Github user lianhuiwang commented on a diff in the pull request:
https://github.com/apache/spark/pull/13494#discussion_r70248137
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/OptimizeMetadataOnlyQuery.scala
---
@@ -0,0 +1,143 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.catalog.{CatalogRelation,
SessionCatalog}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.datasources.{HadoopFsRelation,
LogicalRelation}
+import org.apache.spark.sql.internal.SQLConf
+
+/**
+ * This rule optimizes the execution of queries that can be answered by
looking only at
+ * partition-level metadata. This applies when all the columns scanned are
partition columns, and
+ * the query has an aggregate operator that satisfies the following
conditions:
+ * 1. aggregate expression is partition columns.
+ * e.g. SELECT col FROM tbl GROUP BY col.
+ * 2. aggregate function on partition columns with DISTINCT.
+ * e.g. SELECT col1, count(DISTINCT col2) FROM tbl GROUP BY col1.
+ * 3. aggregate function on partition columns which have same result w or
w/o DISTINCT keyword.
+ * e.g. SELECT col1, Max(col2) FROM tbl GROUP BY col1.
+ */
+case class OptimizeMetadataOnlyQuery(
+ catalog: SessionCatalog,
+ conf: SQLConf) extends Rule[LogicalPlan] {
+
+ def apply(plan: LogicalPlan): LogicalPlan = {
+ if (!conf.optimizerMetadataOnly) {
+ return plan
+ }
+
+ plan.transform {
+ case a @ Aggregate(_, aggExprs, child @
PartitionedRelation(partAttrs, relation)) =>
+ // We only apply this optimization when only partitioned
attributes are scanned.
+ if (a.references.subsetOf(partAttrs)) {
+ val aggFunctions = aggExprs.flatMap(_.collect {
+ case agg: AggregateExpression => agg
+ })
+ val isAllDistinctAgg = aggFunctions.forall { agg =>
+ agg.isDistinct || (agg.aggregateFunction match {
+ // `Max` and `Min` are always distinct aggregate functions
no matter they have
+ // DISTINCT keyword or not, as the result will be same.
+ case _: Max => true
+ case _: Min => true
+ case _ => false
+ })
+ }
+ if (isAllDistinctAgg) {
+
a.withNewChildren(Seq(replaceTableScanWithPartitionMetadata(child, relation)))
+ } else {
+ a
+ }
+ } else {
+ a
+ }
+ }
+ }
+
+ /**
+ * Transform the given plan, find its table scan nodes that matches the
given relation, and then
+ * replace the table scan node with its corresponding partition values.
+ */
+ private def replaceTableScanWithPartitionMetadata(
+ child: LogicalPlan,
+ relation: LogicalPlan): LogicalPlan = {
+ child transform {
+ case plan if plan eq relation =>
+ relation match {
+ case l @ LogicalRelation(fsRelation: HadoopFsRelation, _, _) =>
+ val partColumns =
fsRelation.partitionSchema.map(_.name.toLowerCase).toSet
+ val partAttrs = l.output.filter(a =>
partColumns.contains(a.name.toLowerCase))
+ val partitionData = fsRelation.location.listFiles(filters =
Nil)
+ LocalRelation(partAttrs, partitionData.map(_.values))
+
+ case relation: CatalogRelation =>
+ val partColumns =
relation.catalogTable.partitionColumnNames.map(_.toLowerCase).toSet
+ val partAttrs = relation.output.filter(a =>
partColumns.contains(a.name.toLowerCase))
+ val partitionData =
catalog.listPartitions(relation.catalogTable.identifier).map { p =>
+ InternalRow.fromSeq(partAttrs.map { attr =>
+ Cast(Literal(p.spec(attr.name)), attr.dataType).eval()
+ })
+ }
+ LocalRelation(partAttrs, partitionData)
+
+ case _ =>
+ throw new IllegalStateException(s"unrecognized table scan
node: $relation, " +
+ s"please turn off ${SQLConf.OPTIMIZER_METADATA_ONLY.key} and
try again.")
+ }
+ }
+ }
+
+ /**
+ * A pattern that finds the partitioned table relation node inside the
given plan, and returns a
+ * pair of the partition attributes and the table relation node.
+ *
+ * It keeps traversing down the given plan tree if there is a
[[Project]] or [[Filter]] with
+ * deterministic expressions, and returns result after reaching the
partitioned table relation
+ * node.
+ */
+ object PartitionedRelation {
+ def unapply(plan: LogicalPlan): Option[(AttributeSet, LogicalPlan)] =
plan match {
+ case l @ LogicalRelation(fsRelation: HadoopFsRelation, _, _)
+ if fsRelation.partitionSchema.nonEmpty =>
+ val partColumns =
fsRelation.partitionSchema.map(_.name.toLowerCase).toSet
--- End diff --
Because there are two lines, I think it is unnecessary for a separate
method that causes extra method called.
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