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From "spencerlee (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-13140) spark sql aggregate performance decrease
Date Tue, 02 Feb 2016 09:00:48 GMT

     [ https://issues.apache.org/jira/browse/SPARK-13140?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

spencerlee updated SPARK-13140:
-------------------------------
    Remaining Estimate: 10h  (was: 168h)
     Original Estimate: 10h  (was: 168h)

> spark sql  aggregate performance decrease  
> -------------------------------------------
>
>                 Key: SPARK-13140
>                 URL: https://issues.apache.org/jira/browse/SPARK-13140
>             Project: Spark
>          Issue Type: Question
>    Affects Versions: 1.6.0
>            Reporter: spencerlee
>   Original Estimate: 10h
>  Remaining Estimate: 10h
>
> In our scenario, their are 30 + key columns with 60+ metric columns.
> our typical query is: select key1, key2, key3, key4, key5, sum(metric1), sum(metric2),
sum(metric3).... sum(metric30) from table_name group by key1, key2, key3, key4, key5.
> I import a single parquet file(60M, about 250w+ records) into sparksql , and do the typical
query with local mode.  I found that, when I only aggregate 24 metrics, the response time
is about 4.81s, when I aggregate 25+ metrics, the response time is 45.9s, which is almost
10 times slower. that's obviously unreasonable. 
> Is this a bug or need modify some configuration to tune the query?    



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