path: root/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala

/*
 * 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
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package org.apache.spark.sql.execution.datasources

import scala.collection.mutable.ArrayBuffer

import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
import org.apache.spark.sql.catalyst.CatalystTypeConverters.convertToScala
import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
import org.apache.spark.sql.catalyst.expressions
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.planning.PhysicalOperation
import org.apache.spark.sql.catalyst.plans.logical
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.plans.physical.HashPartitioning
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.execution.DataSourceScan.{INPUT_PATHS, PUSHED_FILTERS}
import org.apache.spark.sql.execution.command.ExecutedCommand
import org.apache.spark.sql.sources._
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.UTF8String

/**
 * Replaces generic operations with specific variants that are designed to work with Spark
 * SQL Data Sources.
 */
private[sql] object DataSourceAnalysis extends Rule[LogicalPlan] {
  override def apply(plan: LogicalPlan): LogicalPlan = plan transform {
    case i @ logical.InsertIntoTable(
           l @ LogicalRelation(t: HadoopFsRelation, _, _), part, query, overwrite, false)
        if query.resolved && t.schema.asNullable == query.schema.asNullable =>

      // Sanity checks
      if (t.location.paths.size != 1) {
        throw new AnalysisException(
          "Can only write data to relations with a single path.")
      }

      val outputPath = t.location.paths.head
      val inputPaths = query.collect {
        case LogicalRelation(r: HadoopFsRelation, _, _) => r.location.paths
      }.flatten

      val mode = if (overwrite) SaveMode.Overwrite else SaveMode.Append
      if (overwrite && inputPaths.contains(outputPath)) {
        throw new AnalysisException(
          "Cannot overwrite a path that is also being read from.")
      }

      InsertIntoHadoopFsRelation(
        outputPath,
        t.partitionSchema.fields.map(_.name).map(UnresolvedAttribute(_)),
        t.bucketSpec,
        t.fileFormat,
        () => t.refresh(),
        t.options,
        query,
        mode)
  }
}

/**
 * A Strategy for planning scans over data sources defined using the sources API.
 */
private[sql] object DataSourceStrategy extends Strategy with Logging {
  def apply(plan: LogicalPlan): Seq[execution.SparkPlan] = plan match {
    case PhysicalOperation(projects, filters, l @ LogicalRelation(t: CatalystScan, _, _)) =>
      pruneFilterProjectRaw(
        l,
        projects,
        filters,
        (requestedColumns, allPredicates, _) =>
          toCatalystRDD(l, requestedColumns, t.buildScan(requestedColumns, allPredicates))) :: Nil

    case PhysicalOperation(projects, filters, l @ LogicalRelation(t: PrunedFilteredScan, _, _)) =>
      pruneFilterProject(
        l,
        projects,
        filters,
        (a, f) => toCatalystRDD(l, a, t.buildScan(a.map(_.name).toArray, f))) :: Nil

    case PhysicalOperation(projects, filters, l @ LogicalRelation(t: PrunedScan, _, _)) =>
      pruneFilterProject(
        l,
        projects,
        filters,
        (a, _) => toCatalystRDD(l, a, t.buildScan(a.map(_.name).toArray))) :: Nil

    case l @ LogicalRelation(baseRelation: TableScan, _, _) =>
      execution.DataSourceScan.create(
        l.output, toCatalystRDD(l, baseRelation.buildScan()), baseRelation) :: Nil

    case i @ logical.InsertIntoTable(l @ LogicalRelation(t: InsertableRelation, _, _),
      part, query, overwrite, false) if part.isEmpty =>
      ExecutedCommand(InsertIntoDataSource(l, query, overwrite)) :: Nil

