Is the answer here good for your case?
http://stackoverflow.com/questions/33151866/spark-udf-with-varargs [https://cdn.sstatic.net/Sites/stackoverflow/img/[email protected]?v=73d79a89bded]<http://stackoverflow.com/questions/33151866/spark-udf-with-varargs> scala - Spark UDF with varargs - Stack Overflow<http://stackoverflow.com/questions/33151866/spark-udf-with-varargs> stackoverflow.com UDFs don't support varargs* but you can pass an arbitrary number of columns wrapped using an array function: import org.apache.spark.sql.functions.{udf, array, lit ... ________________________________ From: anup ahire <[email protected]> Sent: Wednesday, March 15, 2017 2:04 AM To: [email protected] Subject: apply UDFs to N columns dynamically in dataframe Hello, I have a schema and name of columns to apply UDF to. Name of columns are user input and they can differ in numbers for each input. Is there a way to apply UDFs to N columns in dataframe ? Thanks !
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