[jira] [Updated] (SPARK-20184) performance regression for complex/long sql when enable whole stage codegen

2018-12-06 Thread Dongjoon Hyun (JIRA) 


 [ 
https://issues.apache.org/jira/browse/SPARK-20184?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Dongjoon Hyun updated SPARK-20184:
--
Target Version/s:   (was: 2.5.0)

> 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
>Priority: Major
>
> 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 3500.
> 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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[jira] [Updated] (SPARK-20184) performance regression for complex/long sql when enable whole stage codegen

2018-09-20 Thread Wenchen Fan (JIRA) 


 [ 
https://issues.apache.org/jira/browse/SPARK-20184?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Wenchen Fan updated SPARK-20184:

Target Version/s: 2.5.0  (was: 2.4.0)

> 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
>Priority: Major
>
> 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 3500.
> 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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[jira] [Updated] (SPARK-20184) performance regression for complex/long sql when enable whole stage codegen

2018-01-08 Thread Sameer Agarwal (JIRA) 

 [ 
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 3500.
> 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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[jira] [Updated] (SPARK-20184) performance regression for complex/long sql when enable whole stage codegen

2017-04-04 Thread Fei Wang (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-20184?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Fei Wang updated SPARK-20184:
-
Summary: performance regression for complex/long sql when enable whole 
stage codegen  (was: performance regression for complex/long sql when enable 
codegen)

> 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 3500.
> 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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