[SPARK-15752] [SQL] Optimize metadata only query that has an aggregate whose children are deterministic project or filter operators. #13494
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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. | ||
| */ | ||
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| package org.apache.spark.sql.execution | ||
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| 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 | ||
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| /** | ||
| * 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] { | ||
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| def apply(plan: LogicalPlan): LogicalPlan = { | ||
| if (!conf.optimizerMetadataOnly) { | ||
| return plan | ||
| } | ||
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| 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 | ||
| } | ||
| } | ||
| } | ||
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| /** | ||
| * Returns the partition attributes of the table relation plan. | ||
| */ | ||
| private def getPartitionAttrs( | ||
| partitionColumnNames: Seq[String], | ||
| relation: LogicalPlan): Seq[Attribute] = { | ||
| val partColumns = partitionColumnNames.map(_.toLowerCase).toSet | ||
| relation.output.filter(a => partColumns.contains(a.name.toLowerCase)) | ||
| } | ||
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| /** | ||
| * 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 = getPartitionAttrs(fsRelation.partitionSchema.map(_.name), l) | ||
| val partitionData = fsRelation.location.listFiles(filters = Nil) | ||
| LocalRelation(partAttrs, partitionData.map(_.values)) | ||
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| case relation: CatalogRelation => | ||
| val partAttrs = 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) | ||
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| case _ => | ||
| throw new IllegalStateException(s"unrecognized table scan node: $relation, " + | ||
| s"please turn off ${SQLConf.OPTIMIZER_METADATA_ONLY.key} and try again.") | ||
| } | ||
| } | ||
| } | ||
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| /** | ||
| * 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 { | ||
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| def unapply(plan: LogicalPlan): Option[(AttributeSet, LogicalPlan)] = plan match { | ||
| case l @ LogicalRelation(fsRelation: HadoopFsRelation, _, _) | ||
| if fsRelation.partitionSchema.nonEmpty => | ||
| val partAttrs = getPartitionAttrs(fsRelation.partitionSchema.map(_.name), l) | ||
| Some(AttributeSet(partAttrs), l) | ||
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| case relation: CatalogRelation if relation.catalogTable.partitionColumnNames.nonEmpty => | ||
| val partAttrs = getPartitionAttrs(relation.catalogTable.partitionColumnNames, relation) | ||
| Some(AttributeSet(partAttrs), relation) | ||
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| case p @ Project(projectList, child) if projectList.forall(_.deterministic) => | ||
| unapply(child).flatMap { case (partAttrs, relation) => | ||
|
Contributor
There was a problem hiding this comment. Logic here is the same as the Filter case. Move this into a separate method?
Contributor
Author
There was a problem hiding this comment. Because there are three lines, I think it is unnecessary for a separate method that causes extra method called. |
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| if (p.references.subsetOf(partAttrs)) Some(p.outputSet, relation) else None | ||
| } | ||
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| case f @ Filter(condition, child) if condition.deterministic => | ||
| unapply(child).flatMap { case (partAttrs, relation) => | ||
| if (f.references.subsetOf(partAttrs)) Some(partAttrs, relation) else None | ||
| } | ||
|
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| case _ => None | ||
| } | ||
| } | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,120 @@ | ||
| /* | ||
| * 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._ | ||
| import org.apache.spark.sql.catalyst.plans.logical.LocalRelation | ||
| import org.apache.spark.sql.internal.SQLConf | ||
| import org.apache.spark.sql.test.SharedSQLContext | ||
|
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| class OptimizeMetadataOnlyQuerySuite extends QueryTest with SharedSQLContext { | ||
| import testImplicits._ | ||
|
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| override def beforeAll(): Unit = { | ||
| super.beforeAll() | ||
| val data = (1 to 10).map(i => (i, s"data-$i", i % 2, if ((i % 2) == 0) "even" else "odd")) | ||
| .toDF("col1", "col2", "partcol1", "partcol2") | ||
