Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/16391#discussion_r93818360 --- 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 --- End diff -- the method signature in `Dataset` is: `def map[U : Encoder](f: T => U)`, unless we create primitive version methods, e.g. `def map(f: T => Long)`, I can't think of an easy way to get the concrete signature. BTW, I think the best solution is to analyze the byte code(class file) of the lambda function, and turn it into expressions.
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