tested?
New tests.
Closes#33360 from beliefer/SPARK-36090.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 42275bb20d)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR aims to set `MaxMetaspaceSize` to `2g` because it's increasing the native memory consumption unlimitedly by default. The unlimited increasing memory causes GitHub Action flakiness. The value I observed during `hive` module test was over 1.8G and growing.
- https://docs.oracle.com/javase/10/gctuning/other-considerations.htm#JSGCT-GUID-BFB89453-60C0-42AC-81CA-87D59B0ACE2E
> Starting with JDK 8, the permanent generation was removed and the class metadata is allocated in native memory. The amount of native memory that can be used for class metadata is by default unlimited. Use the option -XX:MaxMetaspaceSize to put an upper limit on the amount of native memory used for class metadata.
In addition, I increased the following memory limit to 4g consistently from two places.
```xml
- <jvmArg>-Xms2048m</jvmArg>
- <jvmArg>-Xmx2048m</jvmArg>
+ <jvmArg>-Xms4g</jvmArg>
+ <jvmArg>-Xmx4g</jvmArg>
```
```scala
- javaOptions += "-Xmx3g",
+ javaOptions ++= "-Xmx4g -XX:MaxMetaspaceSize=2g".split(" ").toSeq,
```
### Why are the changes needed?
This will reduce the flakiness in CI environment by limiting the memory usage explicitly.
When we limit it with `1g`, Hive module fails with `OOM` like the following.
```
java.lang.OutOfMemoryError: Metaspace
Error: Exception in thread "dispatcher-event-loop-110" java.lang.OutOfMemoryError: Metaspace
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CIs.
Closes#33405 from dongjoon-hyun/SPARK-36195.
Lead-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Co-authored-by: Kyle Bendickson <kbendickson@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit d7df7a805f)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR changes the behavior of the quoted interval literals like `SELECT INTERVAL '1 year 2 month'` to be converted to ANSI interval types.
### Why are the changes needed?
The tnit-to-unit interval literals and the unit list interval literals are converted to ANSI interval types but quoted interval literals are still converted to CalendarIntervalType.
```
-- Unit list interval literals
spark-sql> select interval 1 year 2 month;
1-2
-- Quoted interval literals
spark-sql> select interval '1 year 2 month';
1 years 2 months
```
### Does this PR introduce _any_ user-facing change?
Yes but the following sentence in `sql-migration-guide.md` seems to cover this change.
```
- In Spark 3.2, the unit list interval literals can not mix year-month fields (YEAR and MONTH) and day-time fields (WEEK, DAY, ..., MICROSECOND).
For example, `INTERVAL 1 day 1 hour` is invalid in Spark 3.2. In Spark 3.1 and earlier,
there is no such limitation and the literal returns value of `CalendarIntervalType`.
To restore the behavior before Spark 3.2, you can set `spark.sql.legacy.interval.enabled` to `true`.
```
### How was this patch tested?
Modified existing tests and add new tests.
Closes#33380 from sarutak/fix-interval-constructor.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 71ea25d4f5)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
