cloud-fan commented on a change in pull request #32049:
URL: https://github.com/apache/spark/pull/32049#discussion_r667098021
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2ScanRelationPushDown.scala
##########
@@ -17,61 +17,179 @@
package org.apache.spark.sql.execution.datasources.v2
-import org.apache.spark.sql.catalyst.expressions.{And, Expression,
NamedExpression, ProjectionOverSchema, SubqueryExpression}
-import org.apache.spark.sql.catalyst.planning.ScanOperation
-import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan,
Project}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
+import org.apache.spark.sql.catalyst.planning.{OperationHelper, ScanOperation}
+import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, Filter,
LeafNode, LogicalPlan, Project}
import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.connector.read.{Scan, V1Scan}
+import org.apache.spark.sql.catalyst.util.toPrettySQL
+import org.apache.spark.sql.connector.expressions.Aggregation
+import org.apache.spark.sql.connector.read.{Scan, ScanBuilder,
SupportsPushDownAggregates, SupportsPushDownFilters, V1Scan}
import org.apache.spark.sql.execution.datasources.DataSourceStrategy
import org.apache.spark.sql.sources
import org.apache.spark.sql.types.StructType
-object V2ScanRelationPushDown extends Rule[LogicalPlan] {
+object V2ScanRelationPushDown extends Rule[LogicalPlan] with AliasHelper
+ with OperationHelper with PredicateHelper {
import DataSourceV2Implicits._
- override def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
- case ScanOperation(project, filters, relation: DataSourceV2Relation) =>
- val scanBuilder =
relation.table.asReadable.newScanBuilder(relation.options)
+ def apply(plan: LogicalPlan): LogicalPlan = {
+
applyColumnPruning(pushdownAggregate(pushDownFilters(createScanBuilder(plan))))
+ }
+
+ private def createScanBuilder(plan: LogicalPlan) = plan.transform {
+ case r: DataSourceV2Relation =>
+ ScanBuilderHolder(r.output, r,
r.table.asReadable.newScanBuilder(r.options))
+ }
- val normalizedFilters = DataSourceStrategy.normalizeExprs(filters,
relation.output)
+ private def pushDownFilters(plan: LogicalPlan) = plan.transform {
+ // update the scan builder with filter push down and return a new plan
with filter pushed
+ case filter @ Filter(_, sHolder: ScanBuilderHolder) =>
+ val (filters, _, _) = collectFilters(filter).get
+
+ val normalizedFilters =
+ DataSourceStrategy.normalizeExprs(filters, sHolder.relation.output)
val (normalizedFiltersWithSubquery, normalizedFiltersWithoutSubquery) =
normalizedFilters.partition(SubqueryExpression.hasSubquery)
// `pushedFilters` will be pushed down and evaluated in the underlying
data sources.
// `postScanFilters` need to be evaluated after the scan.
// `postScanFilters` and `pushedFilters` can overlap, e.g. the parquet
row group filter.
val (pushedFilters, postScanFiltersWithoutSubquery) =
PushDownUtils.pushFilters(
- scanBuilder, normalizedFiltersWithoutSubquery)
+ sHolder.builder, normalizedFiltersWithoutSubquery)
val postScanFilters = postScanFiltersWithoutSubquery ++
normalizedFiltersWithSubquery
+ logInfo(
+ s"""
+ |Pushing operators to ${sHolder.relation.name}
+ |Pushed Filters: ${pushedFilters.mkString(", ")}
+ |Post-Scan Filters: ${postScanFilters.mkString(",")}
+ """.stripMargin)
+
+ val filterCondition = postScanFilters.reduceLeftOption(And)
+ filterCondition.map(Filter(_, sHolder)).getOrElse(sHolder)
+ }
+
+ def pushdownAggregate(plan: LogicalPlan): LogicalPlan = plan.transform {
+ // update the scan builder with agg pushdown and return a new plan with
agg pushed
+ case aggNode@Aggregate(groupingExpressions, resultExpressions, child) =>
+ child match {
+ case ScanOperation(project, filters, sHolder: ScanBuilderHolder) =>
+ sHolder.builder match {
+ case r: SupportsPushDownAggregates =>
+ if (filters.length == 0) { // can't push down aggregate if
postScanFilters exist
+ val aggregates = getAggregateExpression(resultExpressions,
project, sHolder)
+ val pushedAggregates = PushDownUtils
+ .pushAggregates(sHolder.builder, aggregates,
groupingExpressions)
+ if (pushedAggregates.aggregateExpressions.isEmpty) {
+ aggNode // return original plan node
+ } else {
+ // use the aggregate columns as the output columns
+ // e.g. TABLE t (c1 INT, c2 INT, c3 INT)
+ // SELECT min(c1), max(c1) FROM t;
+ // Use min(c1), max(c1) as output for
DataSourceV2ScanRelation
+ // We want to have the following logical plan:
+ // == Optimized Logical Plan ==
+ // Aggregate [min(min(c1)#21) AS min(c1)#17, max(max(c1)#22)
AS max(c1)#18]
+ // +- RelationV2[min(c1)#21, max(c1)#22] parquet file ...
+ val output = aggregates.map {
+ case agg: AggregateExpression =>
+ AttributeReference(toPrettySQL(agg), agg.dataType)()
+ }
+
+ // No need to do column pruning because only the aggregate
columns are used as
+ // DataSourceV2ScanRelation output columns. All the other
columns are not
+ // included in the output. Since PushDownUtils.pruneColumns
is not called,
+ // ScanBuilder.requiredSchema is not pruned, but
ScanBuilder.requiredSchema is
+ // not used anyways. The schema for aggregate columns will
be built in Scan.
+ val scan = sHolder.builder.build()
+
+ logInfo(
+ s"""
+ |Pushing operators to ${sHolder.relation.name}
+ |Pushed Aggregate Functions:
+ | ${pushedAggregates.aggregateExpressions.mkString(",
")}
+ |Output: ${output.mkString(", ")}
+ """.stripMargin)
+
+ val scanRelation =
DataSourceV2ScanRelation(sHolder.relation, scan, output)
+ val plan = Aggregate(groupingExpressions, resultExpressions,
scanRelation)
Review comment:
If we push down grouping expr `a + b` to the source, then spark side
shouldn't group by `a + b` again, spark should group by the output column of
the v2 source:
```
val agg = Aggregate(output.take(groupingExpressions.length),
resultExpressions, scanRelation)
```
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