[ https://issues.apache.org/jira/browse/SPARK-20969?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Perrine Letellier updated SPARK-20969: -------------------------------------- Description: 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} was: 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> 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("last", first(col("description")).over(window)).show +---+---+-----+-----+ | id| ts| description| last| +---+---+-----+-----+ | i1| 2|desc3|desc3| | i1| 1|desc1|desc3| | i1| 1|desc2|desc3| +---+---+-----+-----+ {code} > 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} -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org