Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/21503#discussion_r194839473
--- 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 --
it's nice to decouple the problem and do pushdown during planning, but I
feel the cost is too high in this approach. For file-based data sources, we
need to query hive metastore to apply partitioning pruning during filter
pushdown, and this can be very expensive. Doing it twice looks scaring to me.
cc @gatorsmile @dongjoon-hyun @mallman , please correct me if I have a
wrong understanding.
also cc @wzhfy do you have an estimation about how long it takes to move
statistics to physical plan?
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