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From hvanhovell <...@git.apache.org>
Subject [GitHub] spark pull request #13494: [SPARK-15752] [SQL] Optimize metadata only query ...
Date Mon, 11 Jul 2016 10:48:15 GMT
Github user hvanhovell commented on a diff in the pull request:

    https://github.com/apache/spark/pull/13494#discussion_r70236613
  
    --- 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
    +        val partAttrs = l.output.filter(a => partColumns.contains(a.name.toLowerCase))
    +        Some(AttributeSet(partAttrs), l)
    +
    +      case relation: CatalogRelation if relation.catalogTable.partitionColumnNames.nonEmpty
=>
    +        val partColumns = relation.catalogTable.partitionColumnNames.map(_.toLowerCase).toSet
    +        val partAttrs = relation.output.filter(a => partColumns.contains(a.name.toLowerCase))
    +        Some(AttributeSet(partAttrs), relation)
    +
    +      case p @ Project(projectList, child) if projectList.forall(_.deterministic) =>
    +        unapply(child).flatMap { case (partAttrs, relation) =>
    --- End diff --
    
    Logic here is the same as the Filter case. Move this into a separate method?


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