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https://issues.apache.org/jira/browse/SPARK-42346?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17685466#comment-17685466
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Ritika Maheshwari commented on SPARK-42346:
-------------------------------------------
Hello added three rows to input_table. Still no error. I do have DPP enabled.
*********************************************************************
Using Scala version 2.12.15 (Java HotSpot(TM) 64-Bit Server VM, Java 12.0.2)
Type in expressions to have them evaluated.
Type :help for more information.
scala> val df = Seq(("a","b"),("c","d"),("e","f")).toDF("surname","first_name")
*df*: *org.apache.spark.sql.DataFrame* = [surname: string, first_name: string]
scala> df.createOrReplaceTempView("input_table")
scala> spark.sql("select(Select Count(Distinct first_name) from input_table) As
distinct_value_count from input_table Union all select (select count(Distinct
surname) from input_table) as distinct_value_count from input_table").show()
+--------------------+
|distinct_value_count|
+--------------------+
| 3|
| 3|
| 3|
| 3|
| 3|
| 3|
+--------------------+
**************************************************************
AdaptiveSparkPlan isFinalPlan=false
+- Union
:- Project [cast(Subquery subquery#145, [id=#571] as string) AS
distinct_value_count#161]
: : +- Subquery subquery#145, [id=#571]
: : +- AdaptiveSparkPlan isFinalPlan=false
: : +- HashAggregate(keys=[], functions=[count(distinct
first_name#8)], output=[count(DISTINCT first_name)#152L])
: : +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#569]
: : +- HashAggregate(keys=[],
functions=[partial_count(distinct first_name#8)], output=[count#167L])
: : +- HashAggregate(keys=[first_name#8], functions=[],
output=[first_name#8])
: : +- Exchange hashpartitioning(first_name#8, 200),
ENSURE_REQUIREMENTS, [id=#565]
: : +- HashAggregate(keys=[first_name#8],
functions=[], output=[first_name#8])
: : +- LocalTableScan [first_name#8]
: +- LocalTableScan [_1#2, _2#3]
+- Project [cast(Subquery subquery#147, [id=#590] as string) AS
distinct_value_count#163]
: +- Subquery subquery#147, [id=#590]
: +- AdaptiveSparkPlan isFinalPlan=false
: +- HashAggregate(keys=[], functions=[count(distinct surname#7)],
output=[count(DISTINCT surname)#154L])
: +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#588]
: +- HashAggregate(keys=[],
functions=[partial_count(distinct surname#7)], output=[count#170L])
: +- HashAggregate(keys=[surname#7], functions=[],
output=[surname#7])
: +- Exchange hashpartitioning(surname#7, 200),
ENSURE_REQUIREMENTS, [id=#584]
: +- HashAggregate(keys=[surname#7], functions=[],
output=[surname#7])
: +- LocalTableScan [surname#7]
+- LocalTableScan [_1#149, _2#150]
> distinct(count colname) with UNION ALL causes query analyzer bug
> ----------------------------------------------------------------
>
> Key: SPARK-42346
> URL: https://issues.apache.org/jira/browse/SPARK-42346
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.3.0, 3.4.0, 3.5.0
> Reporter: Robin
> Assignee: Peter Toth
> Priority: Major
> Fix For: 3.3.2, 3.4.0, 3.5.0
>
>
> If you combine a UNION ALL with a count(distinct colname) you get a query
> analyzer bug.
>
> This behaviour is introduced in 3.3.0. The bug was not present in 3.2.1.
>
> Here is a reprex in PySpark:
> {{df_pd = pd.DataFrame([}}
> {{ \{'surname': 'a', 'first_name': 'b'}}}
> {{])}}
> {{df_spark = spark.createDataFrame(df_pd)}}
> {{df_spark.createOrReplaceTempView("input_table")}}
> {{sql = """}}
> {{SELECT }}
> {{ (SELECT Count(DISTINCT first_name) FROM input_table) }}
> {{ AS distinct_value_count}}
> {{FROM input_table}}
> {{UNION ALL}}
> {{SELECT }}
> {{ (SELECT Count(DISTINCT surname) FROM input_table) }}
> {{ AS distinct_value_count}}
> {{FROM input_table """}}
> {{spark.sql(sql).toPandas()}}
>
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