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https://issues.apache.org/jira/browse/SPARK-12880?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15105678#comment-15105678
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Herman van Hovell commented on SPARK-12880:
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You are ordering by {{YYYYMM}} and it looks like there is not an unique
ordering (for instance 200509). I don't think it is a given that sorting will
always yield the same ordering in such a case. As a result calling the
{{lag()}} function will not return the same results.
Please check if the {{YYYYMM}} values are unique for each {{product, bnd &
age}} tuple.
> Different results on groupBy after window function
> --------------------------------------------------
>
> Key: SPARK-12880
> URL: https://issues.apache.org/jira/browse/SPARK-12880
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.6.0
> Reporter: Saif Addin Ellafi
> Priority: Critical
>
> {noformat}
> scala> val overVint = Window.partitionBy("product", "bnd",
> "age").orderBy(asc("yyyymm"))
> scala> val df_data2 = df_data.withColumn("result", lag("baleom",
> 1).over(overVint))
> scala> df_data2.filter("product = 'MAIN' and bnd = 'High' and yyyymm =
> 200509").groupBy("yyyymm", "closed",
> "ever_closed").agg(sum("result").as("result")).show
> +------+------+-----------+--------------------+
> |yyyymm|closed|ever_closed| result|
> +------+------+-----------+--------------------+
> |200509| 1| 1|1.2672666129980398E7|
> |200509| 0| 0|2.7104834668856387E9|
> |200509| 0| 1| 1.151339011298214E8|
> +------+------+-----------+--------------------+
> scala> df_data2.filter("product = 'MAIN' and bnd = 'High' and yyyymm =
> 200509").groupBy("yyyymm", "closed",
> "ever_closed").agg(sum("result").as("result")).show
> +------+------+-----------+--------------------+
> |yyyymm|closed|ever_closed| result|
> +------+------+-----------+--------------------+
> |200509| 1| 1|1.2357681589980595E7|
> |200509| 0| 0| 2.709930867575646E9|
> |200509| 0| 1|1.1595048973981345E8|
> +------+------+-----------+--------------------+
> {noformat}
> Does NOT happen with columns not of the window function
> Happens both in cluster mode and local mode
> Before group by operation, data looks good and is consistent
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