| data.write.partitionBy("partcol1", "partcol2").mode("append").saveAsTable("srcpart") | ||
| } | ||
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| override protected def afterAll(): Unit = { | ||
| try { | ||
| sql("DROP TABLE IF EXISTS srcpart") | ||
| } finally { | ||
| super.afterAll() | ||
| } | ||
| } | ||
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| private def assertMetadataOnlyQuery(df: DataFrame): Unit = { | ||
| val localRelations = df.queryExecution.optimizedPlan.collect { | ||
| case l @ LocalRelation(_, _) => l | ||
| } | ||
| assert(localRelations.size == 1) | ||
| } | ||
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| private def assertNotMetadataOnlyQuery(df: DataFrame): Unit = { | ||
| val localRelations = df.queryExecution.optimizedPlan.collect { | ||
| case l @ LocalRelation(_, _) => l | ||
| } | ||
| assert(localRelations.size == 0) | ||
| } | ||
|
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| private def testMetadataOnly(name: String, sqls: String*): Unit = { | ||
| test(name) { | ||
| withSQLConf(SQLConf.OPTIMIZER_METADATA_ONLY.key -> "true") { | ||
| sqls.foreach { case q => assertMetadataOnlyQuery(sql(q)) } | ||
| } | ||
| withSQLConf(SQLConf.OPTIMIZER_METADATA_ONLY.key -> "false") { | ||
| sqls.foreach { case q => assertNotMetadataOnlyQuery(sql(q)) } | ||
| } | ||
| } | ||
| } | ||
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| private def testNotMetadataOnly(name: String, sqls: String*): Unit = { | ||
|
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| test(name) { | ||
| withSQLConf(SQLConf.OPTIMIZER_METADATA_ONLY.key -> "true") { | ||
| sqls.foreach { case q => assertNotMetadataOnlyQuery(sql(q)) } | ||
| } | ||
| withSQLConf(SQLConf.OPTIMIZER_METADATA_ONLY.key -> "false") { | ||
| sqls.foreach { case q => assertNotMetadataOnlyQuery(sql(q)) } | ||
| } | ||
| } | ||
| } | ||
|
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| testMetadataOnly( | ||
| "Aggregate expression is partition columns", | ||
| "select partcol1 from srcpart group by partcol1", | ||
| "select partcol2 from srcpart where partcol1 = 0 group by partcol2") | ||
|
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| testMetadataOnly( | ||
| "Distinct aggregate function on partition columns", | ||
| "SELECT partcol1, count(distinct partcol2) FROM srcpart group by partcol1", | ||
| "SELECT partcol1, count(distinct partcol2) FROM srcpart where partcol1 = 0 group by partcol1") | ||
|
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| testMetadataOnly( | ||
| "Distinct on partition columns", | ||
| "select distinct partcol1, partcol2 from srcpart", | ||
| "select distinct c1 from (select partcol1 + 1 as c1 from srcpart where partcol1 = 0) t") | ||
|
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| testMetadataOnly( | ||
| "Aggregate function on partition columns which have same result w or w/o DISTINCT keyword", | ||
| "select max(partcol1) from srcpart", | ||
| "select min(partcol1) from srcpart where partcol1 = 0", | ||
| "select first(partcol1) from srcpart", | ||
| "select last(partcol1) from srcpart where partcol1 = 0", | ||
| "select partcol2, min(partcol1) from srcpart where partcol1 = 0 group by partcol2", | ||
| "select max(c1) from (select partcol1 + 1 as c1 from srcpart where partcol1 = 0) t") | ||
|
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| testNotMetadataOnly( | ||
| "Don't optimize metadata only query for non-partition columns", | ||
| "select col1 from srcpart group by col1", | ||
| "select partcol1, max(col1) from srcpart group by partcol1", | ||
| "select partcol1, count(distinct col1) from srcpart group by partcol1", | ||
| "select distinct partcol1, col1 from srcpart") | ||
|
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| testNotMetadataOnly( | ||
| "Don't optimize metadata only query for non-distinct aggregate function on partition columns", | ||
| "select partcol1, sum(partcol2) from srcpart group by partcol1", | ||
| "select partcol1, count(partcol2) from srcpart group by partcol1") | ||
|
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| testNotMetadataOnly( | ||
| "Don't optimize metadata only query for GroupingSet/Union operator", | ||
| "select partcol1, max(partcol2) from srcpart where partcol1 = 0 group by rollup (partcol1)", | ||
| "select partcol2 from (select partcol2 from srcpart where partcol1 = 0 union all " + | ||
| "select partcol2 from srcpart where partcol1 = 1) t group by partcol2") | ||
| } | ||
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First/Last?
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Yes, we need to handle them. thanks.