Creating a UDF/UDAF using code generation

[AssafMendelson <as...@rsa.com>]

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.
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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