[jira] [Assigned] (ARROW-7394) [C++][DataFrame] Implement zero-copy optimizations when performing Filter

["Todd Farmer (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-7394?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Todd Farmer reassigned ARROW-7394:
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    Assignee:     (was: Eduardo Ponce)

This issue was last updated over 90 days ago, which may be an indication it is no longer being actively worked. To better reflect the current state, the issue is being unassigned. Please feel free to re-take assignment of the issue if it is being actively worked, or if you plan to start that work soon.

> [C++][DataFrame] Implement zero-copy optimizations when performing Filter
> -------------------------------------------------------------------------
>
>                 Key: ARROW-7394
>                 URL: https://issues.apache.org/jira/browse/ARROW-7394
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: C++
>            Reporter: Wes McKinney
>            Priority: Major
>              Labels: dataframe
>
> For high-selectivity filters (most elements included), it may be wasteful and slow to copy large contiguous ranges of array chunks into the resulting ChunkedArray. Instead, we can scan the filter boolean array and slice off chunks of the source data rather than copying. 
> We will need to empirically determine how large the contiguous range needs to be in order to merit the slice-based approach versus simple/native materialization. For example, in a filter array like
> 1 0 1 0 1 0 1 0 1
> it would not make sense to slice 5 times because slicing carries some overhead. But if we had
> 1 ... 1 [100 1's] 0 1 ... 1 [100 1's] 0 1 ... 1 [100 1's] 0 1 ... 1 [100 1's] 
> then performing 4 slices may be faster than doing a copy materialization. 



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