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https://issues.apache.org/jira/browse/SPARK-20969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16038825#comment-16038825
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Perrine Letellier edited comment on SPARK-20969 at 6/6/17 12:59 PM:
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[~viirya] Thanks for your answer !
I could get the expected result by specifying
{code}Window.partitionBy("id").orderBy(col("ts").asc).rowsBetween(Window.unboundedPreceding,
Window.unboundedFollowing){code} instead of simply
{code}Window.partitionBy("id").orderBy(col("ts").asc) {code}.
Is it documented in any api doc that the default frame is {code}RANGE BETWEEN
UNBOUNDED PRECEDING AND CURRENT ROW{code} ?
was (Author: pletelli):
[~viirya] Thanks for your answer !
I could get the expected result by specifying
{{Window.partitionBy("id").orderBy(col("ts").asc).rowsBetween(Window.unboundedPreceding,
Window.unboundedFollowing) }} instead of simply {{
Window.partitionBy("id").orderBy(col("ts").asc) }}.
Is it documented in any api doc that the default frame is {{ RANGE BETWEEN
UNBOUNDED PRECEDING AND CURRENT ROW }} ?
> last() aggregate function fails returning the right answer with ordered
> windows
> -------------------------------------------------------------------------------
>
> Key: SPARK-20969
> URL: https://issues.apache.org/jira/browse/SPARK-20969
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.1
> Reporter: Perrine Letellier
>
> The column on which `orderBy` is performed is considered as another column on
> which to partition.
> {code}
> scala> val df = sc.parallelize(List(("i1", 1, "desc1"), ("i1", 1, "desc2"),
> ("i1", 2, "desc3"))).toDF("id", "ts", "description")
> scala> import org.apache.spark.sql.expressions.Window
> scala> val window = Window.partitionBy("id").orderBy(col("ts").asc)
> scala> df.withColumn("last", last(col("description")).over(window)).show
> +---+---+-----------+-----+
> | id| ts|description| last|
> +---+---+-----------+-----+
> | i1| 1| desc1|desc2|
> | i1| 1| desc2|desc2|
> | i1| 2| desc3|desc3|
> +---+---+-----------+-----+
> {code}
> However what is expected is the same answer as if asking for `first()` with a
> window with descending order.
> {code}
> scala> val window = Window.partitionBy("id").orderBy(col("ts").desc)
> scala> df.withColumn("hackedLast",
> first(col("description")).over(window)).show
> +---+---+-----------+----------+
> | id| ts|description|hackedLast|
> +---+---+-----------+----------+
> | i1| 2| desc3| desc3|
> | i1| 1| desc1| desc3|
> | i1| 1| desc2| desc3|
> +---+---+-----------+----------+
> {code}
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