Valentin Nikotin created SPARK-23791:
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Summary: Sub-optimal generated code when aggregating nullable
columns
Key: SPARK-23791
URL: https://issues.apache.org/jira/browse/SPARK-23791
Project: Spark
Issue Type: Bug
Components: Optimizer
Affects Versions: 2.3.0, 2.2.0
Environment: {code:java}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.{Column, DataFrame, SparkSession}
import org.apache.spark.sql.internal.SQLConf.WHOLESTAGE_CODEGEN_ENABLED
object TestCase {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.master("local[4]")
.appName("test")
.getOrCreate()
def addConstColumns(prefix: String, cnt: Int, value: Column)(inputDF:
DataFrame) =
(0 until cnt).foldLeft(inputDF)((df, idx) =>
df.withColumn(s"$prefix$idx", value))
val dummy = udf(() => Option.empty[Int])
def test(cnt: Int = 50, rows: Int = 5000000, grps: Int = 1000): Double = {
val t0 = System.nanoTime()
spark.range(rows).toDF()
.withColumn("grp", col("id").mod(grps))
.transform(addConstColumns("null_", cnt, dummy()))
.groupBy("grp")
.agg(sum("null_0"), (1 until cnt).map(idx => sum(s"null_$idx")): _*)
.collect()
val t1 = System.nanoTime()
(t1 - t0) / 1e9
}
val timings = for (i <- 1 to 3) yield {
spark.sessionState.conf.setConf(WHOLESTAGE_CODEGEN_ENABLED, true)
val with_wholestage = test()
spark.sessionState.conf.setConf(WHOLESTAGE_CODEGEN_ENABLED, false)
val without_wholestage = test()
(with_wholestage, without_wholestage)
}
timings.foreach(println)
println("Press enter ...")
System.in.read()
}
}
{code}
Reporter: Valentin Nikotin
It appears to be that with wholeStage codegen enabled simple spark job
performing sum aggregation of 50 nullable columns runs ~4 timer slower than
without wholeStage codegen.
Please check test case code. Please note that udf is only to prevent
elimination optimizations that could be applied to literals.
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