spark FileScanBuilder 源码

  • 2022-10-20
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spark FileScanBuilder 代码


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 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
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 * (the "License"); you may not use this file except in compliance with
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package org.apache.spark.sql.execution.datasources.v2

import scala.collection.mutable

import org.apache.spark.sql.{sources, SparkSession}
import org.apache.spark.sql.catalyst.expressions.Expression
import org.apache.spark.sql.connector.expressions.filter.Predicate
import{ScanBuilder, SupportsPushDownRequiredColumns}
import org.apache.spark.sql.execution.datasources.{DataSourceStrategy, DataSourceUtils, PartitioningAwareFileIndex, PartitioningUtils}
import org.apache.spark.sql.internal.connector.SupportsPushDownCatalystFilters
import org.apache.spark.sql.sources.Filter
import org.apache.spark.sql.types.StructType

abstract class FileScanBuilder(
    sparkSession: SparkSession,
    fileIndex: PartitioningAwareFileIndex,
    dataSchema: StructType)
  extends ScanBuilder
    with SupportsPushDownRequiredColumns
    with SupportsPushDownCatalystFilters {
  private val partitionSchema = fileIndex.partitionSchema
  private val isCaseSensitive = sparkSession.sessionState.conf.caseSensitiveAnalysis
  protected val supportsNestedSchemaPruning = false
  protected var requiredSchema = StructType(dataSchema.fields ++ partitionSchema.fields)
  protected var partitionFilters = Seq.empty[Expression]
  protected var dataFilters = Seq.empty[Expression]
  protected var pushedDataFilters = Array.empty[Filter]

  override def pruneColumns(requiredSchema: StructType): Unit = {
    // [SPARK-30107] While `requiredSchema` might have pruned nested columns,
    // the actual data schema of this scan is determined in `readDataSchema`.
    // File formats that don't support nested schema pruning,
    // use `requiredSchema` as a reference and prune only top-level columns.
    this.requiredSchema = requiredSchema

  protected def readDataSchema(): StructType = {
    val requiredNameSet = createRequiredNameSet()
    val schema = if (supportsNestedSchemaPruning) requiredSchema else dataSchema
    val fields = schema.fields.filter { field =>
      val colName = PartitioningUtils.getColName(field, isCaseSensitive)
      requiredNameSet.contains(colName) && !partitionNameSet.contains(colName)

  def readPartitionSchema(): StructType = {
    val requiredNameSet = createRequiredNameSet()
    val fields = partitionSchema.fields.filter { field =>
      val colName = PartitioningUtils.getColName(field, isCaseSensitive)

  override def pushFilters(filters: Seq[Expression]): Seq[Expression] = {
    val (deterministicFilters, nonDeterminsticFilters) = filters.partition(_.deterministic)
    val (partitionFilters, dataFilters) =
      DataSourceUtils.getPartitionFiltersAndDataFilters(partitionSchema, deterministicFilters)
    this.partitionFilters = partitionFilters
    this.dataFilters = dataFilters
    val translatedFilters = mutable.ArrayBuffer.empty[sources.Filter]
    for (filterExpr <- dataFilters) {
      val translated = DataSourceStrategy.translateFilter(filterExpr, true)
      if (translated.nonEmpty) {
        translatedFilters += translated.get
    pushedDataFilters = pushDataFilters(translatedFilters.toArray)
    dataFilters ++ nonDeterminsticFilters

  override def pushedFilters: Array[Predicate] =

   * Push down data filters to the file source, so the data filters can be evaluated there to
   * reduce the size of the data to be read. By default, data filters are not pushed down.
   * File source needs to implement this method to push down data filters.
  protected def pushDataFilters(dataFilters: Array[Filter]): Array[Filter] = Array.empty[Filter]

  private def createRequiredNameSet(): Set[String] =, isCaseSensitive)).toSet

  val partitionNameSet: Set[String] =, isCaseSensitive)).toSet


spark 源码目录


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spark AlterTableExec 源码

spark BatchScanExec 源码

spark CacheTableExec 源码

spark ContinuousScanExec 源码

spark CreateIndexExec 源码

spark CreateNamespaceExec 源码

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