[SPARK-23833] Incorrect primitive type check for input arguments of udf

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Affects Version/s: 2.2.0, 2.3.0
• Component/s: Optimizer, SQL
• Labels: bulk-closed
• Assignee: Unassigned
• Reporter: Valentin Nikotin
• Created: 30/Mar/18 16:30
• Updated: 17/May/20 17:58
• Resolved: 08/Oct/19 05:43

Description

There is claimed behavior for scala UDFs with primitive type arguments:

Note that if you use primitive parameters, you are not able to check if it is null or not, and the UDF will return null for you if the primitive input is null.

This is initial issue - SPARK-11725
Correspondent pr - PR

The problem is that ScalaReflection.getParameterTypes doesn't work correctly due to type erasure.
The correct check "if type is primitive" should be based on typeTag something like this:

typeTag[T].tpe.typeSymbol.asClass.isPrimitive

 

The problem appears if we have high order functions:

val f = (x: Long) => x
def identity[T, U](f: T => U): T => U = (t: T) => f(t)
val udf0 = udf(f)
val udf1 = udf(identity(f))
val getNull = udf(() => null.asInstanceOf[java.lang.Long])
spark.range(5).toDF().
  withColumn("udf0", udf0(getNull())).
  withColumn("udf1", udf1(getNull())).
  show()
spark.range(5).toDF().
  withColumn("udf0", udf0(getNull())).
  withColumn("udf1", udf1(getNull())).
  explain()

Test execution on Spark 2.2 spark-shell:

scala> val f = (x: Long) => x
f: Long => Long = <function1>

scala> def identity[T, U](f: T => U): T => U = (t: T) => f(t)
identity: [T, U](f: T => U)T => U

scala> val udf0 = udf(f)
udf0: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,LongType,Some(List(LongType)))

scala> val udf1 = udf(identity(f))
udf1: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,LongType,Some(List(LongType)))

scala> val getNull = udf(() => null.asInstanceOf[java.lang.Long])
getNull: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function0>,LongType,Some(List()))

scala> spark.range(5).toDF().
     |   withColumn("udf0", udf0(getNull())).
     |   withColumn("udf1", udf1(getNull())).
     |   show()
+---+----+----+                                                                 
| id|udf0|udf1|
+---+----+----+
|  0|null|   0|
|  1|null|   0|
|  2|null|   0|
|  3|null|   0|
|  4|null|   0|
+---+----+----+


scala> spark.range(5).toDF().
     |   withColumn("udf0", udf0(getNull())).
     |   withColumn("udf1", udf1(getNull())).
     |   explain()
== Physical Plan ==
*Project [id#19L, if (isnull(UDF())) null else UDF(UDF()) AS udf0#24L, UDF(UDF()) AS udf1#28L]
+- *Range (0, 5, step=1, splits=6)

 

The typeTag information about input parameters is available in udf function but only used to get schema, it should be added to ScalaUDF too so that we can used it later:

def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag](f: Function2[A1, A2, RT]): UserDefinedFunction = {
  val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: Nil).toOption
  UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
}

 

Here is current vs desired version:

scala> import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.catalyst.ScalaReflection

scala> ScalaReflection.getParameterTypes(identity(f))
res2: Seq[Class[_]] = WrappedArray(class java.lang.Object)

scala> ScalaReflection.getParameterTypes(identity(f)).map(_.isPrimitive)
res7: Seq[Boolean] = ArrayBuffer(false)

versus

scala> import scala.reflect.runtime.universe.{typeTag, TypeTag}
import scala.reflect.runtime.universe.{typeTag, TypeTag}

scala> def myGetParameterTypes[T : TypeTag, U](func: T => U) = {
     |   typeTag[T].tpe.typeSymbol.asClass
     | }
myGetParameterTypes: [T, U](func: T => U)(implicit evidence$1: reflect.runtime.universe.TypeTag[T])reflect.runtime.universe.ClassSymbol

scala> myGetParameterTypes(f)
res3: reflect.runtime.universe.ClassSymbol = class Long

scala> myGetParameterTypes(f).isPrimitive
res4: Boolean = true

Although for this case there is workaround with using @specialized(Long)

scala> def identity2[@specialized(Long) T, U](f: T => U): T => U = (t: T) => f(t)
identity2: [T, U](f: T => U)T => U

scala> ScalaReflection.getParameterTypes(identity2(f))
res10: Seq[Class[_]] = WrappedArray(long)