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https://issues.apache.org/jira/browse/FLINK-5220?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15725755#comment-15725755
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ASF GitHub Bot commented on FLINK-5220:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/2923#discussion_r91081832
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/api/table/plan/nodes/dataset/BatchScan.scala
---
@@ -45,7 +45,8 @@ abstract class BatchScan(
override def computeSelfCost (planner: RelOptPlanner, metadata:
RelMetadataQuery): RelOptCost = {
val rowCnt = metadata.getRowCount(this)
- planner.getCostFactory.makeCost(rowCnt, rowCnt, 0)
+ val columnCnt = getRowType.getFieldCount
--- End diff --
The first cost parameter is number of processed rows, second parameter
indicates CPU cost, and third IO cost. I would not change the number of
processed rows but add IO costs of `rowCnt * rowSize` where `rowSize` can be
computed by `DataSetRel.estimateRowSize()`.
Have a look at `DataSetCost` to check how costs are compared. The cost
model aims at minimizing IO costs. So making here a difference results in the
choice of the plan with the most projection.
> Flink SQL projection pushdown
> -----------------------------
>
> Key: FLINK-5220
> URL: https://issues.apache.org/jira/browse/FLINK-5220
> Project: Flink
> Issue Type: Improvement
> Components: Table API & SQL
> Reporter: zhangjing
> Assignee: zhangjing
>
> The jira is to do projection pushdown optimization. Please go forward to the
> the design document for more details.
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