Github user rdblue commented on a diff in the pull request:
https://github.com/apache/spark/pull/21503#discussion_r194841328
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Strategy.scala
---
@@ -17,15 +17,56 @@
package org.apache.spark.sql.execution.datasources.v2
-import org.apache.spark.sql.Strategy
+import org.apache.spark.sql.{execution, Strategy}
+import org.apache.spark.sql.catalyst.expressions.{And, AttributeReference,
AttributeSet}
+import org.apache.spark.sql.catalyst.planning.PhysicalOperation
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.execution.SparkPlan
import
org.apache.spark.sql.execution.streaming.continuous.{WriteToContinuousDataSource,
WriteToContinuousDataSourceExec}
object DataSourceV2Strategy extends Strategy {
override def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
- case r: DataSourceV2Relation =>
- DataSourceV2ScanExec(r.output, r.source, r.options, r.pushedFilters,
r.reader) :: Nil
+ case PhysicalOperation(project, filters, relation:
DataSourceV2Relation) =>
+ val projectSet = AttributeSet(project.flatMap(_.references))
+ val filterSet = AttributeSet(filters.flatMap(_.references))
+
+ val projection = if (filterSet.subsetOf(projectSet) &&
+ AttributeSet(relation.output) == projectSet) {
+ // When the required projection contains all of the filter columns
and column pruning alone
+ // can produce the required projection, push the required
projection.
+ // A final projection may still be needed if the data source
produces a different column
+ // order or if it cannot prune all of the nested columns.
+ relation.output
+ } else {
+ // When there are filter columns not already in the required
projection or when the required
+ // projection is more complicated than column pruning, base column
pruning on the set of
+ // all columns needed by both.
+ (projectSet ++ filterSet).toSeq
+ }
+
+ val reader = relation.newReader
--- End diff --
@cloud-fan, there's nothing forcing other data sources to implement the new
trait. Other sources can continue to report stats for the entire table and not
account for filters (the code assumes that row counts don't change). This just
opens the option of reporting stats that are more accurate using the filters
and projection that will be pushed.
Ideally, I think that stats-based decisions would happen after pushdown so
we get data that is as accurate as possible. But for now, this fixes the
regression for v2 sources that happens because we move pushdown to a later step
(conversion to physical plan like the other sources).
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