    case _ => Nil
  }

  // Get the bucket ID based on the bucketing values.
  // Restriction: Bucket pruning works iff the bucketing column has one and only one column.
  def getBucketId(bucketColumn: Attribute, numBuckets: Int, value: Any): Int = {
    val mutableRow = new SpecificMutableRow(Seq(bucketColumn.dataType))
    mutableRow(0) = Cast(Literal(value), bucketColumn.dataType).eval(null)
    val bucketIdGeneration = UnsafeProjection.create(
      HashPartitioning(bucketColumn :: Nil, numBuckets).partitionIdExpression :: Nil,
      bucketColumn :: Nil)

    bucketIdGeneration(mutableRow).getInt(0)
  }

  // Based on Public API.
  protected def pruneFilterProject(
      relation: LogicalRelation,
      projects: Seq[NamedExpression],
      filterPredicates: Seq[Expression],
      scanBuilder: (Seq[Attribute], Array[Filter]) => RDD[InternalRow]) = {
    pruneFilterProjectRaw(
      relation,
      projects,
      filterPredicates,
      (requestedColumns, _, pushedFilters) => {
        scanBuilder(requestedColumns, pushedFilters.toArray)
      })
  }

  // Based on Catalyst expressions. The `scanBuilder` function accepts three arguments:
  //
  //  1. A `Seq[Attribute]`, containing all required column attributes. Used to handle relation
  //     traits that support column pruning (e.g. `PrunedScan` and `PrunedFilteredScan`).
  //
  //  2. A `Seq[Expression]`, containing all gathered Catalyst filter expressions, only used for
  //     `CatalystScan`.
  //
  //  3. A `Seq[Filter]`, containing all data source `Filter`s that are converted from (possibly a
  //     subset of) Catalyst filter expressions and can be handled by `relation`.  Used to handle
  //     relation traits (`CatalystScan` excluded) that support filter push-down (e.g.
  //     `PrunedFilteredScan` and `HadoopFsRelation`).
  //
  // Note that 2 and 3 shouldn't be used together.
  protected def pruneFilterProjectRaw(
    relation: LogicalRelation,
    projects: Seq[NamedExpression],
    filterPredicates: Seq[Expression],
    scanBuilder: (Seq[Attribute], Seq[Expression], Seq[Filter]) => RDD[InternalRow]) = {

    val projectSet = AttributeSet(projects.flatMap(_.references))
    val filterSet = AttributeSet(filterPredicates.flatMap(_.references))

    val candidatePredicates = filterPredicates.map { _ transform {
      case a: AttributeReference => relation.attributeMap(a) // Match original case of attributes.
    }}

    val (unhandledPredicates, pushedFilters) =
      selectFilters(relation.relation, candidatePredicates)

    // A set of column attributes that are only referenced by pushed down filters.  We can eliminate
    // them from requested columns.
    val handledSet = {
      val handledPredicates = filterPredicates.filterNot(unhandledPredicates.contains)
      val unhandledSet = AttributeSet(unhandledPredicates.flatMap(_.references))
      AttributeSet(handledPredicates.flatMap(_.references)) --
        (projectSet ++ unhandledSet).map(relation.attributeMap)
    }

    // Combines all Catalyst filter `Expression`s that are either not convertible to data source
    // `Filter`s or cannot be handled by `relation`.
    val filterCondition = unhandledPredicates.reduceLeftOption(expressions.And)

    val metadata: Map[String, String] = {
      val pairs = ArrayBuffer.empty[(String, String)]

      if (pushedFilters.nonEmpty) {
        pairs += (PUSHED_FILTERS -> pushedFilters.mkString("[", ", ", "]"))
      }

      relation.relation match {
        case r: HadoopFsRelation => pairs += INPUT_PATHS -> r.location.paths.mkString(", ")
        case _ =>
      }

      pairs.toMap
    }

    if (projects.map(_.toAttribute) == projects &&
        projectSet.size == projects.size &&
        filterSet.subsetOf(projectSet)) {
      // When it is possible to just use column pruning to get the right projection and
      // when the columns of this projection are enough to evaluate all filter conditions,
      // just do a scan followed by a filter, with no extra project.
      val requestedColumns = projects
        // Safe due to if above.
        .asInstanceOf[Seq[Attribute]]
        // Match original case of attributes.
        .map(relation.attributeMap)
        // Don't request columns that are only referenced by pushed filters.
        .filterNot(handledSet.contains)

