spark FileTable 源码

spark FileTable 代码

文件路径：/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/FileTable.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.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 = userSpecifiedSchema.map { schema =>
      val partitionSchema = fileIndex.partitionSchema
      val resolver = sparkSession.sessionState.conf.resolver
      StructType(schema.filterNot(f => partitionSchema.exists(p => resolver(p.name, f.name))))
    }.orElse {
      inferSchema(fileIndex.allFiles())
    }.getOrElse {
      throw QueryCompilationErrors.dataSchemaNotSpecifiedError(formatName)
    }
    fileIndex match {
      case _: MetadataLogFileIndex => schema
      case _ => schema.asNullable
    }
  }

  override lazy val schema: StructType = {
    val caseSensitive = sparkSession.sessionState.conf.caseSensitiveAnalysis
    SchemaUtils.checkSchemaColumnNameDuplication(dataSchema,
      "in the data schema", caseSensitive)
    dataSchema.foreach { field =>
      if (!supportsDataType(field.dataType)) {
        throw QueryCompilationErrors.dataTypeUnsupportedByDataSourceError(formatName, field)
      }
    }
    val partitionSchema = fileIndex.partitionSchema
    SchemaUtils.checkSchemaColumnNameDuplication(partitionSchema,
      "in the partition schema", caseSensitive)
    val partitionNameSet: Set[String] =
      partitionSchema.fields.map(PartitioningUtils.getColName(_, 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)
      partitionNameSet.contains(colName)
    } ++ partitionSchema.fields
    StructType(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)
}

相关信息

spark 源码目录

相关文章

spark AddPartitionExec 源码

spark AlterNamespaceSetPropertiesExec 源码

spark AlterTableExec 源码

spark BatchScanExec 源码

spark CacheTableExec 源码

spark ContinuousScanExec 源码

spark CreateIndexExec 源码

spark CreateNamespaceExec 源码

spark CreateTableExec 源码

spark DataSourceRDD 源码