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https://issues.apache.org/jira/browse/SPARK-11725?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15005509#comment-15005509
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Reynold Xin commented on SPARK-11725:
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This is the problem of default value in codegen I suspect.
https://github.com/apache/spark/blob/22e96b87fb0a0eb4f2f1a8fc29a742ceabff952a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala#L229
> Let UDF to handle null value
> ----------------------------
>
> Key: SPARK-11725
> URL: https://issues.apache.org/jira/browse/SPARK-11725
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Reporter: Jeff Zhang
>
> I notice that currently spark will take the long field as -1 if it is null.
> Here's the sample code.
> {code}
> sqlContext.udf.register("f", (x:Int)=>x+1)
> df.withColumn("age2", expr("f(age)")).show()
> //////////////// Output ///////////////////////
> +----+-------+----+
> | age| name|age2|
> +----+-------+----+
> |null|Michael| 0|
> | 30| Andy| 31|
> | 19| Justin| 20|
> +----+-------+----+
> {code}
> I think for the null value we have 3 options
> * Use a special value to represent it (what spark does now)
> * Always return null if the udf input has null value argument
> * Let udf itself to handle null
> I would prefer the third option
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