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https://issues.apache.org/jira/browse/SPARK-30212?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17694144#comment-17694144
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Ritika Maheshwari commented on SPARK-30212:
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This is the suggested way to handle this issue(from stack overflow)
"The typical approach to solving this problem is with a self-join. You group by
whatever columns you need, compute the distinct count or any other aggregate
function that cannot be used as a window function, and then join back to your
original data."
So perhaps you can rewrite your query as
spark.sql("""SELECT T.*, (select COUNT(DISTINCT T2.DEMO_DATE) from
DEMO_COUNT_DISTINCT T2 group by T2.DEMO_DATE) UNIQ_DATES FROM
DEMO_COUNT_DISTINCT T
""").show
> COUNT(DISTINCT) window function should be supported
> ---------------------------------------------------
>
> Key: SPARK-30212
> URL: https://issues.apache.org/jira/browse/SPARK-30212
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.1.0
> Environment: Spark 2.4.4
> Scala 2.11.12
> Hive 2.3.6
> Reporter: Kernel Force
> Priority: Major
>
> Suppose we have a typical table in Hive like below:
> {code:sql}
> CREATE TABLE DEMO_COUNT_DISTINCT (
> demo_date string,
> demo_id string
> );
> {code}
> {noformat}
> +--------------------------------+------------------------------+
> | demo_count_distinct.demo_date | demo_count_distinct.demo_id |
> +--------------------------------+------------------------------+
> | 20180301 | 101 |
> | 20180301 | 102 |
> | 20180301 | 103 |
> | 20180401 | 201 |
> | 20180401 | 202 |
> +--------------------------------+------------------------------+
> {noformat}
> Now I want to count distinct number of DEMO_DATE but also reserve every
> columns' data in each row.
> So I use COUNT(DISTINCT) window function (which is also common in other
> mainstream databases like Oracle) in Hive beeline and it work:
> {code:sql}
> SELECT T.*, COUNT(DISTINCT T.DEMO_DATE) OVER(PARTITION BY NULL) UNIQ_DATES
> FROM DEMO_COUNT_DISTINCT T;
> {code}
> {noformat}
> +--------------+------------+-------------+
> | t.demo_date | t.demo_id | uniq_dates |
> +--------------+------------+-------------+
> | 20180401 | 202 | 2 |
> | 20180401 | 201 | 2 |
> | 20180301 | 103 | 2 |
> | 20180301 | 102 | 2 |
> | 20180301 | 101 | 2 |
> +--------------+------------+-------------+
> {noformat}
> But when I came to SparkSQL, it threw exception even if I run the same SQL.
> {code:sql}
> spark.sql("""
> SELECT T.*, COUNT(DISTINCT T.DEMO_DATE) OVER(PARTITION BY NULL) UNIQ_DATES
> FROM DEMO_COUNT_DISTINCT T
> """).show
> {code}
> {noformat}
> org.apache.spark.sql.AnalysisException: Distinct window functions are not
> supported: count(distinct DEMO_DATE#1) windowspecdefinition(null,
> specifiedwindowframe(RowFrame, unboundedpreceding$(),
> unboundedfollowing$()));;
> Project [demo_date#1, demo_id#2, UNIQ_DATES#0L]
> +- Project [demo_date#1, demo_id#2, UNIQ_DATES#0L, UNIQ_DATES#0L]
> +- Window [count(distinct DEMO_DATE#1) windowspecdefinition(null,
> specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$()))
> AS UNIQ_DATES#0L], [null]
> +- Project [demo_date#1, demo_id#2]
> +- SubqueryAlias `T`
> +- SubqueryAlias `default`.`demo_count_distinct`
> +- HiveTableRelation `default`.`demo_count_distinct`,
> org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [demo_date#1, demo_id#2]
> {noformat}
> Then I try to use countDistinct function but also got exceptions.
> {code:sql}
> spark.sql("""
> SELECT T.*, countDistinct(T.DEMO_DATE) OVER(PARTITION BY NULL) UNIQ_DATES
> FROM DEMO_COUNT_DISTINCT T
> """).show
> {code}
> {noformat}
> org.apache.spark.sql.AnalysisException: Undefined function: 'countDistinct'.
> This function is neither a registered temporary function nor a permanent
> function registered in the database 'default'.; line 2 pos 12
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$$anonfun$apply$15$$anonfun$applyOrElse$49.apply(Analyzer.scala:1279)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$$anonfun$apply$15$$anonfun$applyOrElse$49.apply(Analyzer.scala:1279)
> at
> org.apache.spark.sql.catalyst.analysis.package$.withPosition(package.scala:53)
> ......
> {noformat}
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