Explode multiple columns in Spark SQL table

Question

There was a question regarding this issue here:

Explode (transpose?) multiple columns in Spark SQL table

Suppose that we have extra columns as below:

Answer 1

If you want to extend the UDF for more columns, do as below:

val zip = udf((xs: Seq[String], ys: Seq[String], zs: Seq[String]) =>
  for (((xs,ys),zs) <- xs zip ys zip zs) yield (xs,ys,zs))

df.withColumn("vars", explode(zip($"varA", $"varB", $"varC"))).select(
  $"userId", $"someString", $"vars._1".alias("varA"),
  $"vars._2".alias("varB"),$"vars._3".alias("varC")).show

This logic can be applied for n columns as required.