Adding two columns to existing DataFrame using wit

I have a DataFrame with a few columns. Now I want to add two more columns to the existing DataFrame.

Currently I am doing this using withColumn method in DataFrame.

for example:

df.withColumn("newColumn1", udf(col("somecolumn")))
  .withColumn("newColumn2", udf(col("somecolumn")))

Actually I can return both newcoOlumn values in single UDF method using Array[String]. But currently this is how I am doing it.

Is there anyway, I can do this effectively? using explode is the good option here?

Even if I have to use explode, I have to use withColumn once, then return the column value as Array[String], then using explode, create two more columns.

Which one is effective? or is there any alternatives?

AFAIk you need to call withColumn twice (once for each new column). But if your udf is computationally expensive, you can avoid to call it twice with storing the "complex" result in a temporary column and then "unpacking" the result e.g. using the apply method of column (which gives access to the array element). Note that sometimes it's necessary to cache the intermediate result (to prevent that the UDF is called twice per row during unpacking), sometimes it's not needed. This seems to depend on how spark the optimizes the plan :

val myUDf = udf((s:String) => Array(s.toUpperCase(),s.toLowerCase()))

val df = sc.parallelize(Seq("Peter","John")).toDF("name")

val newDf = df
  .withColumn("udfResult",myUDf(col("name"))).cache 
  .withColumn("uppercaseColumn", col("udfResult")(0))
  .withColumn("lowercaseColumn", col("udfResult")(1))
  .drop("udfResult")

newDf.show()

gives

+-----+---------------+---------------+
| name|uppercaseColumn|lowercaseColumn|
+-----+---------------+---------------+
|Peter|          PETER|          peter|
| John|           JOHN|           john|
+-----+---------------+---------------+

With an UDF returning a tuple, the unpacking would look like this:

val newDf = df
    .withColumn("udfResult",myUDf(col("name"))).cache
    .withColumn("lowercaseColumn", col("udfResult._1"))
    .withColumn("uppercaseColumn", col("udfResult._2"))
    .drop("udfResult")