spark SchemaPruning 源码

spark SchemaPruning 代码

文件路径：/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SchemaPruning.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
 * limitations under the License.
 */

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

import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.planning.PhysicalOperation
import org.apache.spark.sql.catalyst.plans.logical.{Filter, LeafNode, LogicalPlan, Project}
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.execution.datasources.orc.OrcFileFormat
import org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat
import org.apache.spark.sql.types.{ArrayType, DataType, MapType, StructType}
import org.apache.spark.sql.util.SchemaUtils._

/**
 * Prunes unnecessary physical columns given a [[PhysicalOperation]] over a data source relation.
 * By "physical column", we mean a column as defined in the data source format like Parquet format
 * or ORC format. For example, in Spark SQL, a root-level Parquet column corresponds to a SQL
 * column, and a nested Parquet column corresponds to a [[StructField]].
 *
 * Also prunes the unnecessary metadata columns if any for all file formats.
 */
object SchemaPruning extends Rule[LogicalPlan] {
  import org.apache.spark.sql.catalyst.expressions.SchemaPruning._

  override def apply(plan: LogicalPlan): LogicalPlan =
    plan transformDown {
      case op @ PhysicalOperation(projects, filters,
      l @ LogicalRelation(hadoopFsRelation: HadoopFsRelation, _, _, _)) =>
        prunePhysicalColumns(l, projects, filters, hadoopFsRelation,
          (prunedDataSchema, prunedMetadataSchema) => {
            val prunedHadoopRelation =
              hadoopFsRelation.copy(dataSchema = prunedDataSchema)(hadoopFsRelation.sparkSession)
            buildPrunedRelation(l, prunedHadoopRelation, prunedMetadataSchema)
          }).getOrElse(op)
    }

  /**
   * This method returns optional logical plan. `None` is returned if no nested field is required or
   * all nested fields are required.
   *
   * This method will prune both the data schema and the metadata schema
   */
  private def prunePhysicalColumns(
      relation: LogicalRelation,
      projects: Seq[NamedExpression],
      filters: Seq[Expression],
      hadoopFsRelation: HadoopFsRelation,
      leafNodeBuilder: (StructType, StructType) => LeafNode): Option[LogicalPlan] = {

    val (normalizedProjects, normalizedFilters) =
      normalizeAttributeRefNames(relation.output, projects, filters)
    val requestedRootFields = identifyRootFields(normalizedProjects, normalizedFilters)

    // If requestedRootFields includes a nested field, continue. Otherwise,
    // return op
    if (requestedRootFields.exists { root: RootField => !root.derivedFromAtt }) {

      val prunedDataSchema = if (canPruneDataSchema(hadoopFsRelation)) {
        pruneSchema(hadoopFsRelation.dataSchema, requestedRootFields)
      } else {
        hadoopFsRelation.dataSchema
      }

      val metadataSchema =
        relation.output.collect { case FileSourceMetadataAttribute(attr) => attr }.toStructType
      val prunedMetadataSchema = if (metadataSchema.nonEmpty) {
        pruneSchema(metadataSchema, requestedRootFields)
      } else {
        metadataSchema
      }

      // If the data schema is different from the pruned data schema
      // OR
      // the metadata schema is different from the pruned metadata schema, continue.
      // Otherwise, return None.
      if (countLeaves(hadoopFsRelation.dataSchema) > countLeaves(prunedDataSchema) ||
        countLeaves(metadataSchema) > countLeaves(prunedMetadataSchema)) {
        val prunedRelation = leafNodeBuilder(prunedDataSchema, prunedMetadataSchema)
        val projectionOverSchema = ProjectionOverSchema(
          prunedDataSchema.merge(prunedMetadataSchema), AttributeSet(relation.output))
        Some(buildNewProjection(projects, normalizedProjects, normalizedFilters,
          prunedRelation, projectionOverSchema))
      } else {
        None
      }
    } else {
      None
    }
  }

  /**
   * Checks to see if the given relation can be pruned. Currently we support Parquet and ORC v1.
   */
  private def canPruneDataSchema(fsRelation: HadoopFsRelation): Boolean =
    conf.nestedSchemaPruningEnabled && (
      fsRelation.fileFormat.isInstanceOf[ParquetFileFormat] ||
        fsRelation.fileFormat.isInstanceOf[OrcFileFormat])

