[SPARK-34581][SQL] Don't optimize out grouping expressions from aggregate expressions without aggregate function #31913

peter-toth · 2021-03-21T09:37:43Z

What changes were proposed in this pull request?

This PR:

Adds a new expression GroupingExprRef that can be used in aggregate expressions of Aggregate nodes to refer grouping expressions by index. These expressions capture the data type and nullability of the referred grouping expression.
Adds a new rule EnforceGroupingReferencesInAggregates that inserts the references in the beginning of the optimization phase.
Adds a new rule UpdateGroupingExprRefNullability to update nullability of GroupingExprRef expressions as nullability of referred grouping expression can change during optimization.

Why are the changes needed?

If aggregate expressions (without aggregate functions) in an Aggregate node are complex then the Optimizer can optimize out grouping expressions from them and so making aggregate expressions invalid.

Here is a simple example:

SELECT not(t.id IS NULL) , count(*)
FROM t
GROUP BY t.id IS NULL

In this case the BooleanSimplification rule does this:

=== Applying Rule org.apache.spark.sql.catalyst.optimizer.BooleanSimplification ===
!Aggregate [isnull(id#222)], [NOT isnull(id#222) AS (NOT (id IS NULL))#226, count(1) AS c#224L]   Aggregate [isnull(id#222)], [isnotnull(id#222) AS (NOT (id IS NULL))#226, count(1) AS c#224L]
 +- Project [value#219 AS id#222]                                                                 +- Project [value#219 AS id#222]
    +- LocalRelation [value#219]                                                                     +- LocalRelation [value#219]

where NOT isnull(id#222) is optimized to isnotnull(id#222) and so it no longer refers to any grouping expression.

Before this PR:

== Optimized Logical Plan ==
Aggregate [isnull(id#222)], [isnotnull(id#222) AS (NOT (id IS NULL))#234, count(1) AS c#232L]
+- Project [value#219 AS id#222]
   +- LocalRelation [value#219]

and running the query throws an error:

Couldn't find id#222 in [isnull(id#222)#230,count(1)#226L]
java.lang.IllegalStateException: Couldn't find id#222 in [isnull(id#222)#230,count(1)#226L]

After this PR:

== Optimized Logical Plan ==
Aggregate [isnull(id#222)], [NOT groupingexprref(0) AS (NOT (id IS NULL))#234, count(1) AS c#232L]
+- Project [value#219 AS id#222]
   +- LocalRelation [value#219]

and the query works.

Does this PR introduce any user-facing change?

Yes, the query works.

How was this patch tested?

Added new UT.

…gate expressions

peter-toth · 2021-03-21T10:19:12Z

The Not(IsNull(e)) => IsNotNull(e) and Not(IsNotNull(e)) => IsNull(e) optimizations were introduced in #25878 so the example query is a regression from 2.4 to 3.x, but I'm not sure that the issue doesn't come up in Spark 2.4 with other optimizations.

SparkQA · 2021-03-21T10:46:01Z

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SparkQA · 2021-03-21T14:23:06Z

Test build #136306 has finished for PR 31913 at commit ae1186f.

This patch passes all tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
case class GroupingExpression(child: Expression) extends UnaryExpression

SparkQA · 2021-03-21T14:50:43Z

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SparkQA · 2021-03-21T18:22:50Z

Test build #136310 has finished for PR 31913 at commit 5ab9f75.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

dongjoon-hyun

Thanks, @peter-toth . Could you update thee PR descritpion with the following?

- then NOT isnull(id#222) is optimized to isnull(id#222) 
+ then NOT isnull(id#222) is optimized to isnotnull(id#222)

maropu · 2021-03-22T00:55:10Z

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala

+        val wrappedAggregateExpressions =
+          agg.aggregateExpressions.map(wrapGroupingExpression(_).asInstanceOf[NamedExpression])
+        agg.copy(aggregateExpressions =
+          buildCleanedProjectList(p.projectList, wrappedAggregateExpressions))


I think this issue is not related to CollapseProject. For example, we can reproduce it like this;

$ ./bin/spark-shell --conf spark.sql.optimizer.excludedRules=org.apache.spark.sql.catalyst.optimizer.CollapseProject Seq(Some(1), None).toDF("id").createOrReplaceTempView("t") val df = sql(""" SELECT NOT(t.id IS NULL) AS X, count(*) AS c FROM t GROUP BY t.id IS NULL """) df.show()

The query fails because BooleanSimplification applys illegal expr transformation to break the group-by constraint (that is, group-by columns must appear in aggregate exprs). In the ohter DBMS-like systems (e.g., PostgreSQL), the transformed query fails like this;

postgres=# SELECT t.id IS NOT NULL AS X, count(*) AS c FROM t GROUP BY t.id IS NULL; ERROR: column "t.id" must appear in the GROUP BY clause or be used in an aggregate function LINE 1: SELECT t.id IS NOT NULL AS X, count(*) AS c FROM t GROUP BY ...

I'm currently not sure that this issue can happen only in BooleanSimplification though, I think we need a general solution to fix this kind of the illegal transformation instead of band-aid fixes.

Ahh you are right, even the initial query can contain terms in which Spark should keep grouping expressions. I think I just inserted the GroupingExpression wrapper around id IS NULL at the wrong place, it should happen during analysis. Will look into this today.

Thanks for the test case @maropu.
I moved the logic to a new rule and simplified the test case in: 2293fd4 and updated the PR description.

maropu · 2021-03-22T00:59:10Z

Anyway, nice catch, @peter-toth !

peter-toth · 2021-03-22T08:34:20Z

Thanks, @peter-toth . Could you update thee PR descritpion with the following?
- then NOT isnull(id#222) is optimized to isnull(id#222) 
+ then NOT isnull(id#222) is optimized to isnotnull(id#222)

Thanks @dongjoon-hyun, I updated the description.

SparkQA · 2021-03-22T09:14:17Z

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SparkQA · 2021-03-22T12:58:58Z

Test build #136337 has finished for PR 31913 at commit 3de19ca.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

# Conflicts: # sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala

SparkQA · 2021-03-23T12:22:48Z

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maropu · 2021-03-23T13:29:47Z

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/grouping.scala

+ * Wrapper expression to avoid further optimizations between the parent and child of this
+ * expression.
+ */
+case class GroupingExpression(child: Expression) extends UnaryExpression {


This is a tagging-like expr (TaggingExpression) ?

Thanks, I think it is. Changed in 6e05f14

SparkQA · 2021-03-23T13:38:07Z

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SparkQA · 2021-03-23T14:14:31Z

Test build #136410 has started for PR 31913 at commit 6e05f14.

SparkQA · 2021-03-23T16:39:03Z

Test build #136400 has finished for PR 31913 at commit 04e61c5.

This patch passes all tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
case class WriteToStream(
case class WriteToStreamStatement(

SparkQA · 2021-03-23T17:36:38Z

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maropu · 2021-03-24T04:45:57Z

sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala

+    withTempView("t") {
+      Seq[Integer](null, 1, 2, 3, null).toDF("id").createOrReplaceTempView("t")
+
+      val df = spark.sql(


If this is a test to check if a query work correctly or not, could you move this test into SQLQueryTestSuite?

It is, moved in 09f1a85

maropu · 2021-03-24T04:46:49Z

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/finishAnalysis.scala

@@ -66,6 +66,34 @@ object RewriteNonCorrelatedExists extends Rule[LogicalPlan] {
  }
 }

+/**
+ * Wrap complex grouping expression in aggregate expressions without aggregate function into


What does "complex" means in this context?

Described in f46b89d

maropu · 2021-03-24T04:48:19Z

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/finishAnalysis.scala

+ * parent.
+ *
+ * This is required as further optimizations could change the grouping expression and so make the
+ * aggregate expression invalid.


Could you add a simple query example to explain about the case above?

Sure, added in f46b89d

SparkQA · 2021-04-15T12:34:32Z

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SparkQA · 2021-04-15T15:55:12Z

Test build #137417 has finished for PR 31913 at commit 34f0439.

This patch passes all tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
public class VectorizedBLAS extends F2jBLAS
class PandasOnSparkFrameMethods(object):
class PandasOnSparkSeriesMethods(object):
class PandasOnSparkPlotAccessor(PandasObject):
class PandasOnSparkBarPlot(PandasBarPlot, TopNPlotBase):
class PandasOnSparkBoxPlot(PandasBoxPlot, BoxPlotBase):
class PandasOnSparkHistPlot(PandasHistPlot, HistogramPlotBase):
class PandasOnSparkPiePlot(PandasPiePlot, TopNPlotBase):
class PandasOnSparkAreaPlot(PandasAreaPlot, SampledPlotBase):
class PandasOnSparkLinePlot(PandasLinePlot, SampledPlotBase):
class PandasOnSparkBarhPlot(PandasBarhPlot, TopNPlotBase):
class PandasOnSparkScatterPlot(PandasScatterPlot, TopNPlotBase):
class PandasOnSparkKdePlot(PandasKdePlot, KdePlotBase):
class PandasOnSparkUsageLogger(object):
trait AnalysisOnlyCommand extends Command
implicit class MetadataColumnHelper(attr: Attribute)
case class WriteToDataSourceV2(
case class WriteToMicroBatchDataSource(

cloud-fan · 2021-04-15T15:59:05Z

cc @hvanhovell as well

SparkQA · 2021-04-15T16:05:16Z

Test build #137416 has finished for PR 31913 at commit 72c173b.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

github-actions · 2021-04-17T16:23:53Z

Test build #758850368 for PR 31913 at commit fb3a19d.

