[SPARK-29325] approxQuantile() results are incorrect and vary significantly for small changes in relativeError - ASF JIRA

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Details

Type: Bug
Status: Resolved
Priority: Major
Resolution: Duplicate
Affects Version/s: 2.3.2, 2.4.4
Fix Version/s: None
Component/s: SQL
Labels:
- correctness
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.

Description

The approxQuantile() method 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.

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)

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.

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)