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https://issues.apache.org/jira/browse/SPARK-21900?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16152284#comment-16152284
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Sean Owen commented on SPARK-21900:
-----------------------------------
I don't feel strongly about it, but this is a reasonable issue to report.
Especially since it didn't seem like it acted this way in 2.2. I don't have a
suggested change but would be open to a patch for this if someone finds a
method to compute the higher-order moments more accurately without sacrificing
(much) speed.
> Numerical Error in simple Skewness Computation
> ----------------------------------------------
>
> Key: SPARK-21900
> URL: https://issues.apache.org/jira/browse/SPARK-21900
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Jakob Bach
> Priority: Minor
>
> The skewness() aggregate SQL function in the Scala implementation
> (org.apache.spark.sql.skewness) seems to be buggy .The following code
> {code:java}
> import org.apache.spark.sql.functions
> import org.apache.spark.sql.SparkSession
> object SkewTest {
> def main(args: Array[String]): Unit = {
> val spark = SparkSession.
> builder().
> appName("Skewness example").
> master("local[1]").
> getOrCreate()
>
> spark.createDataFrame(Seq(4,1,2,3).map(Tuple1(_))).agg(functions.skewness("_1")).show()
> }
> }
> {code}
> should output 0 (as it does for Seq(1,2,3,4)), but outputs
> {code:none}
> +--------------------+
> | skewness(_1)|
> +--------------------+
> |5.958081967793454...|
> +--------------------+
> {code}
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