Rewrite LogicalPlan to push down udf from aggregate

Question

I have defined an UDF which increases the input value by one, named \"inc\", this is the code of my udf

spark.udf.r         


        

Answer 1

OK, finally I find way to so answer this question.

Though ScalaUDF can't cast to NamedExpression, but Alias could.

So, I create Alias from ScalaUDF, then construct Project.

import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, ExpectsInputTypes, ExprId, Expression, NamedExpression, ScalaUDF}
import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, LocalRelation, LogicalPlan, Project, Subquery}
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.types.{AbstractDataType, DataType}

import scala.collection.mutable

object RewritePlanTest {

  case class UdfRule(spark: SparkSession) extends Rule[LogicalPlan] {

    def collectUDFs(e: Expression): Seq[Expression] = e match {
      case udf: ScalaUDF => Seq(udf)
      case _ => e.children.flatMap(collectUDFs)
    }

    override def apply(plan: LogicalPlan): LogicalPlan = plan match {
      case agg@Aggregate(g, a, c) if g.isEmpty && a.length == 1 => {
        val udfs = agg.expressions.flatMap(collectUDFs)
        if (udfs.isEmpty) {
          agg
        } else {
          val alias_udf = for (i <- 0 until udfs.size) yield Alias(udfs(i), s"udf${i}")()
          val alias_set = mutable.HashMap[Expression, Attribute]()
          val proj = Project(alias_udf, c)
          alias_set ++= udfs.zip(proj.output)
          val new_agg = agg.withNewChildren(Seq(proj)).transformExpressionsUp {
            case udf: ScalaUDF if alias_set.contains(udf) => alias_set(udf)
          }
          println("====== new agg ======")
          println(new_agg)
          new_agg
        }
      }
      case _ => plan
    }
  }


  def main(args: Array[String]): Unit = {
    Logger.getLogger("org").setLevel(Level.WARN)

    val spark = SparkSession
      .builder()
      .master("local[*]")
      .appName("Rewrite plan test")
      .withExtensions(e => e.injectOptimizerRule(UdfRule))
      .getOrCreate()

    val input = Seq(100L, 200L, 300L)
    import spark.implicits._
    input.toDF("vals").createOrReplaceTempView("data")

    spark.udf.register("inc", (x: Long) => x + 1)

    val df = spark.sql("select sum(inc(vals)) from data where vals > 100")
    //    val plan = df.queryExecution.analyzed
    //    println(plan)
    df.explain(true)
    df.show()

    spark.stop()

  }
}

This code output the LogicalPlan that I wanted.

====== new agg ======
Aggregate [sum(udf0#9L) AS sum(inc(vals))#7L]
+- Project [inc(vals#4L) AS udf0#9L]
   +- LocalRelation [vals#4L]