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https://issues.apache.org/jira/browse/SPARK-32038?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17142265#comment-17142265
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Mithun Radhakrishnan commented on SPARK-32038:
----------------------------------------------
[~viirya], [~dongjoon], thank you. I'm amazed at the quick resolution of this
bug.
> Regression in handling NaN values in COUNT(DISTINCT)
> ----------------------------------------------------
>
> Key: SPARK-32038
> URL: https://issues.apache.org/jira/browse/SPARK-32038
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Mithun Radhakrishnan
> Assignee: L. C. Hsieh
> Priority: Blocker
> Labels: correctness
> Fix For: 3.0.1, 3.1.0
>
>
> There seems to be a regression in Spark 3.0.0, with regard to how {{NaN}}
> values are normalized/handled in {{COUNT(DISTINCT ...)}}. Here is an
> illustration:
> {code:scala}
> case class Test( uid:String, score:Float)
> val POS_NAN_1 = java.lang.Float.intBitsToFloat(0x7f800001)
> val POS_NAN_2 = java.lang.Float.intBitsToFloat(0x7fffffff)
> val rows = Seq(
> Test("mithunr", Float.NaN),
> Test("mithunr", POS_NAN_1),
> Test("mithunr", POS_NAN_2),
> Test("abellina", 1.0f),
> Test("abellina", 2.0f)
> ).toDF.createOrReplaceTempView("mytable")
> spark.sql(" select uid, count(distinct score) from mytable group by 1 order
> by 1 asc ").show
> {code}
> Here are the results under Spark 3.0.0:
> {code:java|title=Spark 3.0.0 (single aggregation)}
> +--------+---------------------+
> | uid|count(DISTINCT score)|
> +--------+---------------------+
> |abellina| 2|
> | mithunr| 3|
> +--------+---------------------+
> {code}
> Note that the count against {{mithunr}} is {{3}}, accounting for each
> distinct value for {{NaN}}.
> The right results are returned when another aggregation is added to the GBY:
> {code:scala|title=Spark 3.0.0 (multiple aggregations)}
> scala> spark.sql(" select uid, count(distinct score), max(score) from mytable
> group by 1 order by 1 asc ").show
> +--------+---------------------+----------+
> | uid|count(DISTINCT score)|max(score)|
> +--------+---------------------+----------+
> |abellina| 2| 2.0|
> | mithunr| 1| NaN|
> +--------+---------------------+----------+
> {code}
> Also, note that Spark 2.4.6 normalizes the {{DISTINCT}} expression correctly:
> {code:scala|title=Spark 2.4.6}
> scala> spark.sql(" select uid, count(distinct score) from mytable group by 1
> order by 1 asc ").show
> +--------+---------------------+
> | uid|count(DISTINCT score)|
> +--------+---------------------+
> |abellina| 2|
> | mithunr| 1|
> +--------+---------------------+
> {code}
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