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SparkQA · 2021-04-17T21:16:38Z

Test build #137509 has finished for PR 31913 at commit fb3a19d.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

cloud-fan · 2021-04-19T04:58:40Z

thanks, merging to master!

SparkQA · 2021-04-19T07:10:45Z

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peter-toth · 2021-04-19T08:51:22Z

Thanks @cloud-fan, @maropu, @dongjoon-hyun for the review.

I will try look into the idea of #31913 (comment) and maybe open a PR this week.

SparkQA · 2021-04-19T09:51:23Z

Test build #137552 has finished for PR 31913 at commit 34f0439.

This patch passes all tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
public class VectorizedBLAS extends F2jBLAS
class PandasOnSparkFrameMethods(object):
class PandasOnSparkSeriesMethods(object):
class PandasOnSparkPlotAccessor(PandasObject):
class PandasOnSparkBarPlot(PandasBarPlot, TopNPlotBase):
class PandasOnSparkBoxPlot(PandasBoxPlot, BoxPlotBase):
class PandasOnSparkHistPlot(PandasHistPlot, HistogramPlotBase):
class PandasOnSparkPiePlot(PandasPiePlot, TopNPlotBase):
class PandasOnSparkAreaPlot(PandasAreaPlot, SampledPlotBase):
class PandasOnSparkLinePlot(PandasLinePlot, SampledPlotBase):
class PandasOnSparkBarhPlot(PandasBarhPlot, TopNPlotBase):
class PandasOnSparkScatterPlot(PandasScatterPlot, TopNPlotBase):
class PandasOnSparkKdePlot(PandasKdePlot, KdePlotBase):
class PandasOnSparkUsageLogger(object):
trait AnalysisOnlyCommand extends Command
implicit class MetadataColumnHelper(attr: Attribute)
case class WriteToDataSourceV2(
case class WriteToMicroBatchDataSource(

SparkQA · 2021-04-19T10:26:39Z

Test build #137581 has finished for PR 31913 at commit 34f0439.

This patch passes all tests.
This patch does not merge cleanly.
This patch adds the following public classes (experimental):
public class VectorizedBLAS extends F2jBLAS
class PandasOnSparkFrameMethods(object):
class PandasOnSparkSeriesMethods(object):
class PandasOnSparkPlotAccessor(PandasObject):
class PandasOnSparkBarPlot(PandasBarPlot, TopNPlotBase):
class PandasOnSparkBoxPlot(PandasBoxPlot, BoxPlotBase):
class PandasOnSparkHistPlot(PandasHistPlot, HistogramPlotBase):
class PandasOnSparkPiePlot(PandasPiePlot, TopNPlotBase):
class PandasOnSparkAreaPlot(PandasAreaPlot, SampledPlotBase):
class PandasOnSparkLinePlot(PandasLinePlot, SampledPlotBase):
class PandasOnSparkBarhPlot(PandasBarhPlot, TopNPlotBase):
class PandasOnSparkScatterPlot(PandasScatterPlot, TopNPlotBase):
class PandasOnSparkKdePlot(PandasKdePlot, KdePlotBase):
class PandasOnSparkUsageLogger(object):
trait AnalysisOnlyCommand extends Command
implicit class MetadataColumnHelper(attr: Attribute)
case class WriteToDataSourceV2(
case class WriteToMicroBatchDataSource(

SparkQA · 2021-04-19T10:28:27Z

Test build #137561 has finished for PR 31913 at commit fb3a19d.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

maropu · 2021-04-20T00:04:47Z

late lgtm. Nice fix, @peter-toth ~

sigmod · 2021-04-23T01:27:21Z

Posted a message on the git commit page and copy it here.

Hi @peter-toth

@cloud-fan @maryannxue @rednaxelafx and I had an offline discussion about this PR.

Here's a summary:

(1) This PR would bring complexities when we add more optimization around grouping columns down the road. E.g., if we want to reorder grouping columns in order to reuse partitioning properties or optimize away certain grouping columns, we would need to re-generate the ordinals in grouping refs.
(2) It seems safe to pull out non-attribute grouping expressions from an Aggregate and put them in a Project below the Aggregate. Then, grouping expressions in the Aggregate are only AttributeReferences. The rest of Optimizer rules can still match the Aggregate as before. We do need to pay attention to rules that collapse Aggregate and Project. In the planner, we can combine Aggregate and Project, so that the physical plan doesn't change.

Thus, would you mind reverting this PR and try the proposal in (2)? Thanks in advance!

Best,
Yingyi

cloud-fan · 2021-04-23T04:41:29Z

I think pulling out grouping expressions into a project is a good idea. By-name matching is more robust than by-ordinal matching. It's a pity that I didn't come up with this idea while reviewing this PR. @peter-toth what do you think about the new idea?

peter-toth · 2021-04-23T07:27:54Z

@sigmod, @cloud-fan, I'm ok with reverting this change and try the new proposal if it better fits the roadmap. Probably the new rule should be similar to PullOutNondeterministic. Do you have a PR ready or shall I open a new one?

sigmod · 2021-04-23T07:30:54Z

Probably the new rule should be similar to PullOutNondeterministic.

Yeah, exactly!

Do you have a PR ready or shall I open a new one?

We don't have one. So please open a new one. Thanks a lot, Peter!

cloud-fan · 2021-04-23T07:54:16Z

I think the new idea should also be simpler:

CollapseProject only collpase Project + Aggregate, but not Aggregate + Project. No change is needed. For example:

scala> spark.range(10).select('id + 1 as 'a).groupBy('a).count().explain(true)
== Parsed Logical Plan ==
'Aggregate ['a], ['a, count(1) AS count#17L]
+- Project [(id#11L + cast(1 as bigint)) AS a#13L]
   +- Range (0, 10, step=1, splits=Some(1))

== Analyzed Logical Plan ==
a: bigint, count: bigint
Aggregate [a#13L], [a#13L, count(1) AS count#17L]
+- Project [(id#11L + cast(1 as bigint)) AS a#13L]
   +- Range (0, 10, step=1, splits=Some(1))

== Optimized Logical Plan ==
Aggregate [a#13L], [a#13L, count(1) AS count#17L]
+- Project [(id#11L + 1) AS a#13L]
   +- Range (0, 10, step=1, splits=Some(1))

== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[a#13L], functions=[count(1)], output=[a#13L, count#17L])
   +- HashAggregate(keys=[a#13L], functions=[partial_count(1)], output=[a#13L, count#21L])
      +- Project [(id#11L + 1) AS a#13L]
         +- Range (0, 10, step=1, splits=1)

PhysicalAggregation needs update to collapse Project under Aggregate.

Anyway, I'm reverting this PR, @peter-toth look forward to your new PR!

peter-toth · 2021-04-26T16:18:13Z

I've started working on the new PR. I'm a bit busy this week, but will try to open it by the end of this week.

peter-toth · 2021-04-29T12:40:29Z

I've opened a new PR here: #32396 with the new approach.
I didn't update PhysicalAggregation to collapse Project under Aggregate as is doesn't seem necessary.

