Just note that 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}
val myConcatFunc = (xs: Seq[Any], sep: String) =>
xs.filter(_ != null).mkString(sep)
val myConcat = udf(myConcatFunc)
An example usage:
val df = sc.parallelize(Seq(
(null, "a", "b", "c"), ("d", null, null, "e")
)).toDF("x1", "x2", "x3", "x4")
val cols = array($"x1", $"x2", $"x3", $"x4")
val sep = lit("-")
df.select(myConcat(cols, sep).alias("concatenated")).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
With raw SQL:
df.registerTempTable("df")
sqlContext.udf.register("myConcat", myConcatFunc)
sqlContext.sql(
"SELECT myConcat(array(x1, x2, x4), '.') AS concatenated FROM df"
).show
// +------------+
// |concatenated|
// +------------+
// | a.c|
// | d.e|
// +------------+
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