[
https://issues.apache.org/jira/browse/SPARK-32908?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Maxim Gekk updated SPARK-32908:
-------------------------------
Description:
Read input data from the attached CSV file:
{code:scala}
val df = spark.read.option("header", "true")
.option("inferSchema", "true")
.csv("/Users/maximgekk/tmp/percentile_approx-input.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
was:
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
> 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
> Priority: Major
> Attachments: percentile_approx-input.csv
>
>
> Read input data from the attached CSV file:
> {code:scala}
> val df = spark.read.option("header", "true")
> .option("inferSchema", "true")
> .csv("/Users/maximgekk/tmp/percentile_approx-input.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
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