* [SPARK-34225][CORE][FOLLOWUP] Replace Hadoop's Path with Utils.resolveURI to make the way to get URI simple ### What changes were proposed in this pull request? This PR proposes to replace Hadoop's `Path` with `Utils.resolveURI` to make the way to get URI simple in `SparkContext`. ### Why are the changes needed? Keep the code simple. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Existing tests. Closes #32164 from sarutak/followup-SPARK-34225. Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> * [SPARK-35070][SQL] TRANSFORM not support alias in inputs ### What changes were proposed in this pull request? Normal function parameters should not support alias, hive not support too ![image](https://user-images.githubusercontent.com/46485123/114645556-4a7ff400-9d0c-11eb-91eb-bc679ea0039a.png) In this pr we forbid use alias in `TRANSFORM`'s inputs ### Why are the changes needed? Fix bug ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Added UT Closes #32165 from AngersZhuuuu/SPARK-35070. Authored-by: Angerszhuuuu <angers.zhu@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [MINOR][CORE] Correct the number of started fetch requests in log ### What changes were proposed in this pull request? When counting the number of started fetch requests, we should exclude the deferred requests. ### Why are the changes needed? Fix the wrong number in the log. ### Does this PR introduce _any_ user-facing change? Yes, users see the correct number of started requests in logs. ### How was this patch tested? Manually tested. Closes #32180 from Ngone51/count-deferred-request. Lead-authored-by: yi.wu <yi.wu@databricks.com> Co-authored-by: wuyi <yi.wu@databricks.com> Signed-off-by: attilapiros <piros.attila.zsolt@gmail.com> * [SPARK-34995] Port/integrate Koalas remaining codes into PySpark ### What changes were proposed in this pull request? There are some more changes in Koalas such as [databricks/koalas#2141](https://github.com/databricks/koalas/commit/c8f803d6becb3accd767afdb3774c8656d0d0b47), [databricks/koalas#2143](https://github.com/databricks/koalas/commit/913d68868d38ee7158c640aceb837484f417267e) after the main code porting, this PR is to synchronize those changes with the `pyspark.pandas`. ### Why are the changes needed? We should port the whole Koalas codes into PySpark and synchronize them. ### Does this PR introduce _any_ user-facing change? Fixed some incompatible behavior with pandas 1.2.0 and added more to the `to_markdown` docstring. ### How was this patch tested? Manually tested in local. Closes #32154 from itholic/SPARK-34995. Authored-by: itholic <haejoon.lee@databricks.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> * Revert "[SPARK-34995] Port/integrate Koalas remaining codes into PySpark" This reverts commit 9689c44b602781c1d6b31a322162c488ed17a29b. * [SPARK-34843][SQL][FOLLOWUP] Fix a test failure in OracleIntegrationSuite ### What changes were proposed in this pull request? This PR fixes a test failure in `OracleIntegrationSuite`. After SPARK-34843 (#31965), the way to divide partitions is changed and `OracleIntegrationSuites` is affected. ``` [info] - SPARK-22814 support date/timestamp types in partitionColumn *** FAILED *** (230 milliseconds) [info] Set(""D" < '2018-07-11' or "D" is null", ""D" >= '2018-07-11' AND "D" < '2018-07-15'", ""D" >= '2018-07-15'") did not equal Set(""D" < '2018-07-10' or "D" is null", ""D" >= '2018-07-10' AND "D" < '2018-07-14'", ""D" >= '2018-07-14'") (OracleIntegrationSuite.scala:448) [info] Analysis: [info] Set(missingInLeft: ["D" < '2018-07-10' or "D" is null, "D" >= '2018-07-10' AND "D" < '2018-07-14', "D" >= '2018-07-14'], missingInRight: ["D" < '2018-07-11' or "D" is null, "D" >= '2018-07-11' AND "D" < '2018-07-15', "D" >= '2018-07-15']) ``` ### Why are the changes needed? To follow the previous change. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? The modified test. Closes #32186 from sarutak/fix-oracle-date-error. Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> * [SPARK-35032][PYTHON] Port Koalas Index unit tests into PySpark ### What changes were proposed in this pull request? Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas Index unit tests to PySpark. ### Why are the changes needed? Currently, the pandas-on-Spark modules are not tested fully. We should enable the Index unit tests. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Enable Index unit tests. Closes #32139 from xinrong-databricks/port.indexes_tests. Authored-by: Xinrong Meng <xinrong.meng@databricks.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> * [SPARK-35099][SQL] Convert ANSI interval literals to SQL string in ANSI style ### What changes were proposed in this pull request? Handle `YearMonthIntervalType` and `DayTimeIntervalType` in the `sql()` and `toString()` method of `Literal`, and format the ANSI interval in the ANSI style. ### Why are the changes needed? To improve readability and UX with Spark SQL. For example, a test output before the changes: ``` -- !query select timestamp'2011-11-11 11:11:11' - interval '2' day -- !query schema struct<TIMESTAMP '2011-11-11 11:11:11' - 172800000000:timestamp> -- !query output 2011-11-09 11:11:11 ``` ### Does this PR introduce _any_ user-facing change? Should not since the new intervals haven't been released yet. ### How was this patch tested? By running new tests: ``` $ ./build/sbt "test:testOnly *LiteralExpressionSuite" ``` Closes #32196 from MaxGekk/literal-ansi-interval-sql. Authored-by: Max Gekk <max.gekk@gmail.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-35083][CORE] Support remote scheduler pool files ### What changes were proposed in this pull request? Use hadoop FileSystem instead of FileInputStream. ### Why are the changes needed? Make `spark.scheduler.allocation.file` suport remote file. When using Spark as a server (e.g. SparkThriftServer), it's hard for user to specify a local path as the scheduler pool. ### Does this PR introduce _any_ user-facing change? Yes, a minor feature. ### How was this patch tested? Pass `core/src/test/scala/org/apache/spark/scheduler/PoolSuite.scala` and manul test After add config `spark.scheduler.allocation.file=hdfs:///tmp/fairscheduler.xml`. We intrudoce the configed pool. ![pool1](https://user-images.githubusercontent.com/12025282/114810037-df065700-9ddd-11eb-8d7a-54b59a07ee7b.jpg) Closes #32184 from ulysses-you/SPARK-35083. Authored-by: ulysses-you <ulyssesyou18@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> * [SPARK-35104][SQL] Fix ugly indentation of multiple JSON records in a single split file generated by JacksonGenerator when pretty option is true ### What changes were proposed in this pull request? This issue fixes an issue that indentation of multiple output JSON records in a single split file are broken except for the first record in the split when `pretty` option is `true`. ``` // Run in the Spark Shell. // Set spark.sql.leafNodeDefaultParallelism to 1 for the current master. // Or set spark.default.parallelism for the previous releases. spark.conf.set("spark.sql.leafNodeDefaultParallelism", 1) val df = Seq("a", "b", "c").toDF df.write.option("pretty", "true").json("/path/to/output") # Run in a Shell $ cat /path/to/output/*.json { "value" : "a" } { "value" : "b" } { "value" : "c" } ``` ### Why are the changes needed? It's not pretty even though `pretty` option is true. ### Does this PR introduce _any_ user-facing change? I think "No". Indentation style is changed but JSON format is not changed. ### How was this patch tested? New test. Closes #32203 from sarutak/fix-ugly-indentation. Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-34995] Port/integrate Koalas remaining codes into PySpark ### What changes were proposed in this pull request? There are some more changes in Koalas such as [databricks/koalas#2141](https://github.com/databricks/koalas/commit/c8f803d6becb3accd767afdb3774c8656d0d0b47), [databricks/koalas#2143](https://github.com/databricks/koalas/commit/913d68868d38ee7158c640aceb837484f417267e) after the main code porting, this PR is to synchronize those changes with the `pyspark.pandas`. ### Why are the changes needed? We should port the whole Koalas codes into PySpark and synchronize them. ### Does this PR introduce _any_ user-facing change? Fixed some incompatible behavior with pandas 1.2.0 and added more to the `to_markdown` docstring. ### How was this patch tested? Manually tested in local. Closes #32197 from itholic/SPARK-34995-fix. Authored-by: itholic <haejoon.lee@databricks.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> * [MINOR][DOCS] Soften security warning and keep it in cluster management docs only ### What changes were proposed in this pull request? Soften security warning and keep it in cluster management docs only, not in the main doc page, where it's not necessarily relevant. ### Why are the changes needed? The statement is perhaps unnecessarily 'frightening' as the first section in the main docs page. It applies to clusters not local mode, anyhow. ### Does this PR introduce _any_ user-facing change? Just a docs change. ### How was this patch tested? N/A Closes #32206 from srowen/SecurityStatement. Authored-by: Sean Owen <srowen@gmail.com> Signed-off-by: Sean Owen <srowen@gmail.com> * [SPARK-34787][CORE] Option variable in Spark historyServer log should be displayed as actual value instead of Some(XX) ### What changes were proposed in this pull request? Make the attemptId in the log of historyServer to be more easily to read. ### Why are the changes needed? Option variable in Spark historyServer log should be displayed as actual value instead of Some(XX) ### Does this PR introduce any user-facing change? No ### How was this patch tested? manual test Closes #32189 from kyoty/history-server-print-option-variable. Authored-by: kyoty <echohlne@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> * [SPARK-35101][INFRA] Add GitHub status check in PR instead of a comment ### What changes were proposed in this pull request? TL;DR: now it shows green yellow read status of tests instead of relying on a comment in a PR, **see https://github.com/HyukjinKwon/spark/pull/41 for an example**. This PR proposes the GitHub status checks instead of a comment that link to the build (from forked repository) in PRs. This is how it works: 1. **forked repo**: "Build and test" workflow is triggered when you create a branch to create a PR which uses your resources in GitHub Actions. 