Attila Zsolt Piros created SPARK-22806:
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Summary: Window Aggregate functions: unexpected result at ordered
partition
Key: SPARK-22806
URL: https://issues.apache.org/jira/browse/SPARK-22806
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.3.0
Reporter: Attila Zsolt Piros
I got different results for the aggregate function (even for sum and count)
when the partition is ordered "Window.partitionBy(column).orderBy(column))" and
when it is not ordered 'Window.partitionBy("column")".
Example:
test("count, sum, stddev_pop functions over window") {
val df = Seq(
("a", 1, 100.0),
("b", 1, 200.0)).toDF("key", "partition", "value")
df.createOrReplaceTempView("window_table")
checkAnswer(
df.select(
$"key",
count("value").over(Window.partitionBy("partition")),
sum("value").over(Window.partitionBy("partition")),
stddev_pop("value").over(Window.partitionBy("partition"))
),
Seq(
Row("a", 2, 300.0, 50.0),
Row("b", 2, 300.0, 50.0)))
}
test("count, sum, stddev_pop functions over ordered by window") {
val df = Seq(
("a", 1, 100.0),
("b", 1, 200.0)).toDF("key", "partition", "value")
df.createOrReplaceTempView("window_table")
checkAnswer(
df.select(
$"key",
count("value").over(Window.partitionBy("partition").orderBy("key")),
sum("value").over(Window.partitionBy("partition").orderBy("key")),
stddev_pop("value").over(Window.partitionBy("partition").orderBy("key"))
),
Seq(
Row("a", 2, 300.0, 50.0),
Row("b", 2, 300.0, 50.0)))
}
The "count, sum, stddev_pop functions over ordered by window" fails with the
error:
== Results ==
!== Correct Answer - 2 == == Spark Answer - 2 ==
!struct<> struct<key:string,count(value) OVER (PARTITION BY
partition ORDER BY key ASC NULLS FIRST unspecifiedframe$()):bigint,sum(value)
OVER (PARTITION BY partition ORDER BY key ASC NULLS FIRST
unspecifiedframe$()):double,stddev_pop(value) OVER (PARTITION BY partition
ORDER BY key ASC NULLS FIRST unspecifiedframe$()):double>
![a,2,300.0,50.0] [a,1,100.0,0.0]
[b,2,300.0,50.0] [b,2,300.0,50.0]
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