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Posted to issues@spark.apache.org by "Sameer Agarwal (JIRA)" <ji...@apache.org> on 2018/01/08 20:14:00 UTC
[jira] [Updated] (SPARK-20184) performance regression for
complex/long sql when enable whole stage codegen
[ https://issues.apache.org/jira/browse/SPARK-20184?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sameer Agarwal updated SPARK-20184:
-----------------------------------
Target Version/s: 2.4.0 (was: 2.3.0)
> 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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