1. **main repo**: "Notify test workflow" (previously created a comment) now creates a in-progress status (yellow status) as a GitHub Actions check to your current PR. 1. **main repo**: "Update build status workflow" regularly (every 15 mins) checks open PRs, and updates the status of GitHub Actions checks at PRs according to the status of workflows in the forked repositories (status sync). **NOTE** that creating/updating statuses in the PRs is only allowed from the main repo. That's why the flow is as above. ### Why are the changes needed? The GitHub status shows a green although the tests are running, which is confusing. ### Does this PR introduce _any_ user-facing change? No, dev-only. ### How was this patch tested? Manually tested at: - https://github.com/HyukjinKwon/spark/pull/41 - HyukjinKwon#42 - HyukjinKwon#43 - https://github.com/HyukjinKwon/spark/pull/37 **queued**: <img width="861" alt="Screen Shot 2021-04-16 at 10 56 03 AM" src="https://user-images.githubusercontent.com/6477701/114960831-c9a73080-9ea2-11eb-8442-ddf3f6008a45.png"> **in progress**: <img width="871" alt="Screen Shot 2021-04-16 at 12 14 39 PM" src="https://user-images.githubusercontent.com/6477701/114966359-59ea7300-9ead-11eb-98cb-1e63323980ad.png"> **passed**: ![Screen Shot 2021-04-16 at 2 04 07 PM](https://user-images.githubusercontent.com/6477701/114974045-a12c3000-9ebc-11eb-9be5-653393a863e6.png) **failure**: ![Screen Shot 2021-04-16 at 10 46 10 PM](https://user-images.githubusercontent.com/6477701/115033584-90ec7300-9f05-11eb-8f2e-0fc2ef986a70.png) Closes #32193 from HyukjinKwon/update-checks-pr-poc. Lead-authored-by: HyukjinKwon <gurwls223@apache.org> Co-authored-by: Hyukjin Kwon <gurwls223@apache.org> Co-authored-by: Yikun Jiang <yikunkero@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> * [MINOR][INFRA] Upgrade Jira client to 2.0.0 ### What changes were proposed in this pull request? SPARK-10498 added the initial Jira client requirement with 1.0.3 five year ago (2016 January). As of today, it causes `dev/merge_spark_pr.py` failure with `Python 3.9.4` due to this old dependency. This PR aims to upgrade it to the latest version, 2.0.0. The latest version is also a little old (2018 July). - https://pypi.org/project/jira/#history ### Why are the changes needed? `Jira==2.0.0` works well with both Python 3.8/3.9 while `Jira==1.0.3` fails with Python 3.9. **BEFORE** ``` $ pyenv global 3.9.4 $ pip freeze | grep jira jira==1.0.3 $ dev/merge_spark_pr.py Traceback (most recent call last): File "/Users/dongjoon/APACHE/spark-merge/dev/merge_spark_pr.py", line 39, in <module> import jira.client File "/Users/dongjoon/.pyenv/versions/3.9.4/lib/python3.9/site-packages/jira/__init__.py", line 5, in <module> from .config import get_jira File "/Users/dongjoon/.pyenv/versions/3.9.4/lib/python3.9/site-packages/jira/config.py", line 17, in <module> from .client import JIRA File "/Users/dongjoon/.pyenv/versions/3.9.4/lib/python3.9/site-packages/jira/client.py", line 165 validate=False, get_server_info=True, async=False, logging=True, max_retries=3): ^ SyntaxError: invalid syntax ``` **AFTER** ``` $ pip install jira==2.0.0 $ dev/merge_spark_pr.py git rev-parse --abbrev-ref HEAD Which pull request would you like to merge? (e.g. 34): ``` ### Does this PR introduce _any_ user-facing change? No. This is a committer-only script. ### How was this patch tested? Manually. Closes #32215 from dongjoon-hyun/jira. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> * [SPARK-35116][SQL][TESTS] The generated data fits the precision of DayTimeIntervalType in spark ### What changes were proposed in this pull request? The precision of `java.time.Duration` is nanosecond, but when it is used as `DayTimeIntervalType` in Spark, it is microsecond. At present, the `DayTimeIntervalType` data generated in the implementation of `RandomDataGenerator` is accurate to nanosecond, which will cause the `DayTimeIntervalType` to be converted to long, and then back to `DayTimeIntervalType` to lose the accuracy, which will cause the test to fail. For example: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/137390/testReport/org.apache.spark.sql.hive.execution/HashAggregationQueryWithControlledFallbackSuite/udaf_with_all_data_types/ ### Why are the changes needed? Improve `RandomDataGenerator` so that the generated data fits the precision of DayTimeIntervalType in spark. ### Does this PR introduce _any_ user-facing change? 'No'. Just change the test class. ### How was this patch tested? Jenkins test. Closes #32212 from beliefer/SPARK-35116. Authored-by: beliefer <beliefer@163.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-35114][SQL][TESTS] Add checks for ANSI intervals to `LiteralExpressionSuite` ### What changes were proposed in this pull request? In the PR, I propose to add additional checks for ANSI interval types `YearMonthIntervalType` and `DayTimeIntervalType` to `LiteralExpressionSuite`. Also, I replaced some long literal values by `CalendarInterval` to check `CalendarIntervalType` that the tests were supposed to check. ### Why are the changes needed? To improve test coverage and have the same checks for ANSI types as for `CalendarIntervalType`. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? By running the modified test suite: ``` $ build/sbt "test:testOnly *LiteralExpressionSuite" ``` Closes #32213 from MaxGekk/interval-literal-tests. Authored-by: Max Gekk <max.gekk@gmail.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-34716][SQL] Support ANSI SQL intervals by the aggregate function `sum` ### What changes were proposed in this pull request? Extend the `Sum` expression to to support `DayTimeIntervalType` and `YearMonthIntervalType` added by #31614. Note: the expressions can throw the overflow exception independently from the SQL config `spark.sql.ansi.enabled`. In this way, the modified expressions always behave in the ANSI mode for the intervals. ### Why are the changes needed? Extend `org.apache.spark.sql.catalyst.expressions.aggregate.Sum` to support `DayTimeIntervalType` and `YearMonthIntervalType`. ### Does this PR introduce _any_ user-facing change? 'No'. Should not since new types have not been released yet. ### How was this patch tested? Jenkins test Closes #32107 from beliefer/SPARK-34716. Lead-authored-by: gengjiaan <gengjiaan@360.cn> Co-authored-by: beliefer <beliefer@163.com> Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-35115][SQL][TESTS] Check ANSI intervals in `MutableProjectionSuite` ### What changes were proposed in this pull request? Add checks for `YearMonthIntervalType` and `DayTimeIntervalType` to `MutableProjectionSuite`. ### Why are the changes needed? To improve test coverage, and the same checks as for `CalendarIntervalType`. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? By running the modified test suite: ``` $ build/sbt "test:testOnly *MutableProjectionSuite" ``` Closes #32225 from MaxGekk/test-ansi-intervals-in-MutableProjectionSuite. Authored-by: Max Gekk <max.gekk@gmail.com> Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org> * [SPARK-35092][UI] the auto-generated rdd's name in the storage tab should be truncated if it is too long ### What changes were proposed in this pull request? the auto-generated rdd's name in the storage tab should be truncated as a single line if it is too long. ### Why are the changes needed? to make the ui shows more friendly. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? just a simple modifition in css, manual test works well like below: before modified: ![the rdd title in storage page shows too long](https://user-images.githubusercontent.com/52202080/115009655-17da2500-9edf-11eb-86a7-088bed7ef8f7.png) after modified： Tht titile needs just one line: ![storage标题过长修改后](https://user-images.githubusercontent.com/52202080/114872091-8c07c080-9e2c-11eb-81a8-0c097b1a77bf.png) Closes #32191 from kyoty/storage-rdd-titile-display-improve. Authored-by: kyoty <echohlne@gmail.com> Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com> * [SPARK-35109][SQL] Fix minor exception messages of HashedRelation and HashJoin ### What changes were proposed in this pull request? It seems that we miss classifying one `SparkOutOfMemoryError` in `HashedRelation`. Add the error classification for it. In addition, clean up two errors definition of `HashJoin` as they are not used. ### Why are the changes needed? Better error classification. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Existing tests. Closes #32211 from c21/error-message. Authored-by: Cheng Su <chengsu@fb.com> Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org> * [SPARK-34581][SQL] Don't optimize out grouping expressions from aggregate expressions without aggregate function ### What changes were proposed in this pull request? This PR: - Adds a new expression `GroupingExprRef` that can be used in aggregate expressions of `Aggregate` nodes to refer grouping expressions by index. These expressions capture the data type and nullability of the referred grouping expression. - Adds a new rule `EnforceGroupingReferencesInAggregates` that inserts the references in the beginning of the optimization phase. - Adds a new rule `UpdateGroupingExprRefNullability` to update nullability of `GroupingExprRef` expressions as nullability of referred grouping expression can change during optimization. ### Why are the changes needed? If aggregate expressions (without aggregate functions) in an `Aggregate` node are complex then the `Optimizer` can optimize out grouping expressions from them and so making aggregate expressions invalid. Here is a simple example: ``` SELECT not(t.id IS NULL) , count(*) FROM t GROUP BY t.id IS NULL ``` In this case the `BooleanSimplification` rule does this: ``` === Applying Rule org.apache.spark.sql.catalyst.optimizer.BooleanSimplification === !Aggregate [isnull(id#222)], [NOT isnull(id#222) AS (NOT (id IS NULL))#226, count(1) AS c#224L] Aggregate [isnull(id#222)], [isnotnull(id#222) AS (NOT (id IS NULL))#226, count(1) AS c#224L] +- Project [value#219 AS id#222] +- Project [value#219 AS id#222] +- LocalRelation [value#219] +- LocalRelation [value#219] ``` where `NOT isnull(id#222)` is optimized to `isnotnull(id#222)` and so it no longer refers to any grouping expression. Before