Github user chenghao-intel commented on the pull request:
https://github.com/apache/spark/pull/5154#issuecomment-85569221
Verified the code change by the following micro-benchmark
```scala
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.types._
case class Floor(child: Expression) extends UnaryExpression with Predicate {
override def foldable = child.foldable
def nullable = child.nullable
override def toString = s"Floor $child"
override def eval(input: Row): Any = {
child.eval(input) match {
case null => null
case ts: Int => ts - ts % 300
}
}
}
object T {
def benchmark(count: Int, expr: Expression): Unit = {
var i = 0
val row = new GenericRow(Array[Any](123, 21, 42))
val s = System.currentTimeMillis()
while (i < count) {
expr.eval(row)
i += 1
}
val e = System.currentTimeMillis()
println (s"${expr.getClass.getSimpleName} -- ${e - s} ms")
}
def main(args: Array[String]) {
def func(ts: Int) = ts - ts % 300
val udf0 = ScalaUdf(func _, IntegerType, BoundReference(0, IntegerType,
true) :: Nil)
val udf1 = Floor(BoundReference(0, IntegerType, true))
benchmark(1000000, udf0)
benchmark(1000000, udf0)
benchmark(1000000, udf0)
benchmark(1000000, udf1)
benchmark(1000000, udf1)
benchmark(1000000, udf1)
}
}
```
Without the code change it outputs
ScalaUdf -- 1183 ms
ScalaUdf -- 887 ms
ScalaUdf -- 929 ms
Floor -- 49 ms
Floor -- 15 ms
Floor -- 21 ms
With the code change, it outputs
ScalaUdf -- 382 ms
ScalaUdf -- 255 ms
ScalaUdf -- 247 ms
Floor -- 27 ms
Floor -- 6 ms
Floor -- 8 ms
Conclusions:
* The code change will improve the performance of scala udf by 2-3x
* Scala UDF is in very low performance compare to the built-in type of
Expression.
We probably need to provide more efficient way of UDF extension interface.
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