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https://issues.apache.org/jira/browse/SPARK-20184?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15965597#comment-15965597
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Fei Wang commented on SPARK-20184:
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try this :
1. create table
[code]
val df = (1 to 500000).map(x => (x.toString, x.toString, x, x, x, x, x, x, x, 
x, x, x, x, x, x, x, x, x, x, x, x, x)).toDF("dim_1", "dim_2", "c1", "c2", 
"c3", "c4", "c5", "c6", "c7", "c8", "c9", "c10","c11", "c12", "c13", "c14", 
"c15", "c16", "c17", "c18", "c19", "c20")
df.write.saveAsTable("sum_table_50w_3")

df.write.format("csv").saveAsTable("sum_table_50w_1")

[code]

2. query the table

select dim_1, dim_2, sum(c1), sum(c2), sum(c3), sum(c4), sum(c5), sum(c6), 
sum(c7), sum(c8), sum(c9), sum(c10), sum(c11), sum(c12), sum(c13), sum(c14), 
sum(c15), sum(c16), sum(c17), sum(c18), sum(c19), sum(c20) from sum_table_50w_3 
group by dim_1, dim_2 limit 100

> performance regression for complex/long sql when enable whole stage codegen
> ---------------------------------------------------------------------------
>
>                 Key: SPARK-20184
>                 URL: https://issues.apache.org/jira/browse/SPARK-20184
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 1.6.0, 2.1.0
>            Reporter: Fei Wang
>
> The performance of following SQL get much worse in spark 2.x  in contrast 
> with codegen off.
>     SELECT
>        sum(COUNTER_57) 
>         ,sum(COUNTER_71) 
>         ,sum(COUNTER_3)  
>         ,sum(COUNTER_70) 
>         ,sum(COUNTER_66) 
>         ,sum(COUNTER_75) 
>         ,sum(COUNTER_69) 
>         ,sum(COUNTER_55) 
>         ,sum(COUNTER_63) 
>         ,sum(COUNTER_68) 
>         ,sum(COUNTER_56) 
>         ,sum(COUNTER_37) 
>         ,sum(COUNTER_51) 
>         ,sum(COUNTER_42) 
>         ,sum(COUNTER_43) 
>         ,sum(COUNTER_1)  
>         ,sum(COUNTER_76) 
>         ,sum(COUNTER_54) 
>         ,sum(COUNTER_44) 
>         ,sum(COUNTER_46) 
>         ,DIM_1 
>         ,DIM_2 
>               ,DIM_3
>     FROM aggtable group by DIM_1, DIM_2, DIM_3 limit 100;
> Num of rows of aggtable is about 35000000.
> whole stage codegen on(spark.sql.codegen.wholeStage = true):    40s
> whole stage codegen  off(spark.sql.codegen.wholeStage = false):    6s
> After some analysis i think this is related to the huge java method(a java 
> method of thousand lines) which generated by codegen.
> And If i config -XX:-DontCompileHugeMethods the performance get much 
> better(about 7s).



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