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Posted to issues@spark.apache.org by "shahid (Jira)" <ji...@apache.org> on 2021/05/10 22:29:00 UTC

[jira] [Commented] (SPARK-35079) Transform with udf gives incorrect result

    [ https://issues.apache.org/jira/browse/SPARK-35079?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17342171#comment-17342171 ] 

shahid commented on SPARK-35079:
--------------------------------

Seems It is not reproducible with master branch?
+-----------------------------------------------------------------------------+
|transform(value, lambdafunction(UDF(lambda x_0#3993), namedlambdavariable()))|
+-----------------------------------------------------------------------------+
|                                                                    [a, b, c]|
+-----------------------------------------------------------------------------+



> Transform with udf gives incorrect result
> -----------------------------------------
>
>                 Key: SPARK-35079
>                 URL: https://issues.apache.org/jira/browse/SPARK-35079
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 3.1.1
>            Reporter: koert kuipers
>            Priority: Minor
>
> i think this is a correctness bug in spark 3.1.1
> the behavior is correct in spark 3.0.1
> in spark 3.0.1:
> {code:java}
> scala> import spark.implicits._
> scala> import org.apache.spark.sql.functions._
> scala> val x = Seq(Seq("aa", "bb", "cc")).toDF
> x: org.apache.spark.sql.DataFrame = [value: array<string>]
> scala> x.select(transform(col("value"), col => udf((_: String).drop(1)).apply(col))).show
> +---------------------------------------------------+
> |transform(value, lambdafunction(UDF(lambda 'x), x))|
> +---------------------------------------------------+
> |                                          [a, b, c]|
> +---------------------------------------------------+
> {code}
> in spark 3.1.1:
> {code:java}
> scala> import spark.implicits._
> scala> import org.apache.spark.sql.functions._
> scala> val x = Seq(Seq("aa", "bb", "cc")).toDF
> x: org.apache.spark.sql.DataFrame = [value: array<string>]
> scala> x.select(transform(col("value"), col => udf((_: String).drop(1)).apply(col))).show
> +---------------------------------------------------+
> |transform(value, lambdafunction(UDF(lambda 'x), x))|
> +---------------------------------------------------+
> |                                          [c, c, c]|
> +---------------------------------------------------+
> {code}



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