Github user chenghao-intel commented on a diff in the pull request:
https://github.com/apache/spark/pull/7334#discussion_r34434828
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala ---
@@ -332,8 +332,12 @@ private[sql] abstract class SparkStrategies extends
QueryPlanner[SparkPlan] {
execution.Sample(lb, ub, withReplacement, seed, planLater(child))
:: Nil
case logical.LocalRelation(output, data) =>
LocalTableScan(output, data) :: Nil
- case logical.Limit(IntegerLiteral(limit), child) =>
- execution.Limit(limit, planLater(child)) :: Nil
+ case logical.Limit(IntegerLiteral(limit), child) => {
+ val perPartitionLimit = execution.PartitionLocalLimit(limit,
planLater(child))
+ val globalLimit = execution.PartitionLocalLimit(
+ limit, execution.Exchange(SinglePartition, Nil,
perPartitionLimit))
--- End diff --
Instead of manually specify a single `SinglePartition` in
`SparkStrategies`, a better idea probably specifying the `requiredDistribution`
in the physical operator `execution.PartitionLocalLimit`, as we did in
`execution.Aggregate`, the rational we do that is, in the case of the
`perPartitionLimit` is a `SinglePartition` already, we don't need to do anther
shuffle, the `EnsureRequirements` can handle such kind of optimization for us.
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