SparkSQL: How to deal with null values in user defined function? - scala

Question

Given Table 1 with one column "x" of type String.
I want to create Table 2 with a column "y" that is an integer representation of the date strings given in "x".
Essential is to keep null values in column "y".
Table 1 (Dataframe df1):
+----------+
| x|
+----------+
|2015-09-12|
|2015-09-13|
| null|
| null|
+----------+
root
|-- x: string (nullable = true)
Table 2 (Dataframe df2):
+----------+--------+
| x| y|
+----------+--------+
| null| null|
| null| null|
|2015-09-12|20150912|
|2015-09-13|20150913|
+----------+--------+
root
|-- x: string (nullable = true)
|-- y: integer (nullable = true)
While the user-defined function (udf) to convert values from column "x" into those of column "y" is:
val extractDateAsInt = udf[Int, String] (
(d:String) => d.substring(0, 10)
.filterNot( "-".toSet)
.toInt )
and works, dealing with null values is not possible.
Even though, I can do something like
val extractDateAsIntWithNull = udf[Int, String] (
(d:String) =>
if (d != null) d.substring(0, 10).filterNot( "-".toSet).toInt
else 1 )
I have found no way, to "produce" null values via udfs (of course, as Ints can not be null).
My current solution for creation of df2 (Table 2) is as follows:
// holds data of table 1
val df1 = ...
// filter entries from df1, that are not null
val dfNotNulls = df1.filter(df1("x")
.isNotNull)
.withColumn("y", extractDateAsInt(df1("x")))
.withColumnRenamed("x", "right_x")
// create df2 via a left join on df1 and dfNotNull having
val df2 = df1.join( dfNotNulls, df1("x") === dfNotNulls("right_x"), "leftouter" ).drop("right_x")
Questions:
The current solution seems cumbersome (and probably not efficient wrt. performance). Is there a better way?
#Spark-developers: Is there a type NullableInt planned / avaiable, such that the following udf is possible (see Code excerpt ) ?
Code excerpt
val extractDateAsNullableInt = udf[NullableInt, String] (
(d:String) =>
if (d != null) d.substring(0, 10).filterNot( "-".toSet).toInt
else null )

Answer 1

This is where Optioncomes in handy:
val extractDateAsOptionInt = udf((d: String) => d match {
case null => None
case s => Some(s.substring(0, 10).filterNot("-".toSet).toInt)
})
or to make it slightly more secure in general case:
import scala.util.Try
val extractDateAsOptionInt = udf((d: String) => Try(
d.substring(0, 10).filterNot("-".toSet).toInt
).toOption)
All credit goes to Dmitriy Selivanov who've pointed out this solution as a (missing?) edit here.
Alternative is to handle null outside the UDF:
import org.apache.spark.sql.functions.{lit, when}
import org.apache.spark.sql.types.IntegerType
val extractDateAsInt = udf(
(d: String) => d.substring(0, 10).filterNot("-".toSet).toInt
)
df.withColumn("y",
when($"x".isNull, lit(null))
.otherwise(extractDateAsInt($"x"))
.cast(IntegerType)
)

Answer 2

Scala actually has a nice factory function, Option(), that can make this even more concise:
val extractDateAsOptionInt = udf((d: String) =>
Option(d).map(_.substring(0, 10).filterNot("-".toSet).toInt))
Internally the Option object's apply method is just doing the null check for you:
def apply[A](x: A): Option[A] = if (x == null) None else Some(x)

Answer 3

Supplementary code
With the nice answer of #zero323, I created the following code, to have user defined functions available that handle null values as described. Hope, it is helpful for others!
/**
* Set of methods to construct [[org.apache.spark.sql.UserDefinedFunction]]s that
* handle `null` values.
*/
object NullableFunctions {
import org.apache.spark.sql.functions._
import scala.reflect.runtime.universe.{TypeTag}
import org.apache.spark.sql.UserDefinedFunction
/**
* Given a function A1 => RT, create a [[org.apache.spark.sql.UserDefinedFunction]] such that
* * if fnc input is null, None is returned. This will create a null value in the output Spark column.
* * if A1 is non null, Some( f(input) will be returned, thus creating f(input) as value in the output column.
* #param f function from A1 => RT
* #tparam RT return type
* #tparam A1 input parameter type
* #return a [[org.apache.spark.sql.UserDefinedFunction]] with the behaviour describe above
*/
def nullableUdf[RT: TypeTag, A1: TypeTag](f: Function1[A1, RT]): UserDefinedFunction = {
udf[Option[RT],A1]( (i: A1) => i match {
case null => None
case s => Some(f(i))
})
}
/**
* Given a function A1, A2 => RT, create a [[org.apache.spark.sql.UserDefinedFunction]] such that
* * if on of the function input parameters is null, None is returned.
* This will create a null value in the output Spark column.
* * if both input parameters are non null, Some( f(input) will be returned, thus creating f(input1, input2)
* as value in the output column.
* #param f function from A1 => RT
* #tparam RT return type
* #tparam A1 input parameter type
* #tparam A2 input parameter type
* #return a [[org.apache.spark.sql.UserDefinedFunction]] with the behaviour describe above
*/
def nullableUdf[RT: TypeTag, A1: TypeTag, A2: TypeTag](f: Function2[A1, A2, RT]): UserDefinedFunction = {
udf[Option[RT], A1, A2]( (i1: A1, i2: A2) => (i1, i2) match {
case (null, _) => None
case (_, null) => None
case (s1, s2) => Some((f(s1,s2)))
} )
}
}