spark StreamingRelation 源码

spark StreamingRelation 代码

文件路径：/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingRelation.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.streaming

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
import org.apache.spark.sql.catalyst.catalog.CatalogTable
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
import org.apache.spark.sql.catalyst.plans.logical.{ExposesMetadataColumns, LeafNode, LogicalPlan, Statistics}
import org.apache.spark.sql.connector.read.streaming.SparkDataStream
import org.apache.spark.sql.errors.QueryExecutionErrors
import org.apache.spark.sql.execution.LeafExecNode
import org.apache.spark.sql.execution.datasources.{DataSource, FileFormat}

object StreamingRelation {
  def apply(dataSource: DataSource): StreamingRelation = {
    StreamingRelation(
      dataSource, dataSource.sourceInfo.name, dataSource.sourceInfo.schema.toAttributes)
  }
}

/**
 * Used to link a streaming [[DataSource]] into a
 * [[org.apache.spark.sql.catalyst.plans.logical.LogicalPlan]]. This is only used for creating
 * a streaming [[org.apache.spark.sql.DataFrame]] from [[org.apache.spark.sql.DataFrameReader]].
 * It should be used to create [[Source]] and converted to [[StreamingExecutionRelation]] when
 * passing to [[StreamExecution]] to run a query.
 */
case class StreamingRelation(dataSource: DataSource, sourceName: String, output: Seq[Attribute])
  extends LeafNode with MultiInstanceRelation with ExposesMetadataColumns {
  override def isStreaming: Boolean = true
  override def toString: String = sourceName

  // There's no sensible value here. On the execution path, this relation will be
  // swapped out with microbatches. But some dataframe operations (in particular explain) do lead
  // to this node surviving analysis. So we satisfy the LeafNode contract with the session default
  // value.
  override def computeStats(): Statistics = Statistics(
    sizeInBytes = BigInt(dataSource.sparkSession.sessionState.conf.defaultSizeInBytes)
  )

  override def newInstance(): LogicalPlan = this.copy(output = output.map(_.newInstance()))

  override lazy val metadataOutput: Seq[AttributeReference] = {
    dataSource.providingClass match {
      // If the dataSource provided class is a same or subclass of FileFormat class
      case f if classOf[FileFormat].isAssignableFrom(f) =>
        val resolve = conf.resolver
        val outputNames = outputSet.map(_.name)
        def isOutputColumn(col: AttributeReference): Boolean = {
          outputNames.exists(name => resolve(col.name, name))
        }
        // filter out the metadata struct column if it has the name conflicting with output columns.
        // if the file has a column "_metadata",
        // then the data column should be returned not the metadata struct column
        Seq(FileFormat.createFileMetadataCol(
          dataSource.providingInstance().asInstanceOf[FileFormat])).filterNot(isOutputColumn)
      case _ => Nil
    }
  }

  override def withMetadataColumns(): LogicalPlan = {
    if (metadataOutput.nonEmpty) {
      this.copy(output = output ++ metadataOutput)
    } else {
      this
    }
  }
}

/**
 * Used to link a streaming [[Source]] of data into a
 * [[org.apache.spark.sql.catalyst.plans.logical.LogicalPlan]].
 */
case class StreamingExecutionRelation(
    source: SparkDataStream,
    output: Seq[Attribute],
    catalogTable: Option[CatalogTable])(session: SparkSession)
  extends LeafNode with MultiInstanceRelation {

  override def otherCopyArgs: Seq[AnyRef] = session :: Nil
  override def isStreaming: Boolean = true
  override def toString: String = source.toString

  // There's no sensible value here. On the execution path, this relation will be
  // swapped out with microbatches. But some dataframe operations (in particular explain) do lead
  // to this node surviving analysis. So we satisfy the LeafNode contract with the session default
  // value.
  override def computeStats(): Statistics = Statistics(
    sizeInBytes = BigInt(session.sessionState.conf.defaultSizeInBytes)
  )

  override def newInstance(): LogicalPlan = this.copy(output = output.map(_.newInstance()))(session)
}

/**
 * A dummy physical plan for [[StreamingRelation]] to support
 * [[org.apache.spark.sql.Dataset.explain]]
 */
case class StreamingRelationExec(
    sourceName: String,
    output: Seq[Attribute],
    tableIdentifier: Option[String]) extends LeafExecNode {
  override def toString: String = sourceName
  override protected def doExecute(): RDD[InternalRow] = {
    throw QueryExecutionErrors.cannotExecuteStreamingRelationExecError()
  }
}

object StreamingExecutionRelation {
  def apply(source: Source, session: SparkSession): StreamingExecutionRelation = {
    StreamingExecutionRelation(source, source.schema.toAttributes, None)(session)
  }

  def apply(
      source: Source,
      session: SparkSession,
      catalogTable: CatalogTable): StreamingExecutionRelation = {
    StreamingExecutionRelation(source, source.schema.toAttributes, Some(catalogTable))(session)
  }
}

相关信息

spark 源码目录

相关文章

spark AvailableNowDataStreamWrapper 源码

spark AvailableNowMicroBatchStreamWrapper 源码

spark AvailableNowSourceWrapper 源码

spark CheckpointFileManager 源码

spark CommitLog 源码

spark CompactibleFileStreamLog 源码

spark ContinuousRecordEndpoint 源码

spark EventTimeWatermarkExec 源码

spark FileStreamOptions 源码

spark FileStreamSink 源码