Maxim Gekk created SPARK-32908:
----------------------------------
Summary: percentile_approx() returns incorrect results
Key: SPARK-32908
URL: https://issues.apache.org/jira/browse/SPARK-32908
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.4.8, 3.0.2, 3.1.0
Reporter: Maxim Gekk
Read input data from the attached CSV file:
{code:scala}
val df = spark.read.option("header", "true")
.option("inferSchema", "true")
.csv("/Users/maximgekk/tmp/tr_rat_resampling_score.csv")
.repartition(1)
df.createOrReplaceTempView(table)
{code}
Calculate the 0.77 percentile with accuracy 1e-05:
{code:scala}
spark.sql(
s"""SELECT
| percentile_approx(tr_rat_resampling_score, 0.77, 100000)
|FROM $table
""".stripMargin).show
{code}
{code}
+------------------------------------------------------------------------+
|percentile_approx(tr_rat_resampling_score, CAST(0.77 AS DOUBLE), 100000)|
+------------------------------------------------------------------------+
| 1000|
+------------------------------------------------------------------------+
{code}
The same for smaller accuracy 0.001:
{code}
+----------------------------------------------------------------------+
|percentile_approx(tr_rat_resampling_score, CAST(0.77 AS DOUBLE), 1000)|
+----------------------------------------------------------------------+
| 18|
+----------------------------------------------------------------------+
{code}
and better accuracy 1e-06:
{code}
+-------------------------------------------------------------------------+
|percentile_approx(tr_rat_resampling_score, CAST(0.77 AS DOUBLE), 1000000)|
+-------------------------------------------------------------------------+
| 17|
+-------------------------------------------------------------------------+
{code}
For the accuracy 1e-05, the result must be around 17-18 but not 1000.
Here is percentile calculation in Google Sheets for the same input:
https://docs.google.com/spreadsheets/d/1Y1i4Td6s9jZQ-bD4IRTESLXP3UxKpqJSXGtmx0Q5TA0/edit?usp=sharing
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