Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/11788#discussion_r56548669
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/BenchmarkWholeStageCodegen.scala
---
@@ -247,7 +247,26 @@ class BenchmarkWholeStageCodegen extends SparkFunSuite
{
*/
}
- ignore("rube") {
+ ignore("shuffle hash join") {
+ val N = 4 << 20
+ sqlContext.setConf("spark.sql.shuffle.partitions", "2")
+ sqlContext.setConf("spark.sql.autoBroadcastJoinThreshold", "10000000")
+ runBenchmark("shuffle hash join", N) {
+ val df1 = sqlContext.range(N).selectExpr(s"id as k1")
+ val df2 = sqlContext.range(N / 5).selectExpr(s"id * 3 as k2")
+ df1.join(df2, col("k1") === col("k2")).count()
+ }
+
+ /**
+ Intel(R) Core(TM) i7-4558U CPU @ 2.80GHz
+ shuffle hash join: Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-------------------------------------------------------------------------------------------
+ shuffle hash join codegen=false 1168 / 1902 3.6
278.6 1.0X
+ shuffle hash join codegen=true 850 / 1196 4.9
202.8 1.4X
--- End diff --
SMJ will take about 450ns per row.
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