this PR: ``` == Optimized Logical Plan == Aggregate [isnull(id#222)], [isnotnull(id#222) AS (NOT (id IS NULL))#234, count(1) AS c#232L] +- Project [value#219 AS id#222] +- LocalRelation [value#219] ``` and running the query throws an error: ``` Couldn't find id#222 in [isnull(id#222)#230,count(1)#226L] java.lang.IllegalStateException: Couldn't find id#222 in [isnull(id#222)#230,count(1)#226L] ``` After this PR: ``` == Optimized Logical Plan == Aggregate [isnull(id#222)], [NOT groupingexprref(0) AS (NOT (id IS NULL))#234, count(1) AS c#232L] +- Project [value#219 AS id#222] +- LocalRelation [value#219] ``` and the query works. ### Does this PR introduce _any_ user-facing change? Yes, the query works. ### How was this patch tested? Added new UT. Closes #31913 from peter-toth/SPARK-34581-keep-grouping-expressions. Authored-by: Peter Toth <peter.toth@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [SPARK-35122][SQL] Migrate CACHE/UNCACHE TABLE to use AnalysisOnlyCommand ### What changes were proposed in this pull request? Now that `AnalysisOnlyCommand` in introduced in #32032, `CacheTable` and `UncacheTable` can extend `AnalysisOnlyCommand` to simplify the code base. For example, the logic to handle these commands such that the tables are only analyzed is scattered across different places. ### Why are the changes needed? To simplify the code base to handle these two commands. ### Does this PR introduce _any_ user-facing change? No, just internal refactoring. ### How was this patch tested? The existing tests (e.g., `CachedTableSuite`) cover the changes in this PR. For example, if I make `CacheTable`/`UncacheTable` extend `LeafCommand`, there are few failures in `CachedTableSuite`. Closes #32220 from imback82/cache_cmd_analysis_only. Authored-by: Terry Kim <yuminkim@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [SPARK-31937][SQL] Support processing ArrayType/MapType/StructType data using no-serde mode script transform ### What changes were proposed in this pull request? Support no-serde mode script transform use ArrayType/MapType/StructStpe data. ### Why are the changes needed? Make user can process array/map/struct data ### Does this PR introduce _any_ user-facing change? Yes, user can process array/map/struct data in script transform `no-serde` mode ### How was this patch tested? Added UT Closes #30957 from AngersZhuuuu/SPARK-31937. Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com> Co-authored-by: angerszhu <angers.zhu@gmail.com> Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> * [SPARK-35045][SQL][FOLLOW-UP] Add a configuration for CSV input buffer size ### What changes were proposed in this pull request? This PR makes the input buffer configurable (as an internal configuration). This is mainly to work around the regression in uniVocity/univocity-parsers#449. This is particularly useful for SQL workloads that requires to rewrite the `CREATE TABLE` with options. ### Why are the changes needed? To work around uniVocity/univocity-parsers#449. ### Does this PR introduce _any_ user-facing change? No, it's only internal option. ### How was this patch tested? Manually tested by modifying the unittest added in https://github.com/apache/spark/pull/31858 as below: ```diff diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala index fd25a79619d..705f38dbfbd 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala -2456,6 +2456,7 abstract class CSVSuite test("SPARK-34768: counting a long record with ignoreTrailingWhiteSpace set to true") { val bufSize = 128 val line = "X" * (bufSize - 1) + "| |" + spark.conf.set("spark.sql.csv.parser.inputBufferSize", 128) withTempPath { path => Seq(line).toDF.write.text(path.getAbsolutePath) assert(spark.read.format("csv") ``` Closes #32231 from HyukjinKwon/SPARK-35045-followup. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org> * [SPARK-34837][SQL] Support ANSI SQL intervals by the aggregate function `avg` ### What changes were proposed in this pull request? Extend the `Average` expression to support `DayTimeIntervalType` and `YearMonthIntervalType` added by #31614. Note: the expressions can throw the overflow exception independently from the SQL config `spark.sql.ansi.enabled`. In this way, the modified expressions always behave in the ANSI mode for the intervals. ### Why are the changes needed? Extend `org.apache.spark.sql.catalyst.expressions.aggregate.Average` to support `DayTimeIntervalType` and `YearMonthIntervalType`. ### Does this PR introduce _any_ user-facing change? 'No'. Should not since new types have not been released yet. ### How was this patch tested? Jenkins test Closes #32229 from beliefer/SPARK-34837. Authored-by: gengjiaan <gengjiaan@360.cn> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-35107][SQL] Parse unit-to-unit interval literals to ANSI intervals ### What changes were proposed in this pull request? Parse the year-month interval literals like `INTERVAL '1-1' YEAR TO MONTH` to values of `YearMonthIntervalType`, and day-time interval literals to `DayTimeIntervalType` values. Currently, Spark SQL supports: - DAY TO HOUR - DAY TO MINUTE - DAY TO SECOND - HOUR TO MINUTE - HOUR TO SECOND - MINUTE TO SECOND All such interval literals are converted to `DayTimeIntervalType`, and `YEAR TO MONTH` to `YearMonthIntervalType` while loosing info about `from` and `to` units. **Note**: new behavior is under the SQL config `spark.sql.legacy.interval.enabled` which is `false` by default. When the config is set to `true`, the interval literals are parsed to `CaledarIntervalType` values. Closes #32176 ### Why are the changes needed? To conform the ANSI SQL standard which assumes conversions of interval literals to year-month or day-time interval but not to mixed interval type like Catalyst's `CalendarIntervalType`. ### Does this PR introduce _any_ user-facing change? Yes. Before: ```sql spark-sql> SELECT INTERVAL '1 01:02:03.123' DAY TO SECOND; 1 days 1 hours 2 minutes 3.123 seconds spark-sql> SELECT typeof(INTERVAL '1 01:02:03.123' DAY TO SECOND); interval ``` After: ```sql spark-sql> SELECT INTERVAL '1 01:02:03.123' DAY TO SECOND; 1 01:02:03.123000000 spark-sql> SELECT typeof(INTERVAL '1 01:02:03.123' DAY TO SECOND); day-time interval ``` ### How was this patch tested? 1. By running the affected test suites: ``` $ ./build/sbt "test:testOnly *.ExpressionParserSuite" $ SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "sql/testOnly *SQLQueryTestSuite -- -z interval.sql" $ SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "sql/testOnly *SQLQueryTestSuite -- -z create_view.sql" $ SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "sql/testOnly *SQLQueryTestSuite -- -z date.sql" $ SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "sql/testOnly *SQLQueryTestSuite -- -z timestamp.sql" ``` 2. PostgresSQL tests are executed with `spark.sql.legacy.interval.enabled` is set to `true` to keep compatibility with PostgreSQL output: ```sql > SELECT interval '999' second; 0 years 0 mons 0 days 0 hours 16 mins 39.00 secs ``` Closes #32209 from MaxGekk/parse-ansi-interval-literals. Authored-by: Max Gekk <max.gekk@gmail.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-34715][SQL][TESTS] Add round trip tests for period <-> month and duration <-> micros ### What changes were proposed in this pull request? Similarly to the test from the PR https://github.com/apache/spark/pull/31799, add tests: 1. Months -> Period -> Months 2. Period -> Months -> Period 3. Duration -> micros -> Duration ### Why are the changes needed? Add round trip tests for period <-> month and duration <-> micros ### Does this PR introduce _any_ user-facing change? 'No'. Just test cases. ### How was this patch tested? Jenkins test Closes #32234 from beliefer/SPARK-34715. Authored-by: gengjiaan <gengjiaan@360.cn> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-35125][K8S] Upgrade K8s client to 5.3.0 to support K8s 1.20 ### What changes were proposed in this pull request? Although AS-IS master branch already works with K8s 1.20, this PR aims to upgrade K8s client to 5.3.0 to support K8s 1.20 officially. - https://github.com/fabric8io/kubernetes-client#compatibility-matrix The following are the notable breaking API changes. 1. Remove Doneable (5.0+): - https://github.com/fabric8io/kubernetes-client/pull/2571 2. Change Watcher.onClose signature (5.0+): - https://github.com/fabric8io/kubernetes-client/pull/2616 3. Change Readiness (5.1+) - https://github.com/fabric8io/kubernetes-client/pull/2796 ### Why are the changes needed? According to the compatibility matrix, this makes Apache Spark and its external cluster manager extension support all K8s 1.20 features officially for Apache Spark 3.2.0. ### Does this PR introduce _any_ user-facing change? Yes, this is a dev dependency change which affects K8s cluster extension users. ### How was this patch tested? Pass the CIs. This is manually tested with K8s IT. ``` KubernetesSuite: - Run SparkPi with no resources - Run SparkPi with a very long application name. - Use SparkLauncher.NO_RESOURCE - Run SparkPi with a master URL without a scheme. - Run SparkPi with an argument. - Run SparkPi with custom labels, annotations, and environment variables. - All pods have the same service account by default - Run extraJVMOptions check on driver - Run SparkRemoteFileTest using a remote data file - Verify logging configuration is picked from the provided SPARK_CONF_DIR/log4j.properties - Run SparkPi with env and mount secrets. - Run PySpark on simple pi.py example - Run PySpark to test a pyfiles example - Run PySpark with memory customization - Run in client mode. - Start pod creation from template - PVs with local storage - Launcher client dependencies - SPARK-33615: Launcher client archives - SPARK-33748: Launcher python client respecting PYSPARK_PYTHON - SPARK-33748: Launcher python client respecting spark.pyspark.python and spark.pyspark.driver.python - Launcher python client dependencies using a zip file - Test basic decommissioning - Test basic decommissioning with shuffle cleanup - Test decommissioning with dynamic allocation & shuffle cleanups - Test decommissioning timeouts - Run SparkR on simple dataframe.R example Run completed in 17 minutes, 44 seconds. Total number of tests run: 27 Suites: completed 2, aborted 0 Tests: succeeded 27, failed 0, canceled 0, ignored 0, pending 0 All tests passed. ``` Closes #32221 from dongjoon-hyun/SPARK-K8S-530. