Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/12067#discussion_r59648857
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala ---
@@ -117,30 +160,45 @@ object DatasetBenchmark {
val sparkContext = new SparkContext("local[*]", "Dataset benchmark")
val sqlContext = new SQLContext(sparkContext)
- val numRows = 10000000
+ val numRows = 100000000
val numChains = 10
val benchmark = backToBackMap(sqlContext, numRows, numChains)
val benchmark2 = backToBackFilter(sqlContext, numRows, numChains)
+ val benchmark3 = aggregate(sqlContext, numRows)
/*
Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.11.4
Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz
back-to-back map: Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
-------------------------------------------------------------------------------------------
- Dataset 902 / 995 11.1
90.2 1.0X
- DataFrame 132 / 167 75.5
13.2 6.8X
- RDD 216 / 237 46.3
21.6 4.2X
+ RDD 1935 / 2105 51.7
19.3 1.0X
+ DataFrame 756 / 799 132.3
7.6 2.6X
+ Dataset 7359 / 7506 13.6
73.6 0.3X
--- End diff --
oh wait, this line is for `back-to-back map`, but this PR aims to improve
the aggregator case.
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