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https://issues.apache.org/jira/browse/SPARK-22105?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-22105.
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Resolution: Incomplete
> Dataframe has poor performance when computing on many columns with codegen
> --------------------------------------------------------------------------
>
> Key: SPARK-22105
> URL: https://issues.apache.org/jira/browse/SPARK-22105
> Project: Spark
> Issue Type: Improvement
> Components: ML, SQL
> Affects Versions: 2.3.0
> Reporter: Weichen Xu
> Priority: Minor
> Labels: bulk-closed
>
> Suppose we have a dataframe with many columns (e.g 100 columns), each column
> is DoubleType.
> And we need to compute avg on each column. We will find using dataframe avg
> will be much slower than using RDD.aggregate.
> I observe this issue from this PR: (One pass imputer)
> https://github.com/apache/spark/pull/18902
> I also write a minimal testing code to reproduce this issue, I use computing
> sum to reproduce this issue:
> https://github.com/apache/spark/compare/master...WeichenXu123:aggr_test2?expand=1
> When we compute `sum` on 100 `DoubleType` columns, dataframe avg will be
> about 3x slower than `RDD.aggregate`, but if we only compute one column,
> dataframe avg will be much faster than `RDD.aggregate`.
> The reason of this issue, should be the defact in dataframe codegen. Codegen
> will inline everything and generate large code block. When the column number
> is large (e.g 100 columns), the codegen size will be too large, which cause
> jvm failed to JIT and fall back to byte code interpretation.
> This PR should address this issue:
> https://github.com/apache/spark/pull/19082
> But we need more performance test against some code in ML after above PR
> merged, to check whether this issue is actually fixed.
> This JIRA used to track this performance issue.
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