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Dongjoon Hyun commented on SPARK-20184:
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If this exists up to 2.4.0, could you update the `Affects
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Kazuaki Ishizaki commented on SPARK-20184:
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In {{branch-2.4}}, we still see the performance
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Kazuaki Ishizaki commented on SPARK-20184:
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Although I created another JIRA
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Wenchen Fan commented on SPARK-20184:
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[~kiszk] did you create another JIRA to replace this one?
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Kazuaki Ishizaki commented on SPARK-20184:
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When # of the aggregated columns gets large, I saw
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Kazuaki Ishizaki commented on SPARK-20184:
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The root cause is overhead in Java code generated by
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Takeshi Yamamuro commented on SPARK-20184:
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When #aggregated columns gets large, it seems we get
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Kazuaki Ishizaki commented on SPARK-20184:
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I succeeded to reproduce this...
{code}
% git log |
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Tejas Patil commented on SPARK-20184:
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Out of curiosity, I tried out a query with ~20 columns
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Fei Wang commented on SPARK-20184:
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Also use the master branch to test my test case:
1. Java version
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Herman van Hovell commented on SPARK-20184:
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I just tried your example using the master branch,
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Fei Wang commented on SPARK-20184:
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try this :
1. create table
[code]
val df = (1 to 50).map(x =>
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Fei Wang commented on SPARK-20184:
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Tested with a smaller table 100,000 rows.
Codegen on: 2.6s
Codegen
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Kazuaki Ishizaki commented on SPARK-20184:
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If the number of rows are smaller, how about the
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