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https://issues.apache.org/jira/browse/SPARK-32306?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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L. C. Hsieh resolved SPARK-32306.
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Fix Version/s: 3.1.0
Assignee: Maxim Gekk
Resolution: Fixed
> `approx_percentile` in Spark SQL gives incorrect results
> --------------------------------------------------------
>
> Key: SPARK-32306
> URL: https://issues.apache.org/jira/browse/SPARK-32306
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 2.4.4
> Reporter: Sean Malory
> Assignee: Maxim Gekk
> Priority: Major
> Fix For: 3.1.0
>
>
> The `approx_percentile` function in Spark SQL does not give the correct
> result. I'm not sure how incorrect it is; it may just be a boundary issue.
> From the docs:
> {quote}The accuracy parameter (default: 10000) is a positive numeric literal
> which controls approximation accuracy at the cost of memory. Higher value of
> accuracy yields better accuracy, 1.0/accuracy is the relative error of the
> approximation.
> {quote}
> This is not true. Here is a minimum example in `pyspark` where, essentially,
> the median of 5 and 8 is being calculated as 5:
> {code:python}
> import pyspark.sql.functions as psf
> df = spark.createDataFrame(
> [('bar', 5), ('bar', 8)], ['name', 'val']
> )
> median = psf.expr('percentile_approx(val, 0.5, 2147483647)')
> df.groupBy('name').agg(median.alias('median')) # gives the median as 5
> {code}
> I've tested this with Spark v2.4.4, pyspark v2.4.5- although I suspect this
> is an issue with the underlying algorithm.
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