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> * [SPARK-35102][SQL] Make spark.sql.hive.version read-only, not deprecated and meaningful ### What changes were proposed in this pull request? Firstly let's take a look at the definition and comment. ``` // A fake config which is only here for backward compatibility reasons. This config has no effect // to Spark, just for reporting the builtin Hive version of Spark to existing applications that // already rely on this config. val FAKE_HIVE_VERSION = buildConf("spark.sql.hive.version") .doc(s"deprecated, please use ${HIVE_METASTORE_VERSION.key} to get the Hive version in Spark.") .version("1.1.1") .fallbackConf(HIVE_METASTORE_VERSION) ``` It is used for reporting the built-in Hive version but the current status is unsatisfactory, as it is could be changed in many ways e.g. --conf/SET syntax. It is marked as deprecated but kept a long way until now. I guess it is hard for us to remove it and not even necessary. On second thought, it's actually good for us to keep it to work with the `spark.sql.hive.metastore.version`. As when `spark.sql.hive.metastore.version` is changed, it could be used to report the compiled hive version statically, it's useful when an error occurs in this case. So this parameter should be fixed to compiled hive version. ### Why are the changes needed? `spark.sql.hive.version` is useful in certain cases and should be read-only ### Does this PR introduce _any_ user-facing change? `spark.sql.hive.version` now is read-only ### How was this patch tested? new test cases Closes #32200 from yaooqinn/SPARK-35102. Authored-by: Kent Yao <yao@apache.org> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [SPARK-35136] Remove initial null value of LiveStage.info ### What changes were proposed in this pull request? To prevent potential NullPointerExceptions, this PR changes the `LiveStage` constructor to take `info` as a constructor parameter and adds a nullcheck in `AppStatusListener.activeStages`. ### Why are the changes needed? The `AppStatusListener.getOrCreateStage` would create a LiveStage object with the `info` field set to null and right after that set it to a specific StageInfo object. This can lead to a race condition when the `livestages` are read in between those calls. This could then lead to a null pointer exception in, for instance: `AppStatusListener.activeStages`. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Regular CI/CD tests Closes #32233 from sander-goos/SPARK-35136-livestage. Authored-by: Sander Goos <sander.goos@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [SPARK-35138][SQL] Remove Antlr4 workaround ### What changes were proposed in this pull request? Remove Antlr 4.7 workaround. ### Why are the changes needed? The https://github.com/antlr/antlr4/commit/ac9f7530 has been fixed in upstream, so remove the workaround to simplify code. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existed UTs. Closes #32238 from pan3793/antlr-minor. Authored-by: Cheng Pan <379377944@qq.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> * [SPARK-35120][INFRA] Guide users to sync branch and enable GitHub Actions in their forked repository ### What changes were proposed in this pull request? This PR proposes to add messages when the workflow fails to find the workflow run in a forked repository, for example as below: **Before** ![Screen Shot 2021-04-19 at 9 41 52 PM](https://user-images.githubusercontent.com/6477701/115238011-28e19b00-a158-11eb-8c5c-6374ca1e9790.png) ![Screen Shot 2021-04-19 at 9 42 00 PM](https://user-images.githubusercontent.com/6477701/115237984-22ebba00-a158-11eb-9b0f-11fe11072830.png) **After** ![Screen Shot 2021-04-19 at 9 25 32 PM](https://user-images.githubusercontent.com/6477701/115237507-9c36dd00-a157-11eb-8ba7-f5f88caa1058.png) ![Screen Shot 2021-04-19 at 9 23 13 PM](https://user-images.githubusercontent.com/6477701/115236793-c2a84880-a156-11eb-98fc-1bb7d4bc31dd.png) (typo `foce` in the image was fixed) See this example: https://github.com/HyukjinKwon/spark/runs/2380644793 ### Why are the changes needed? To guide users to enable Github Actions in their forked repositories (and sync their branch to the latest `master` in Apache Spark). ### Does this PR introduce _any_ user-facing change? No, dev-only. ### How was this patch tested? Manually tested in: - https://github.com/HyukjinKwon/spark/pull/47 - https://github.com/HyukjinKwon/spark/pull/46 Closes #32235 from HyukjinKwon/test-test-test. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> * [SPARK-35131][K8S] Support early driver service clean-up during app termination ### What changes were proposed in this pull request? This PR aims to support a new configuration, `spark.kubernetes.driver.service.deleteOnTermination`, to clean up `Driver Service` resource during app termination. ### Why are the changes needed? The K8s service is one of the important resources and sometimes it's controlled by quota. ``` $ k describe quota Name: service Namespace: default Resource Used Hard -------- ---- ---- services 1 3 ``` Apache Spark creates a service for driver whose lifecycle is the same with driver pod. It means a new Spark job submission fails if the number of completed Spark jobs equals the number of service quota. **BEFORE** ``` $ k get pod NAME READY STATUS RESTARTS AGE org-apache-spark-examples-sparkpi-a32c9278e7061b4d-driver 0/1 Completed 0 31m org-apache-spark-examples-sparkpi-a9f1f578e721ef62-driver 0/1 Completed 0 78s $ k get svc NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE kubernetes ClusterIP 10.96.0.1 <none> 443/TCP 80m org-apache-spark-examples-sparkpi-a32c9278e7061b4d-driver-svc ClusterIP None <none> 7078/TCP,7079/TCP,4040/TCP 31m org-apache-spark-examples-sparkpi-a9f1f578e721ef62-driver-svc ClusterIP None <none> 7078/TCP,7079/TCP,4040/TCP 80s $ k describe quota Name: service Namespace: default Resource Used Hard -------- ---- ---- services 3 3 $ bin/spark-submit... Exception in thread "main" io.fabric8.kubernetes.client.KubernetesClientException: Failure executing: POST at: https://192.168.64.50:8443/api/v1/namespaces/default/services. Message: Forbidden! User minikube doesn't have permission. services "org-apache-spark-examples-sparkpi-843f6978e722819c-driver-svc" is forbidden: exceeded quota: service, requested: services=1, used: services=3, limited: services=3. ``` **AFTER** ``` $ k get pod NAME READY STATUS RESTARTS AGE org-apache-spark-examples-sparkpi-23d5f278e77731a7-driver 0/1 Completed 0 26s org-apache-spark-examples-sparkpi-d1292278e7768ed4-driver 0/1 Completed 0 67s org-apache-spark-examples-sparkpi-e5bedf78e776ea9d-driver 0/1 Completed 0 44s $ k get svc NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE kubernetes ClusterIP 10.96.0.1 <none> 443/TCP 172m $ k describe quota Name: service Namespace: default Resource Used Hard -------- ---- ---- services 1 3 ``` ### Does this PR introduce _any_ user-facing change? Yes, this PR adds a new configuration, `spark.kubernetes.driver.service.deleteOnTermination`, and enables it by default. The change is documented at the migration guide. ### How was this patch tested? Pass the CIs. This is tested with K8s IT manually. ``` KubernetesSuite: - Run SparkPi with no resources - Run SparkPi with a very long application name. - Use SparkLauncher.NO_RESOURCE - Run SparkPi with a master URL without a scheme. - Run SparkPi with an argument. - Run SparkPi with custom labels, annotations, and environment variables. - All pods have the same service account by default - Run extraJVMOptions check on driver - Run SparkRemoteFileTest using a remote data file - Verify logging configuration is picked from the provided SPARK_CONF_DIR/log4j.properties - Run SparkPi with env and mount secrets. - Run PySpark on simple pi.py example - Run PySpark to test a pyfiles example - Run PySpark with memory customization - Run in client mode. - Start pod creation from template - PVs with local storage - Launcher client dependencies - SPARK-33615: Launcher client archives - SPARK-33748: Launcher python client respecting PYSPARK_PYTHON - SPARK-33748: Launcher python client respecting spark.pyspark.python and spark.pyspark.driver.python - Launcher python client dependencies using a zip file - Test basic decommissioning - Test basic decommissioning with shuffle cleanup - Test decommissioning with dynamic allocation & shuffle cleanups - Test decommissioning timeouts - Run SparkR on simple dataframe.R example Run completed in 19 minutes, 9 seconds. Total number of tests run: 27 Suites: completed 2, aborted 0 Tests: succeeded 27, failed 0, canceled 0, ignored 0, pending 0 All tests passed. ``` Closes #32226 from dongjoon-hyun/SPARK-35131. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> * [SPARK-35103][SQL] Make TypeCoercion rules more efficient ## What changes were proposed in this pull request? This PR fixes a couple of things in TypeCoercion rules: - Only run the propagate types step if the children of a node have output attributes with changed dataTypes and/or nullability. This is implemented as custom tree transformation. The TypeCoercion rules now only implement a partial function. - Combine multiple type coercion rules into a single rule. Multiple rules are applied in single tree traversal. - Reduce calls to conf.get in DecimalPrecision. This now happens once per tree traversal, instead of once per matched expression. - Reduce the use of withNewChildren. This brings down the number of CPU cycles spend in analysis by ~28% (benchmark: 10 iterations of all TPC-DS queries on SF10). ## How was this patch tested? Existing tests. Closes #32208 from sigmod/coercion. Authored-by: Yingyi Bu <yingyi.bu@databricks.com> Signed-off-by: herman <herman@databricks.com> * [SPARK-35117][UI] Change progress bar back to highlight ratio of tasks in progress ### What changes were proposed in this pull request? Small UI update to add highlighting the number of tasks in progress in a stage/job instead of highlighting the whole in progress stage/job. This was the behavior pre Spark 3.1 and the bootstrap 4 upgrade. ### Why are the changes needed? To add back in functionality lost between 3.0 and 3.1. This provides a great visual queue of how much of a stage/job is currently being run. ### Does this PR introduce _any_ user-facing change? Small