  /**
   * Normalizes the names of the attribute references in the given projects and filters to reflect
   * the names in the given logical relation. This makes it possible to compare attributes and
   * fields by name. Returns a tuple with the normalized projects and filters, respectively.
   */
  private def normalizeAttributeRefNames(
      output: Seq[AttributeReference],
      projects: Seq[NamedExpression],
      filters: Seq[Expression]): (Seq[NamedExpression], Seq[Expression]) = {
    val normalizedAttNameMap = output.map(att => (att.exprId, att.name)).toMap
    val normalizedProjects = projects.map(_.transform {
      case att: AttributeReference if normalizedAttNameMap.contains(att.exprId) =>
        att.withName(normalizedAttNameMap(att.exprId))
    }).map { case expr: NamedExpression => expr }
    val normalizedFilters = filters.map(_.transform {
      case att: AttributeReference if normalizedAttNameMap.contains(att.exprId) =>
        att.withName(normalizedAttNameMap(att.exprId))
    })
    (normalizedProjects, normalizedFilters)
  }

  /**
   * Builds the new output [[Project]] Spark SQL operator that has the `leafNode`.
   */
  private def buildNewProjection(
      projects: Seq[NamedExpression],
      normalizedProjects: Seq[NamedExpression],
      filters: Seq[Expression],
      leafNode: LeafNode,
      projectionOverSchema: ProjectionOverSchema): Project = {
    // Construct a new target for our projection by rewriting and
    // including the original filters where available
    val projectionChild =
      if (filters.nonEmpty) {
        val projectedFilters = filters.map(_.transformDown {
          case projectionOverSchema(expr) => expr
        })
        val newFilterCondition = projectedFilters.reduce(And)
        Filter(newFilterCondition, leafNode)
      } else {
        leafNode
      }

    // Construct the new projections of our Project by
    // rewriting the original projections
    val newProjects = normalizedProjects.map(_.transformDown {
      case projectionOverSchema(expr) => expr
    }).map { case expr: NamedExpression => expr }

    if (log.isDebugEnabled) {
      logDebug(s"New projects:\n${newProjects.map(_.treeString).mkString("\n")}")
    }

    Project(restoreOriginalOutputNames(newProjects, projects.map(_.name)), projectionChild)
  }

  /**
   * Builds a pruned logical relation from the output of the output relation and the schema of the
   * pruned base relation.
   */
  private def buildPrunedRelation(
      outputRelation: LogicalRelation,
      prunedBaseRelation: HadoopFsRelation,
      prunedMetadataSchema: StructType) = {
    val finalSchema = prunedBaseRelation.schema.merge(prunedMetadataSchema)
    val prunedOutput = getPrunedOutput(outputRelation.output, finalSchema)
    outputRelation.copy(relation = prunedBaseRelation, output = prunedOutput)
  }

  // Prune the given output to make it consistent with `requiredSchema`.
  private def getPrunedOutput(
      output: Seq[AttributeReference],
      requiredSchema: StructType): Seq[AttributeReference] = {
    // We need to update the data type of the output attributes to use the pruned ones.
    // so that references to the original relation's output are not broken
    val nameAttributeMap = output.map(att => (att.name, att)).toMap
    requiredSchema
      .toAttributes
      .map {
        case att if nameAttributeMap.contains(att.name) =>
          nameAttributeMap(att.name).withDataType(att.dataType)
        case att => att
      }
  }

  /**
   * Counts the "leaf" fields of the given dataType. Informally, this is the
   * number of fields of non-complex data type in the tree representation of
   * [[DataType]].
   */
  private def countLeaves(dataType: DataType): Int = {
    dataType match {
      case array: ArrayType => countLeaves(array.elementType)
      case map: MapType => countLeaves(map.keyType) + countLeaves(map.valueType)
      case struct: StructType =>
        struct.map(field => countLeaves(field.dataType)).sum
      case _ => 1
    }
  }
}

相关信息

spark 源码目录

相关文章

spark AggregatePushDownUtils 源码

spark ApplyCharTypePadding 源码

spark BasicWriteStatsTracker 源码

spark BucketingUtils 源码

spark CatalogFileIndex 源码

spark CodecStreams 源码

spark DataSource 源码

spark DataSourceStrategy 源码

spark DataSourceUtils 源码

spark DaysWritable 源码