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/*
* 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 java.util.Locale
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.catalog.{HiveTableRelation, SessionCatalog}
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.aggregate._
import org.apache.spark.sql.catalyst.planning.PhysicalOperation
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils}
import org.apache.spark.sql.errors.QueryCompilationErrors
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) extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = {
if (!conf.optimizerMetadataOnly) {
return plan
}
plan.transform {
case a @ Aggregate(_, aggExprs, child @ PhysicalOperation(
projectList, filters, PartitionedRelation(partAttrs, rel))) =>
// We only apply this optimization when only partitioned attributes are scanned.
if (AttributeSet((projectList ++ filters).flatMap(_.references)).subsetOf(partAttrs)) {
// The project list and filters all only refer to partition attributes, which means the
// the Aggregator operator can also only refer to partition attributes, and filters are
// all partition filters. This is a metadata only query we can optimize.
val aggFunctions = aggExprs.flatMap(_.collect {
case agg: AggregateExpression => agg
})
val isAllDistinctAgg = aggFunctions.forall { agg =>
agg.isDistinct || (agg.aggregateFunction match {
// `Max`, `Min`, `First` and `Last` 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 _: First => true
case _: Last => true
case _ => false
})
}
if (isAllDistinctAgg) {
logWarning("Since configuration `spark.sql.optimizer.metadataOnly` is enabled, " +
"Spark will scan partition-level metadata without scanning data files. " +
"This could result in wrong results when the partition metadata exists but the " +
"inclusive data files are empty."
)
a.withNewChildren(Seq(replaceTableScanWithPartitionMetadata(child, rel, filters)))
} else {
a
}
} else {
a
}
}
}
/**
* Returns the partition attributes of the table relation plan.
*/
private def getPartitionAttrs(
partitionColumnNames: Seq[String],
relation: LogicalPlan): Seq[Attribute] = {
val attrMap = relation.output.map(a => a.name.toLowerCase(Locale.ROOT) -> a).toMap
partitionColumnNames.map { colName =>
attrMap.getOrElse(colName.toLowerCase(Locale.ROOT),
throw QueryCompilationErrors.cannotFindColumnInRelationOutputError(colName, relation)
)
}
}
/**
* 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,
partFilters: Seq[Expression]): LogicalPlan = {
// this logic comes from PruneFileSourcePartitions. it ensures that the filter names match the
// relation's schema. PartitionedRelation ensures that the filters only reference partition cols
val normalizedFilters = partFilters.map { e =>
e transform {
case a: AttributeReference =>
a.withName(relation.output.find(_.semanticEquals(a)).get.name)
}
}.filterNot(SubqueryExpression.hasSubquery)
child transform {
case plan if plan eq relation =>
relation match {
case l @ LogicalRelation(fsRelation: HadoopFsRelation, _, _, isStreaming) =>
val partAttrs = getPartitionAttrs(fsRelation.partitionSchema.map(_.name), l)
val partitionData = fsRelation.location.listFiles(normalizedFilters, Nil)
LocalRelation(partAttrs, partitionData.map(_.values), isStreaming)
case relation: HiveTableRelation =>
val partAttrs = getPartitionAttrs(relation.tableMeta.partitionColumnNames, relation)
val caseInsensitiveProperties =
CaseInsensitiveMap(relation.tableMeta.storage.properties)
val timeZoneId = caseInsensitiveProperties.get(DateTimeUtils.TIMEZONE_OPTION)
.getOrElse(conf.sessionLocalTimeZone)
val partitions = relation.prunedPartitions match {
// for the case where partitions have already been pruned by PruneHiveTablePartitions
case Some(parts) => parts
case None => if (partFilters.nonEmpty) {
catalog.listPartitionsByFilter(relation.tableMeta.identifier, normalizedFilters)
} else {
catalog.listPartitions(relation.tableMeta.identifier)
}
}
val partitionData = partitions.map { p =>
InternalRow.fromSeq(partAttrs.map { attr =>
Cast(Literal(p.spec(attr.name)), attr.dataType, Option(timeZoneId)).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.
*/
object PartitionedRelation {
def unapply(plan: LogicalPlan): Option[(AttributeSet, LogicalPlan)] = {
plan match {
case l @ LogicalRelation(fsRelation: HadoopFsRelation, _, _, _)
if fsRelation.partitionSchema.nonEmpty =>
val partAttrs = AttributeSet(getPartitionAttrs(fsRelation.partitionSchema.map(_.name), l))
Some((partAttrs, l))
case relation: HiveTableRelation if relation.tableMeta.partitionColumnNames.nonEmpty =>
val partAttrs = AttributeSet(
getPartitionAttrs(relation.tableMeta.partitionColumnNames, relation))
Some((partAttrs, relation))
case _ => None
}
}
}
}
