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https://issues.apache.org/jira/browse/SPARK-20184?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Fei Wang updated SPARK-20184:
-----------------------------
Description:
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 codege 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).
was:
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.
codegen on: 40s
codegen off: 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).
> performance regression for complex/long sql when enable 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 codege 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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