Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/13494#discussion_r69674988
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/OptimizeMetadataOnly.scala
---
@@ -0,0 +1,133 @@
+/*
+ * 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.AnalysisException
+import org.apache.spark.sql.catalyst.{CatalystConf, 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}
+
+/**
+ * When scanning only partition columns, get results based on partition
data without scanning files.
+ * It's used for operators that only need distinct values. Currently only
[[Aggregate]] operator
+ * which satisfy the following conditions are supported:
+ * 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 count(DISTINCT col1) FROM tbl GROUP BY col2.
+ * 3. aggregate function on partition columns which have same result w or
w/o DISTINCT keyword.
+ * e.g. SELECT Max(col2) FROM tbl GROUP BY col1.
+ */
+case class OptimizeMetadataOnly(
+ catalog: SessionCatalog,
+ conf: CatalystConf) extends Rule[LogicalPlan] {
+
+ def apply(plan: LogicalPlan): LogicalPlan = {
+ if (!conf.optimizerMetadataOnly) {
+ return plan
+ }
+
+ plan.transform {
+ case a @ Aggregate(_, aggExprs, child @
PartitionedRelation(partAttrs, relation)) =>
+ if (a.references.subsetOf(partAttrs)) {
+ val aggFunctions = aggExprs.flatMap(_.collect {
+ case agg: AggregateExpression => agg
+ })
+ val isPartitionDataOnly = aggFunctions.isEmpty ||
aggFunctions.forall { agg =>
+ agg.isDistinct || (agg.aggregateFunction match {
+ case _: Max => true
+ case _: Min => true
+ case _ => false
+ })
+ }
+ if (isPartitionDataOnly) {
+ a.withNewChildren(Seq(usePartitionData(child, relation)))
+ } else {
+ a
+ }
+ } else {
+ a
+ }
+ }
+ }
+
+ private def usePartitionData(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(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()
+ }
+ }
+ }
+
+ object PartitionedRelation {
--- End diff --
also need to document what the returned tuple means
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