Re: [SQL]Analysis failed when combining Window function and GROUP BY in Spark2.x
You are seeing a bug in the Hive parser. Hive drops the window clause when
it encounters a count(distinct ...). See
https://issues.apache.org/jira/browse/HIVE-10141 for more information.
Spark 1.6 plans this as a regular distinct aggregate (dropping the window
clause), which is wrong. Spark 2.x uses its own parser, and it does not
allow you to do use 'distinct' aggregates in window functions. You are
getting this error because aggregates are planned before a windows, and the
aggregate cannot find b in its grouping by expressions.
On Wed, Mar 8, 2017 at 5:21 AM, StanZhai wrote:
> We can reproduce this using the following code:
>
> val spark =
> SparkSession.builder().appName("test").master("local").getOrCreate()
>
> val sql1 =
> """
> |create temporary view tb as select * from values
> |(1, 0),
> |(1, 0),
> |(2, 0)
> |as grouping(a, b)
> """.stripMargin
>
> val sql =
> """
> |select count(distinct(b)) over (partition by a) from tb group by a
> """.stripMargin
>
> spark.sql(sql1)
> spark.sql(sql).show()
>
> It will throw exception like this:
>
> Exception in thread "main" org.apache.spark.sql.AnalysisException: expression
> 'tb.`b`' is neither present in the group by, nor is it an aggregate function.
> Add to group by or wrap in first() (or first_value) if you don't care which
> value you get.;;
> Project [count(DISTINCT b) OVER (PARTITION BY a ROWS BETWEEN UNBOUNDED
> PRECEDING AND UNBOUNDED FOLLOWING)#4L]
> +- Project [b#1, a#0, count(DISTINCT b) OVER (PARTITION BY a ROWS BETWEEN
> UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#4L, count(DISTINCT b) OVER
> (PARTITION BY a ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#4L]
>+- Window [count(distinct b#1) windowspecdefinition(a#0, ROWS BETWEEN
> UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS count(DISTINCT b) OVER
> (PARTITION BY a ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED
> FOLLOWING)#4L], [a#0]
> +- Aggregate [a#0], [b#1, a#0]
> +- SubqueryAlias tb
> +- Project [a#0, b#1]
>+- SubqueryAlias grouping
> +- LocalRelation [a#0, b#1]
>
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:40)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:58)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1(CheckAnalysis.scala:220)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$7.apply(CheckAnalysis.scala:247)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$7.apply(CheckAnalysis.scala:247)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:247)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:125)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:125)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:125)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
> at
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
> at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:582)
>
> But, there is no exception in Spark 1.6.x.
>
> I think the sql select count(distinct(b)) over (partition by a) from tb
> group by a should be executed. I've no idea about the exception. Is this
> in line with expectations?
>
> Any help is appreciated!
>
> Best,
>
> Stan
>
>
> -
Re:Re: [SQL]Analysis failed when combining Window function and GROUP BY in Spark2.x
Thanks for your reply!
I know what's going on now.
"Herman van Hövell tot Westerflier-2 [via Apache Spark Developers
List]" wroted at 2017-03-08 21:35:
You are seeing a bug in the Hive parser. Hive drops the window clause when it
encounters a count(distinct ...). See
https://issues.apache.org/jira/browse/HIVE-10141 for more information.
Spark 1.6 plans this as a regular distinct aggregate (dropping the window
clause), which is wrong. Spark 2.x uses its own parser, and it does not allow
you to do use 'distinct' aggregates in window functions. You are getting this
error because aggregates are planned before a windows, and the aggregate cannot
find b in its grouping by expressions.
On Wed, Mar 8, 2017 at 5:21 AM, StanZhai <[hidden email]> wrote:
We can reproduce this using the following code:
val spark = SparkSession.builder().appName("test").master("local").getOrCreate()
val sql1 =
"""
|create temporary view tb as select * from values
|(1, 0),
|(1, 0),
|(2, 0)
|as grouping(a, b)
""".stripMargin
val sql =
"""
|select count(distinct(b)) over (partition by a) from tb group by a
""".stripMargin
spark.sql(sql1)
spark.sql(sql).show()It will throw exception like this:
Exception in thread "main" org.apache.spark.sql.AnalysisException: expression
'tb.`b`' is neither present in the group by, nor is it an aggregate function.
Add to group by or wrap in first() (or first_value) if you don't care which
value you get.;;
Project [count(DISTINCT b) OVER (PARTITION BY a ROWS BETWEEN UNBOUNDED
PRECEDING AND UNBOUNDED FOLLOWING)#4L]
+- Project [b#1, a#0, count(DISTINCT b) OVER (PARTITION BY a ROWS BETWEEN
UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#4L, count(DISTINCT b) OVER
(PARTITION BY a ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#4L]
+- Window [count(distinct b#1) windowspecdefinition(a#0, ROWS BETWEEN
UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS count(DISTINCT b) OVER
(PARTITION BY a ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#4L],
[a#0]
+- Aggregate [a#0], [b#1, a#0]
+- SubqueryAlias tb
+- Project [a#0, b#1]
+- SubqueryAlias grouping
+- LocalRelation [a#0, b#1]
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:40)
at
org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:58)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1(CheckAnalysis.scala:220)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$7.apply(CheckAnalysis.scala:247)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$7.apply(CheckAnalysis.scala:247)
at scala.collection.immutable.List.foreach(List.scala:381)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:247)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:125)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:125)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:125)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
at
org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
at
org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:582)But, there is
no exception in Spark 1.6.x.
I think the sql select count(distinct(b)) over (partition by a) from tb group
by a should be executed. I've no idea about the exception. Is this in line with
expectations?
Any help is appreciated!
Best,
Stan
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