      val scan = execution.DataSourceScan.create(
        projects.map(_.toAttribute),
        scanBuilder(requestedColumns, candidatePredicates, pushedFilters),
        relation.relation, metadata)
      filterCondition.map(execution.Filter(_, scan)).getOrElse(scan)
    } else {
      // Don't request columns that are only referenced by pushed filters.
      val requestedColumns =
        (projectSet ++ filterSet -- handledSet).map(relation.attributeMap).toSeq

      val scan = execution.DataSourceScan.create(
        requestedColumns,
        scanBuilder(requestedColumns, candidatePredicates, pushedFilters),
        relation.relation, metadata)
      execution.Project(
        projects, filterCondition.map(execution.Filter(_, scan)).getOrElse(scan))
    }
  }

  /**
   * Convert RDD of Row into RDD of InternalRow with objects in catalyst types
   */
  private[this] def toCatalystRDD(
      relation: LogicalRelation,
      output: Seq[Attribute],
      rdd: RDD[Row]): RDD[InternalRow] = {
    if (relation.relation.needConversion) {
      execution.RDDConversions.rowToRowRdd(rdd, output.map(_.dataType))
    } else {
      rdd.asInstanceOf[RDD[InternalRow]]
    }
  }

  /**
   * Convert RDD of Row into RDD of InternalRow with objects in catalyst types
   */
  private[this] def toCatalystRDD(relation: LogicalRelation, rdd: RDD[Row]): RDD[InternalRow] = {
    toCatalystRDD(relation, relation.output, rdd)
  }

  /**
   * Tries to translate a Catalyst [[Expression]] into data source [[Filter]].
   *
   * @return a `Some[Filter]` if the input [[Expression]] is convertible, otherwise a `None`.
   */
  protected[sql] def translateFilter(predicate: Expression): Option[Filter] = {
    predicate match {
      case expressions.EqualTo(a: Attribute, Literal(v, t)) =>
        Some(sources.EqualTo(a.name, convertToScala(v, t)))
      case expressions.EqualTo(Literal(v, t), a: Attribute) =>
        Some(sources.EqualTo(a.name, convertToScala(v, t)))

      case expressions.EqualNullSafe(a: Attribute, Literal(v, t)) =>
        Some(sources.EqualNullSafe(a.name, convertToScala(v, t)))
      case expressions.EqualNullSafe(Literal(v, t), a: Attribute) =>
        Some(sources.EqualNullSafe(a.name, convertToScala(v, t)))

      case expressions.GreaterThan(a: Attribute, Literal(v, t)) =>
        Some(sources.GreaterThan(a.name, convertToScala(v, t)))
      case expressions.GreaterThan(Literal(v, t), a: Attribute) =>
        Some(sources.LessThan(a.name, convertToScala(v, t)))

      case expressions.LessThan(a: Attribute, Literal(v, t)) =>
        Some(sources.LessThan(a.name, convertToScala(v, t)))
      case expressions.LessThan(Literal(v, t), a: Attribute) =>
        Some(sources.GreaterThan(a.name, convertToScala(v, t)))

      case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, t)) =>
        Some(sources.GreaterThanOrEqual(a.name, convertToScala(v, t)))
      case expressions.GreaterThanOrEqual(Literal(v, t), a: Attribute) =>
        Some(sources.LessThanOrEqual(a.name, convertToScala(v, t)))

      case expressions.LessThanOrEqual(a: Attribute, Literal(v, t)) =>
        Some(sources.LessThanOrEqual(a.name, convertToScala(v, t)))
      case expressions.LessThanOrEqual(Literal(v, t), a: Attribute) =>
        Some(sources.GreaterThanOrEqual(a.name, convertToScala(v, t)))

      case expressions.InSet(a: Attribute, set) =>
        val toScala = CatalystTypeConverters.createToScalaConverter(a.dataType)
        Some(sources.In(a.name, set.toArray.map(toScala)))

