Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/13494#discussion_r70362160
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/OptimizeMetadataOnlyQuery.scala
---
@@ -0,0 +1,153 @@
+/*
+ * 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`, `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) {
+
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 partAttrs = PartitionedRelation.getPartitionAttrs(
+ fsRelation.partitionSchema.map(_.name), l)
+ val partitionData = fsRelation.location.listFiles(filters =
Nil)
+ LocalRelation(partAttrs, partitionData.map(_.values))
+
+ case relation: CatalogRelation =>
+ val partAttrs = PartitionedRelation.getPartitionAttrs(
+ relation.catalogTable.partitionColumnNames, relation)
+ 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.
+ */
+ private[execution] object PartitionedRelation {
--- End diff --
classes under `execution` package are considered as private, we don't need
to mark them as private explicitly.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
