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From "Sean R. Owen (Jira)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-29325) approxQuantile() results are incorrect and vary significantly for small changes in relativeError
Date Sat, 26 Oct 2019 22:45:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-29325?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16960449#comment-16960449
] 

Sean R. Owen commented on SPARK-29325:
--------------------------------------

My guess is that this was resolved by https://issues.apache.org/jira/browse/SPARK-29336

> approxQuantile() results are incorrect and vary significantly for small changes in relativeError
> ------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-29325
>                 URL: https://issues.apache.org/jira/browse/SPARK-29325
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.3.2, 2.4.4
>         Environment: I was using OSX 10.14.6.
> I was using Scala 2.11.12 and Spark 2.4.4.
> I also verified the bug exists for Scala 2.11.8 and Spark 2.3.2.
>            Reporter: James Verbus
>            Priority: Major
>              Labels: correctness
>         Attachments: 20191001_example_data_approx_quantile_bug.zip
>
>
> The [approxQuantile() method|https://github.com/apache/spark/blob/3b1674cb1f244598463e879477d89632b0817578/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala#L40]
returns sometimes incorrect results that are sensitively dependent upon the choice of the
relativeError.
> Below is an example in the latest Spark version (2.4.4). You can see the result varies
significantly for modest changes in the specified relativeError parameter. The result varies
much more than would be expected based upon the relativeError parameter.
>  
> {code:java}
> Welcome to
>       ____              __
>      / __/__  ___ _____/ /__
>     _\ \/ _ \/ _ `/ __/  '_/
>    /___/ .__/\_,_/_/ /_/\_\   version 2.4.4
>       /_/
>          
> Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_212)
> Type in expressions to have them evaluated.
> Type :help for more information.
> scala> val df = spark.read.format("csv").option("header", "true").option("inferSchema",
"true").load("./20191001_example_data_approx_quantile_bug")
> df: org.apache.spark.sql.DataFrame = [value: double]
> scala> df.stat.approxQuantile("value", Array(0.9), 0)
> res0: Array[Double] = Array(0.5929591082174609)
> scala> df.stat.approxQuantile("value", Array(0.9), 0.001)
> res1: Array[Double] = Array(0.67621027121925)
> scala> df.stat.approxQuantile("value", Array(0.9), 0.002)
> res2: Array[Double] = Array(0.5926195654486178)
> scala> df.stat.approxQuantile("value", Array(0.9), 0.003)
> res3: Array[Double] = Array(0.5924693999048418)
> scala> df.stat.approxQuantile("value", Array(0.9), 0.004)
> res4: Array[Double] = Array(0.67621027121925)
> scala> df.stat.approxQuantile("value", Array(0.9), 0.005)
> res5: Array[Double] = Array(0.5923925937051544) 
> {code}
> I attached a zip file containing the data used for the above example demonstrating the
bug.
> Also, the following demonstrates that there is data for intermediate quantile values
between the 0.5926195654486178 and 0.67621027121925 values observed above.
> {code:java}
> scala> df.stat.approxQuantile("value", Array(0.9), 0.0)
> res10: Array[Double] = Array(0.5929591082174609)
> scala> df.stat.approxQuantile("value", Array(0.91), 0.0)
> res11: Array[Double] = Array(0.5966354540849995)
> scala> df.stat.approxQuantile("value", Array(0.92), 0.0)
> res12: Array[Double] = Array(0.6015676591185595)
> scala> df.stat.approxQuantile("value", Array(0.93), 0.0)
> res13: Array[Double] = Array(0.6029240823799614)
> scala> df.stat.approxQuantile("value", Array(0.94), 0.0)
> res14: Array[Double] = Array(0.6117645471000034)
> scala> df.stat.approxQuantile("value", Array(0.95), 0.0)
> res15: Array[Double] = Array(0.6185162204274052)
> scala> df.stat.approxQuantile("value", Array(0.96), 0.0)
> res16: Array[Double] = Array(0.625983000807062)
> scala> df.stat.approxQuantile("value", Array(0.97), 0.0)
> res17: Array[Double] = Array(0.6306892943412258)
> scala> df.stat.approxQuantile("value", Array(0.98), 0.0)
> res18: Array[Double] = Array(0.6365567375994333)
> scala> df.stat.approxQuantile("value", Array(0.99), 0.0)
> res19: Array[Double] = Array(0.6554479197566019)
> {code}



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