Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/16391#discussion_r93789313
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala ---
@@ -170,36 +176,39 @@ object DatasetBenchmark {
val benchmark3 = aggregate(spark, numRows)
/*
- OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux
3.10.0-327.18.2.el7.x86_64
- Intel Xeon E3-12xx v2 (Ivy Bridge)
+ Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.12.1
+ Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz
+
back-to-back map: Best/Avg Time(ms)
Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
- RDD 3448 / 3646 29.0
34.5 1.0X
- DataFrame 2647 / 3116 37.8
26.5 1.3X
- Dataset 4781 / 5155 20.9
47.8 0.7X
+ RDD 3963 / 3976 25.2
39.6 1.0X
+ DataFrame 826 / 834 121.1
8.3 4.8X
+ Dataset 5178 / 5198 19.3
51.8 0.8X
*/
benchmark.run()
/*
- OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux
3.10.0-327.18.2.el7.x86_64
- Intel Xeon E3-12xx v2 (Ivy Bridge)
+ Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.12.1
+ Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz
+
back-to-back filter: Best/Avg Time(ms)
Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
- RDD 1346 / 1618 74.3
13.5 1.0X
- DataFrame 59 / 72 1695.4
0.6 22.8X
- Dataset 2777 / 2805 36.0
27.8 0.5X
+ RDD 533 / 587 187.6
5.3 1.0X
+ DataFrame 79 / 91 1269.0
0.8 6.8X
+ Dataset 550 / 559 181.7
5.5 1.0X
--- End diff --
For "back-to-back filter", `Dataset` will deserialize the input row to an
object and apply the condition function. When the deserialization becomes
no-op, `Dataset` runs almost the same RDD code like the `RDD` case. So in this
case, `RDD` and `Dataset` has similar performance.
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