spark FileTable 源码

  • 2022-10-20
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spark FileTable 代码


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 * 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
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package org.apache.spark.sql.execution.datasources.v2

import java.util

import scala.collection.JavaConverters._

import org.apache.hadoop.fs.{FileStatus, Path}

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.connector.catalog.{SupportsRead, SupportsWrite, Table, TableCapability}
import org.apache.spark.sql.connector.catalog.TableCapability._
import org.apache.spark.sql.connector.expressions.Transform
import org.apache.spark.sql.errors.QueryCompilationErrors
import org.apache.spark.sql.execution.datasources._
import org.apache.spark.sql.execution.streaming.{FileStreamSink, MetadataLogFileIndex}
import org.apache.spark.sql.types.{DataType, StructType}
import org.apache.spark.sql.util.CaseInsensitiveStringMap
import org.apache.spark.sql.util.SchemaUtils

abstract class FileTable(
    sparkSession: SparkSession,
    options: CaseInsensitiveStringMap,
    paths: Seq[String],
    userSpecifiedSchema: Option[StructType])
  extends Table with SupportsRead with SupportsWrite {

  import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._

  lazy val fileIndex: PartitioningAwareFileIndex = {
    val caseSensitiveMap = options.asCaseSensitiveMap.asScala.toMap
    // Hadoop Configurations are case sensitive.
    val hadoopConf = sparkSession.sessionState.newHadoopConfWithOptions(caseSensitiveMap)
    if (FileStreamSink.hasMetadata(paths, hadoopConf, sparkSession.sessionState.conf)) {
      // We are reading from the results of a streaming query. We will load files from
      // the metadata log instead of listing them using HDFS APIs.
      new MetadataLogFileIndex(sparkSession, new Path(paths.head),
        options.asScala.toMap, userSpecifiedSchema)
    } else {
      // This is a non-streaming file based datasource.
      val rootPathsSpecified = DataSource.checkAndGlobPathIfNecessary(paths, hadoopConf,
        checkEmptyGlobPath = true, checkFilesExist = true, enableGlobbing = globPaths)
      val fileStatusCache = FileStatusCache.getOrCreate(sparkSession)
      new InMemoryFileIndex(
        sparkSession, rootPathsSpecified, caseSensitiveMap, userSpecifiedSchema, fileStatusCache)

  lazy val dataSchema: StructType = {
    val schema = { schema =>
      val partitionSchema = fileIndex.partitionSchema
      val resolver = sparkSession.sessionState.conf.resolver
      StructType(schema.filterNot(f => partitionSchema.exists(p => resolver(,
    }.orElse {
    }.getOrElse {
      throw QueryCompilationErrors.dataSchemaNotSpecifiedError(formatName)
    fileIndex match {
      case _: MetadataLogFileIndex => schema
      case _ => schema.asNullable

  override lazy val schema: StructType = {
    val caseSensitive = sparkSession.sessionState.conf.caseSensitiveAnalysis
      "in the data schema", caseSensitive)
    dataSchema.foreach { field =>
      if (!supportsDataType(field.dataType)) {
        throw QueryCompilationErrors.dataTypeUnsupportedByDataSourceError(formatName, field)
    val partitionSchema = fileIndex.partitionSchema
      "in the partition schema", caseSensitive)
    val partitionNameSet: Set[String] =, caseSensitive)).toSet

    // When data and partition schemas have overlapping columns,
    // tableSchema = dataSchema - overlapSchema + partitionSchema
    val fields = dataSchema.fields.filterNot { field =>
      val colName = PartitioningUtils.getColName(field, caseSensitive)
    } ++ partitionSchema.fields

  override def partitioning: Array[Transform] = fileIndex.partitionSchema.names.toSeq.asTransforms

  override def properties: util.Map[String, String] = options.asCaseSensitiveMap

  override def capabilities: java.util.Set[TableCapability] = FileTable.CAPABILITIES

   * When possible, this method should return the schema of the given `files`.  When the format
   * does not support inference, or no valid files are given should return None.  In these cases
   * Spark will require that user specify the schema manually.
  def inferSchema(files: Seq[FileStatus]): Option[StructType]

   * Returns whether this format supports the given [[DataType]] in read/write path.
   * By default all data types are supported.
  def supportsDataType(dataType: DataType): Boolean = true

   * The string that represents the format that this data source provider uses. This is
   * overridden by children to provide a nice alias for the data source. For example:
   * {{{
   *   override def formatName(): String = "ORC"
   * }}}
  def formatName: String

   * Returns a V1 [[FileFormat]] class of the same file data source.
   * This is a solution for the following cases:
   * 1. File datasource V2 implementations cause regression. Users can disable the problematic data
   *    source via SQL configuration and fall back to FileFormat.
   * 2. Catalog support is required, which is still under development for data source V2.
  def fallbackFileFormat: Class[_ <: FileFormat]

   * Whether or not paths should be globbed before being used to access files.
  private def globPaths: Boolean = {
    val entry = options.get(DataSource.GLOB_PATHS_KEY)
    Option(entry).map(_ == "true").getOrElse(true)

object FileTable {
  private val CAPABILITIES = util.EnumSet.of(BATCH_READ, BATCH_WRITE)


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