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https://issues.apache.org/jira/browse/DRILL-6678?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16580555#comment-16580555
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Sorabh Hamirwasia edited comment on DRILL-6678 at 8/14/18 11:46 PM:
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The idea is the 3rd proposal using Batch Sizing. Updated the description as well
{quote}Or, is the idea to append rows from multiple incoming batches until the 
output batch reaches the target size? That is, if A, B and C are incoming 
batches, the output batch may have all selected rows from A and B, and, say, 
have the selected rows from C.
{quote}


was (Author: shamirwasia):
The idea is the 3rd proposal using Batch Sizing.
{quote}Or, is the idea to append rows from multiple incoming batches until the 
output batch reaches the target size? That is, if A, B and C are incoming 
batches, the output batch may have all selected rows from A and B, and, say, 
have the selected rows from C.
{quote}

> Improve SelectionVectorRemover to pack output batch based on BatchSizing
> ------------------------------------------------------------------------
>
>                 Key: DRILL-6678
>                 URL: https://issues.apache.org/jira/browse/DRILL-6678
>             Project: Apache Drill
>          Issue Type: Improvement
>          Components: Execution - Relational Operators
>    Affects Versions: 1.14.0
>            Reporter: Sorabh Hamirwasia
>            Assignee: Sorabh Hamirwasia
>            Priority: Major
>
> SelectionVectorRemover in most of the cases is downstream to Filter which 
> reduces the number of records to be copied in output container. In those 
> cases if SelectionVectorRemover can pack the output batch to approximate 
> maximum utilization based on RecordBatchSizer target record count, that will 
> reduce the number of output batches from it and will help to improve 
> performance. During Lateral & Unnest Performance evaluation we have noticed a 
> significant decrease in performance as number of batches increases for same 
> number of rows (i.e. Batch is not fully packed)



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