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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:20:12 UTC
[jira] [Updated] (SPARK-11203) UDF doesn't support charType column
and lit function doesn't allow charType as argument
[ https://issues.apache.org/jira/browse/SPARK-11203?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-11203:
---------------------------------
Labels: bulk-closed (was: )
> UDF doesn't support charType column and lit function doesn't allow charType as argument
> ---------------------------------------------------------------------------------------
>
> Key: SPARK-11203
> URL: https://issues.apache.org/jira/browse/SPARK-11203
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 1.5.0
> Reporter: M Bharat lal
> Priority: Minor
> Labels: bulk-closed
>
> We have two issues
> 1) We cannot create dataframe with Char Type , see below example
> scala> val employee = sqlContext.createDataFrame(Seq((1,"John"))).toDF("id","name")
> employee: org.apache.spark.sql.DataFrame = [id: int, name: string]
> scala> employee.withColumn("grade",lit('A'))
> java.lang.RuntimeException: Unsupported literal type class java.lang.Character A
> at org.apache.spark.sql.catalyst.expressions.Literal$.apply(literals.scala:49)
> at org.apache.spark.sql.functions$.lit(functions.scala:89)
> 2) we have a function which takes string and char as input parameters and returns position of char in given string.
>
> registered function as UDF and called the same UDF with characters literal which gave the below exception. Literal function doesn't support character as argument
> scala> def strPos(x:String,c:Char):Integer = {x.indexOf(c)}
> strPos: (x: String, c: Char)Integer
> scala> sqlContext.udf.register("strPos",strPos _)
> res13: org.apache.spark.sql.UserDefinedFunction = UserDefinedFunction(<function2>,IntegerType,List())
> scala> df.select( callUDF("strPos",$"name",lit('J')))
> java.lang.RuntimeException: Unsupported literal type class java.lang.Character J
> at org.apache.spark.sql.catalyst.expressions.Literal$.apply(literals.scala:49)
> Can you please add this support or let us know if there is any other work around to achieve this
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