Hi,
I want to write a UDF/UDAF which provides native processing performance.
Currently, when creating a UDF/UDAF in a normal manner the performance is hit
because it breaks optimizations.
For a simple example I wanted to create a UDF which tests whether the value is
smaller than 10.
I tried something like this :
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext,
ExprCode}
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.util.TypeUtils
import org.apache.spark.sql.types._
import org.apache.spark.util.Utils
import org.apache.spark.sql.catalyst.expressions._
case class genf(child: Expression) extends UnaryExpression with Predicate with
ImplicitCastInputTypes {
override def inputTypes: Seq[AbstractDataType] = Seq(IntegerType)
override def toString: String = s"$child < 10"
override def eval(input: InternalRow): Any = {
val value = child.eval(input)
if (value == null)
{
false
} else {
child.dataType match {
case IntegerType => value.asInstanceOf[Int] < 10
}
}
}
override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
defineCodeGen(ctx, ev, c => s"($c) < 10")
}
}
However, this doesn't work as some of the underlying classes/traits are private
(e.g. AbstractDataType is private) making it problematic to create a new case
class.
Is there a way to do it? The idea is to provide a couple of jars with a bunch
of functions our team needs.
Thanks,
Assaf.
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