[jira] [Resolved] (SPARK-20969) last() aggregate function fails returning the right answer with ordered windows
[
https://issues.apache.org/jira/browse/SPARK-20969?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Perrine Letellier resolved SPARK-20969.
---
Resolution: Not A Problem
> 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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[jira] [Comment Edited] (SPARK-20969) last() aggregate function fails returning the right answer with ordered windows
[
https://issues.apache.org/jira/browse/SPARK-20969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16038825#comment-16038825
]
Perrine Letellier edited comment on SPARK-20969 at 6/6/17 1:00 PM:
---
[~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 {{RANGE BETWEEN
UNBOUNDED PRECEDING AND CURRENT ROW}} ?
was (Author: pletelli):
[~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} ?
> 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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[jira] [Comment Edited] (SPARK-20969) last() aggregate function fails returning the right answer with ordered windows
[
https://issues.apache.org/jira/browse/SPARK-20969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16038825#comment-16038825
]
Perrine Letellier edited comment on SPARK-20969 at 6/6/17 1:00 PM:
---
[~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}} ?
was (Author: pletelli):
[~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 {{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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[jira] [Comment Edited] (SPARK-20969) last() aggregate function fails returning the right answer with ordered windows
[
https://issues.apache.org/jira/browse/SPARK-20969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16038825#comment-16038825
]
Perrine Letellier edited comment on SPARK-20969 at 6/6/17 12:59 PM:
[~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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[jira] [Comment Edited] (SPARK-20969) last() aggregate function fails returning the right answer with ordered windows
[
https://issues.apache.org/jira/browse/SPARK-20969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16038825#comment-16038825
]
Perrine Letellier edited comment on SPARK-20969 at 6/6/17 12:59 PM:
[~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 }} ?
was (Author: pletelli):
[~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} ?
> 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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[jira] [Commented] (SPARK-20969) last() aggregate function fails returning the right answer with ordered windows
[
https://issues.apache.org/jira/browse/SPARK-20969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16038825#comment-16038825
]
Perrine Letellier commented on SPARK-20969:
---
[~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} ?
> 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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[jira] [Updated] (SPARK-20969) last() aggregate function fails returning the right answer with ordered windows
[
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}
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[jira] [Updated] (SPARK-20969) last() aggregate function fails returning the right answer with ordered windows
[
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> 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}
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", "desc")
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> 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}
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[jira] [Updated] (SPARK-20969) last() aggregate function fails returning the right answer with ordered windows
[
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", "desc")
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}
was:
{code}
scala> val df = sc.parallelize(List(("i1", 1, "desc1"), ("i1", 1, "desc2"),
("i1", 2, "desc3"))).toDF("id", "ts", "desc")
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", "desc")
> 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}
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[jira] [Created] (SPARK-20969) last() aggregate function fails returning the right answer with ordered windows
Perrine Letellier created SPARK-20969:
-
Summary: 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
{code}
scala> val df = sc.parallelize(List(("i1", 1, "desc1"), ("i1", 1, "desc2"),
("i1", 2, "desc3"))).toDF("id", "ts", "desc")
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}
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