      // Because we only convert In to InSet in Optimizer when there are more than certain
      // items. So it is possible we still get an In expression here that needs to be pushed
      // down.
      case expressions.In(a: Attribute, list) if !list.exists(!_.isInstanceOf[Literal]) =>
        val hSet = list.map(e => e.eval(EmptyRow))
        val toScala = CatalystTypeConverters.createToScalaConverter(a.dataType)
        Some(sources.In(a.name, hSet.toArray.map(toScala)))

      case expressions.IsNull(a: Attribute) =>
        Some(sources.IsNull(a.name))
      case expressions.IsNotNull(a: Attribute) =>
        Some(sources.IsNotNull(a.name))

      case expressions.And(left, right) =>
        (translateFilter(left) ++ translateFilter(right)).reduceOption(sources.And)

      case expressions.Or(left, right) =>
        for {
          leftFilter <- translateFilter(left)
          rightFilter <- translateFilter(right)
        } yield sources.Or(leftFilter, rightFilter)

      case expressions.Not(child) =>
        translateFilter(child).map(sources.Not)

      case expressions.StartsWith(a: Attribute, Literal(v: UTF8String, StringType)) =>
        Some(sources.StringStartsWith(a.name, v.toString))

      case expressions.EndsWith(a: Attribute, Literal(v: UTF8String, StringType)) =>
        Some(sources.StringEndsWith(a.name, v.toString))

      case expressions.Contains(a: Attribute, Literal(v: UTF8String, StringType)) =>
        Some(sources.StringContains(a.name, v.toString))

      case _ => None
    }
  }

  /**
   * Selects Catalyst predicate [[Expression]]s which are convertible into data source [[Filter]]s
   * and can be handled by `relation`.
   *
   * @return A pair of `Seq[Expression]` and `Seq[Filter]`. The first element contains all Catalyst
   *         predicate [[Expression]]s that are either not convertible or cannot be handled by
   *         `relation`. The second element contains all converted data source [[Filter]]s that
   *         will be pushed down to the data source.
   */
  protected[sql] def selectFilters(
    relation: BaseRelation,
    predicates: Seq[Expression]): (Seq[Expression], Seq[Filter]) = {

    // For conciseness, all Catalyst filter expressions of type `expressions.Expression` below are
    // called `predicate`s, while all data source filters of type `sources.Filter` are simply called
    // `filter`s.

    val translated: Seq[(Expression, Filter)] =
      for {
        predicate <- predicates
        filter <- translateFilter(predicate)
      } yield predicate -> filter

    // A map from original Catalyst expressions to corresponding translated data source filters.
    val translatedMap: Map[Expression, Filter] = translated.toMap

    // Catalyst predicate expressions that cannot be translated to data source filters.
    val unrecognizedPredicates = predicates.filterNot(translatedMap.contains)

    // Data source filters that cannot be handled by `relation`. The semantic of a unhandled filter
    // at here is that a data source may not be able to apply this filter to every row
    // of the underlying dataset.
    val unhandledFilters = relation.unhandledFilters(translatedMap.values.toArray).toSet

    val (unhandled, handled) = translated.partition {
      case (predicate, filter) =>
        unhandledFilters.contains(filter)
    }

    // Catalyst predicate expressions that can be translated to data source filters, but cannot be
    // handled by `relation`.
    val (unhandledPredicates, _) = unhandled.unzip

    // Translated data source filters that can be handled by `relation`
    val (_, handledFilters) = handled.unzip

    // translated contains all filters that have been converted to the public Filter interface.
    // We should always push them to the data source no matter whether the data source can apply
    // a filter to every row or not.
    val (_, translatedFilters) = translated.unzip

    (unrecognizedPredicates ++ unhandledPredicates, translatedFilters)
  }
}