[GLUTEN-3951][CH]Bug fix floor diff #3956
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taiyang-li
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Dec 25, 2023
| @@ -180,6 +179,7 @@ static const std::map<std::string, std::string> SCALAR_FUNCTIONS | |||
| {"add_months", "addMonths"}, | |||
| {"date_trunc", "dateTrunc"}, | |||
| {"floor_datetime", "dateTrunc"}, | |||
| {"floor", "spark_floor"}, | |||
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What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
(Fixes: #3951)
How was this patch tested?
TEST BY UT
端到端性能测试
数据类型为Int64，表结构 test_tbl(d Int64), 测试SQL:
select count(1) from test_tbl where floor(d) > 1数据总量3000W， 测试三次PR 改动前：1.13s, 0.92s, 0.985s
PR 改动后： 1.064s， 1.077s, 0.984s
数据类型为Float64， 表结构为test_tbl(d float64) , 测试SQL
select count(1) from test_tbl where floor(d) > 1数据总量3000W， 测试三次PR 改动前： 1.417s， 1.386s 1.426s
PR 改动后：1.568s， 1.476s， 1.508s
可见对于Int64类型来说，改动前后性能基本持平；对于float64类型来说，大约有7.6%的性能回退，主要是来自于针对数据中可能出现NaN 以及INF 的情况进行了判断和赋值。
benchmark 性能测试
使用开发的
benchmark_spark_floor_function.cpp来测试Int64类型测试
对于CH 的Floor函数, 结果如下
对于新开发的Floor函数，结果如下
Float64类型测试
对于CH的Floor函数，结果如下
对于新开发的Floor函数，结果如下
可见对于Int64，大概有 3%左右的回退，对于Float64类型 大概有70%左右的回退
Spark UT 关于Floor 函数的测试，会通过
org.apache.spark.sql.GlutenMathFunctionsSuite这个测试来完成，已开启