UI change. Before: ![image](https://user-images.githubusercontent.com/3536454/115216189-3fddaa00-a0d2-11eb-88e0-e3be925c92f0.png) After (and pre Spark 3.1): ![image](https://user-images.githubusercontent.com/3536454/115216216-48ce7b80-a0d2-11eb-9953-2adb3b377133.png) ### How was this patch tested? Updated existing UT. Closes #32214 from Kimahriman/progress-bar-started. Authored-by: Adam Binford <adamq43@gmail.com> Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com> * [SPARK-35080][SQL] Only allow a subset of correlated equality predicates when a subquery is aggregated ### What changes were proposed in this pull request? This PR updated the `foundNonEqualCorrelatedPred` logic for correlated subqueries in `CheckAnalysis` to only allow correlated equality predicates that guarantee one-to-one mapping between inner and outer attributes, instead of all equality predicates. ### Why are the changes needed? To fix correctness bugs. Before this fix Spark can give wrong results for certain correlated subqueries that pass CheckAnalysis: Example 1: ```sql create or replace view t1(c) as values ('a'), ('b') create or replace view t2(c) as values ('ab'), ('abc'), ('bc') select c, (select count(*) from t2 where t1.c = substring(t2.c, 1, 1)) from t1 ``` Correct results: [(a, 2), (b, 1)] Spark results: ``` +---+-----------------+ |c |scalarsubquery(c)| +---+-----------------+ |a |1 | |a |1 | |b |1 | +---+-----------------+ ``` Example 2: ```sql create or replace view t1(a, b) as values (0, 6), (1, 5), (2, 4), (3, 3); create or replace view t2(c) as values (6); select c, (select count(*) from t1 where a + b = c) from t2; ``` Correct results: [(6, 4)] Spark results: ``` +---+-----------------+ |c |scalarsubquery(c)| +---+-----------------+ |6 |1 | |6 |1 | |6 |1 | |6 |1 | +---+-----------------+ ``` ### Does this PR introduce _any_ user-facing change? Yes. Users will not be able to run queries that contain unsupported correlated equality predicates. ### How was this patch tested? Added unit tests. Closes #32179 from allisonwang-db/spark-35080-subquery-bug. Lead-authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com> Co-authored-by: Wenchen Fan <cloud0fan@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [SPARK-35052][SQL] Use static bits for AttributeReference and Literal ### What changes were proposed in this pull request? - Share a static ImmutableBitSet for `treePatternBits` in all object instances of AttributeReference. - Share three static ImmutableBitSets for `treePatternBits` in three kinds of Literals. - Add an ImmutableBitSet as a subclass of BitSet. ### Why are the changes needed? Reduce the additional memory usage caused by `treePatternBits`. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Existing tests. Closes #32157 from sigmod/leaf. Authored-by: Yingyi Bu <yingyi.bu@databricks.com> Signed-off-by: Gengliang Wang <ltnwgl@gmail.com> * [SPARK-35134][BUILD][TESTS] Manually exclude redundant netty jars in SparkBuild.scala to avoid version conflicts in test ### What changes were proposed in this pull request? The following logs will print when Jenkins execute [PySpark pip packaging tests](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/137500/console): ``` copying deps/jars/netty-all-4.1.51.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-buffer-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-codec-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-common-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-handler-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-resolver-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-transport-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-transport-native-epoll-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars ``` There will be 2 different versions of netty4 jars copied to the jars directory, but the `netty-xxx-4.1.50.Final.jar` not in maven `dependency:tree `, but spark only needs to rely on `netty-all-xxx.jar`. So this pr try to add new `ExclusionRule`s to `SparkBuild.scala` to exclude unnecessary netty 4 dependencies. ### Why are the changes needed? Make sure that only `netty-all-xxx.jar` is used in the test to avoid possible jar conflicts. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? - Pass the Jenkins or GitHub Action - Check Jenkins log manually, there should be only `copying deps/jars/netty-all-4.1.51.Final.jar -> pyspark-3.2.0.dev0/deps/jars` and there should be no such logs as ``` copying deps/jars/netty-buffer-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-codec-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-common-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-handler-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-resolver-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-transport-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars copying deps/jars/netty-transport-native-epoll-4.1.50.Final.jar -> pyspark-3.2.0.dev0/deps/jars ``` Closes #32230 from LuciferYang/SPARK-35134. Authored-by: yangjie01 <yangjie01@baidu.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> * [SPARK-35018][SQL][TESTS] Check transferring of year-month intervals via Hive Thrift server ### What changes were proposed in this pull request? 1. Add a test to check that Thrift server is able to collect year-month intervals and transfer them via thrift protocol. 2. Improve similar test for day-time intervals. After the changes, the test doesn't depend on the result of date subtractions. In the future, the type of date subtract can be changed. So, current PR should make the test tolerant to the changes. ### Why are the changes needed? To improve test coverage. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? By running the modified test suite: ``` $ ./build/sbt -Phive -Phive-thriftserver "test:testOnly *SparkThriftServerProtocolVersionsSuite" ``` Closes #32240 from MaxGekk/year-month-interval-thrift-protocol. Authored-by: Max Gekk <max.gekk@gmail.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-34974][SQL] Improve subquery decorrelation framework ### What changes were proposed in this pull request? This PR implements the decorrelation technique in the paper "Unnesting Arbitrary Queries" by T. Neumann; A. Kemper (http://www.btw-2015.de/res/proceedings/Hauptband/Wiss/Neumann-Unnesting_Arbitrary_Querie.pdf). It currently supports Filter, Project, Aggregate, Join, and UnaryNode that passes CheckAnalysis. This feature can be controlled by the config `spark.sql.optimizer.decorrelateInnerQuery.enabled` (default: true). A few notes: 1. This PR does not relax any constraints in CheckAnalysis for correlated subqueries, even though some cases can be supported by this new framework, such as aggregate with correlated non-equality predicates. This PR focuses on adding the new framework and making sure all existing cases can be supported. Constraints can be relaxed gradually in the future via separate PRs. 2. The new framework is only enabled for correlated scalar subqueries, as the first step. EXISTS/IN subqueries can be supported in the future. ### Why are the changes needed? Currently, Spark has limited support for correlated subqueries. It only allows `Filter` to reference outer query columns and does not support non-equality predicates when the subquery is aggregated. This new framework will allow more operators to host outer column references and support correlated non-equality predicates and more types of operators in correlated subqueries. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existing unit and SQL query tests and new optimizer plan tests. Closes #32072 from allisonwang-db/spark-34974-decorrelation. Authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [SPARK-35068][SQL] Add tests for ANSI intervals to HiveThriftBinaryServerSuite ### What changes were proposed in this pull request? After the PR https://github.com/apache/spark/pull/32209, this should be possible now. We can add test case for ANSI intervals to HiveThriftBinaryServerSuite ### Why are the changes needed? Add more test case ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Added UT Closes #32250 from AngersZhuuuu/SPARK-35068. Authored-by: Angerszhuuuu <angers.zhu@gmail.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-33976][SQL][DOCS] Add a SQL doc page for a TRANSFORM clause ### What changes were proposed in this pull request? Add doc about `TRANSFORM` and related function. ![image](https://user-images.githubusercontent.com/46485123/114332579-1627fe80-9b79-11eb-8fa7-131f0a20f72f.png) ### Why are the changes needed? ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Not need Closes #31010 from AngersZhuuuu/SPARK-33976. Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com> Co-authored-by: angerszhu <angers.zhu@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [SPARK-34877][CORE][YARN] Add the code change for adding the Spark AM log link in spark UI ### What changes were proposed in this pull request? On Running Spark job with yarn and deployment mode as client, Spark Driver and Spark Application master launch in two separate containers. In various scenarios there is need to see Spark Application master logs to see the resource allocation, Decommissioning status and other information shared between yarn RM and Spark Application master. In Cluster mode Spark driver and Spark AM is on same container, So Log link of the driver already there to see the logs in Spark UI This PR is for adding the spark AM log link for spark job running in the client mode for yarn. Instead of searching the container id and then find the logs. We can directly check in the Spark UI This change is only for showing the AM log links in the Client mode when resource manager is yarn. ### Why are the changes needed? Till now the only way to check this by finding the container id of the AM and check the logs either using Yarn utility or Yarn RM Application History server. This PR is for adding the spark AM log link for spark job running in the client mode for yarn. Instead of searching the container id and then find the logs. We can directly check in the Spark UI ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Added the unit test also checked the Spark UI **In Yarn Client mode** Before Change ![image](https://user-images.githubusercontent.com/34540906/112644861-e1733200-8e6b-11eb-939b-c76ca9902a4e.png) After the Change - The AM info is there ![image](https://user-images.githubusercontent.com/34540906/115264198-b7075280-a153-11eb-98f3-2aed66ffad2a.png) AM Log ![image](https://user-images.githubusercontent.com/34540906/112645680-c0f7a780-8e6c-11eb-8b82-4ccc0aee927b.png) **In Yarn Cluster Mode** - The AM log link will not be there ![image](https://user-images.githubusercontent.com/34540906/112649512-86900980-8e70-11eb-9b37-69d5c4b53ffa.png) Closes #31974 from SaurabhChawla100/SPARK-34877. Authored-by: SaurabhChawla <s.saurabhtim@gmail.com> Signed-off-by: Thomas Graves <tgraves@apache.org> * [SPARK-34035][SQL] Refactor ScriptTransformation to remove input parameter and replace it by child.output ### What changes were proposed in this pull request? Refactor ScriptTransformation to remove input parameter and replace it by child.output ### Why are the changes needed? refactor code ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existed UT Closes #32228 from AngersZhuuuu/SPARK-34035. Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com> Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [SPARK-34338][SQL] Report metrics from Datasource v2 scan ### What changes were proposed in this pull request? This patch proposes to leverage `CustomMetric`, `CustomTaskMetric` API to report custom metrics from DS v2 scan to Spark. ### Why are the changes needed? This is related to #31398. In SPARK-34297, we want to add a couple of metrics when reading from Kafka in SS. We need some public API change in DS v2 to make it possible. This extracts only DS v2 change and make it general for DS v2 instead of micro-batch DS v2 API. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Unit test. Implement a simple test DS v2 class locally and run it: ```scala scala> import org.apache.spark.sql.execution.datasources.v2._ import org.apache.spark.sql.execution.datasources.v2._ scala> classOf[CustomMetricDataSourceV2].getName res0: String = org.apache.spark.sql.execution.datasources.v2.CustomMetricDataSourceV2 scala> val df = spark.read.format(res0).load() df: org.apache.spark.sql.DataFrame = [i: int, j: int] scala> df.collect ``` <img width="703" alt="Screen Shot 2021-03-30 at 11 07 13 PM" src="https://user-images.githubusercontent.com/68855/113098080-d8a49800-91ac-11eb-8681-be408a0f2e69.png"> Closes #31451 from viirya/dsv2-metrics. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [SPARK-35145][SQL] CurrentOrigin should support nested invoking ### What changes were proposed in this pull request? `CurrentOrigin` is a thread-local variable to track the original SQL line position in plan/expression. Usually, we set `CurrentOrigin`, create `TreeNode` instances, and reset `CurrentOrigin`. This PR updates the last step to set `CurrentOrigin` to its previous value, instead of resetting it. This is necessary when we invoke `CurrentOrigin` in a nested way, like with subqueries. ### Why are the changes needed? To keep the original SQL line position in the error message in more cases. ### Does this PR introduce _any_ user-facing change? No, only minor error message changes. ### How was this patch tested? existing tests Closes #32249 from cloud-fan/origin. Authored-by: Wenchen Fan <wenchen@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> * [SPARK-34472][YARN] Ship ivySettings file to driver in cluster mode ### What changes were proposed in this pull request? In YARN, ship the `spark.jars.ivySettings` file to the driver when using `cluster` deploy mode so that `addJar` is able to find it in order to resolve ivy paths. ### Why are the changes needed? SPARK-33084 introduced support for Ivy paths in `sc.addJar` or Spark SQL `ADD JAR`. If we use a custom ivySettings file using `spark.jars.ivySettings`, it is loaded at https://github.com/apache/spark/blob/b26e7b510bbaee63c4095ab47e75ff2a70e377d7/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala#L1280. However, this file is only accessible on the client machine. In YARN cluster mode, this file is not available on the driver and so `addJar` fails to find it. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Added unit tests to verify that the `ivySettings` file is localized by the YARN client and that a YARN cluster mode application is able to find to load the `ivySettings` file. Closes #31591 from shardulm94/SPARK-34472. Authored-by: Shardul Mahadik <smahadik@linkedin.com> Signed-off-by: Thomas Graves <tgraves@apache.org> * [SPARK-35153][SQL] Make textual representation of ANSI interval operators more readable ### What changes were proposed in this pull request? In the PR, I propose to override the `sql` and `toString` methods of the expressions that implement operators over ANSI intervals (`YearMonthIntervalType`/`DayTimeIntervalType`), and replace internal expression class names by operators like `*`, `/` and `-`. ### Why are the changes needed? Proposed methods should make the textual representation of such operators more readable, and potentially parsable by Spark SQL parser. ### Does this PR introduce _any_ user-facing change? Yes. This can influence on column names. ### How was this patch tested? By running existing test suites for interval and datetime expressions, and re-generating the `*.sql` tests: ``` $ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z interval.sql" $ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z datetime.sql" ``` Closes #32262 from MaxGekk/interval-operator-sql. Authored-by: Max Gekk <max.gekk@gmail.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-35132][BUILD][CORE] Upgrade netty-all to 4.1.63.Final ### What changes were proposed in this pull request? There are 3 CVE problems were found after netty 4.1.51.Final as follows: - [CVE-2021-21409](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-21409) - [CVE-2021-21295](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-21295) - [CVE-2021-21290](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-21290) So the main change of this pr is upgrade netty-all to 4.1.63.Final avoid these potential risks. Another change is to clean up deprecated api usage: [Tiny caches have been merged into small caches](https://github.com/netty/netty/blob/4.1/buffer/src/main/java/io/netty/buffer/PooledByteBufAllocator.java#L447-L455)(after [netty#10267](https://github.com/netty/netty/pull/10267)) and [should use PooledByteBufAllocator(boolean, int, int, int, int, int, int, boolean, int)](https://github.com/netty/netty/blob/4.1/buffer/src/main/java/io/netty/buffer/PooledByteBufAllocator.java#L227-L239) api to create `PooledByteBufAllocator`. ### Why are the changes needed? Upgrade netty-all to 4.1.63.Final avoid CVE problems. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Pass the Jenkins or GitHub Action Closes #32227 from LuciferYang/SPARK-35132. Authored-by: yangjie01 <yangjie01@baidu.com> Signed-off-by: Sean Owen <srowen@gmail.com> * [SPARK-35044][SQL][FOLLOWUP][TEST-HADOOP2.7] Fix hadoop 2.7 test due to diff between hadoop 2.7 and hadoop 3 ### What changes were proposed in this pull request? dfs.replication is inconsistent from hadoop 2.x to 3.x, so in this PR we use `dfs.hosts` to verify per https://github.com/apache/spark/pull/32144#discussion_r616833099 ``` == Results == !== Correct Answer - 1 == == Spark Answer - 1 == !struct<> struct<key:string,value:string> ![dfs.replication,<undefined>] [dfs.replication,3] ``` ### Why are the changes needed? fix Jenkins job with Hadoop 2.7 ### Does this PR introduce _any_ user-facing change? test only change ### How was this patch tested? test only change Closes #32263 from yaooqinn/SPARK-35044-F. Authored-by: Kent Yao <yao@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org> * [SPARK-35113][SQL] Support ANSI intervals in the Hash expression ### What changes were proposed in this pull request? Support ANSI interval in HashExpression and add UT ### Why are the changes needed? Support ANSI interval in HashExpression ### Does this PR introduce _any_ user-facing change? User can pass ANSI interval in HashExpression function ### How was this patch tested? Added UT Closes #32259 from AngersZhuuuu/SPARK-35113. Authored-by: Angerszhuuuu <angers.zhu@gmail.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> * [SPARK-35120][INFRA][FOLLOW-UP] Try catch an error to show the correct guidance ### What changes were proposed in this pull request? This PR proposes to handle 404 not found, see https://github.com/apache/spark/pull/32255/checks?check_run_id=2390446579 as an example. If a fork does not have any previous workflow runs, it seems throwing 4…

[SPARK-34581][SQL] Don't optimize out grouping expressions from aggre…

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…gate expressions

github-actions bot added the SQL label Mar 21, 2021

comment fix

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dongjoon-hyun reviewed Mar 21, 2021

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maropu reviewed Mar 22, 2021

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peter-toth added 2 commits March 22, 2021 09:10

move logic to the beginning of optimization, simplify test

2293fd4

regenerate approved plans

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Merge branch 'master' into SPARK-34581-keep-grouping-expressions

04e61c5

# Conflicts: # sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala

maropu reviewed Mar 23, 2021

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define GroupingExpression as TaggingExpression

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maropu reviewed Mar 24, 2021

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move test to SQLQueryTestSuite

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fix validConstraints, minor changes

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cloud-fan closed this in c8d78a7 Apr 19, 2021

peter-toth mentioned this pull request Apr 29, 2021

[SPARK-34581][SQL] Don't optimize out grouping expressions from aggregate expressions without aggregate function #32396

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[SPARK-34581][SQL] Don't optimize out grouping expressions from aggregate expressions without aggregate function #31913

[SPARK-34581][SQL] Don't optimize out grouping expressions from aggregate expressions without aggregate function #31913

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