spark FileWrite 源码

  • 2022-10-20
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spark FileWrite 代码


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 * this work for additional information regarding copyright ownership.
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package org.apache.spark.sql.execution.datasources.v2

import java.util.UUID

import scala.collection.JavaConverters._

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.Path
import org.apache.hadoop.mapreduce.Job
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils}
import org.apache.spark.sql.connector.write.{BatchWrite, LogicalWriteInfo, Write}
import org.apache.spark.sql.errors.QueryCompilationErrors
import org.apache.spark.sql.execution.datasources.{BasicWriteJobStatsTracker, DataSource, OutputWriterFactory, WriteJobDescription}
import org.apache.spark.sql.execution.metric.SQLMetric
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.{DataType, StructType}
import org.apache.spark.sql.util.SchemaUtils
import org.apache.spark.util.SerializableConfiguration

trait FileWrite extends Write {
  def paths: Seq[String]
  def formatName: String
  def supportsDataType: DataType => Boolean
  def info: LogicalWriteInfo

  private val schema = info.schema()
  private val queryId = info.queryId()
  private val options = info.options()

  override def description(): String = formatName

  override def toBatch: BatchWrite = {
    val sparkSession =
    val path = new Path(paths.head)
    val caseSensitiveMap = options.asCaseSensitiveMap.asScala.toMap
    // Hadoop Configurations are case sensitive.
    val hadoopConf = sparkSession.sessionState.newHadoopConfWithOptions(caseSensitiveMap)
    val job = getJobInstance(hadoopConf, path)
    val committer = FileCommitProtocol.instantiate(
      jobId = java.util.UUID.randomUUID().toString,
      outputPath = paths.head)
    lazy val description =
      createWriteJobDescription(sparkSession, hadoopConf, job, paths.head, options.asScala.toMap)

    new FileBatchWrite(job, description, committer)

   * Prepares a write job and returns an [[OutputWriterFactory]].  Client side job preparation can
   * be put here.  For example, user defined output committer can be configured here
   * by setting the output committer class in the conf of spark.sql.sources.outputCommitterClass.
  def prepareWrite(
      sqlConf: SQLConf,
      job: Job,
      options: Map[String, String],
      dataSchema: StructType): OutputWriterFactory

  private def validateInputs(caseSensitiveAnalysis: Boolean): Unit = {
    assert(schema != null, "Missing input data schema")
    assert(queryId != null, "Missing query ID")

    if (paths.length != 1) {
      throw new IllegalArgumentException("Expected exactly one path to be specified, but " +
        s"got: ${paths.mkString(", ")}")
    val pathName = paths.head
      s"when inserting into $pathName", caseSensitiveAnalysis)

    // TODO: [SPARK-36340] Unify check schema filed of DataSource V2 Insert.
    schema.foreach { field =>
      if (!supportsDataType(field.dataType)) {
        throw QueryCompilationErrors.dataTypeUnsupportedByDataSourceError(formatName, field)

  private def getJobInstance(hadoopConf: Configuration, path: Path): Job = {
    val job = Job.getInstance(hadoopConf)
    FileOutputFormat.setOutputPath(job, path)

  private def createWriteJobDescription(
      sparkSession: SparkSession,
      hadoopConf: Configuration,
      job: Job,
      pathName: String,
      options: Map[String, String]): WriteJobDescription = {
    val caseInsensitiveOptions = CaseInsensitiveMap(options)
    // Note: prepareWrite has side effect. It sets "job".
    val outputWriterFactory =
      prepareWrite(sparkSession.sessionState.conf, job, caseInsensitiveOptions, schema)
    val allColumns = schema.toAttributes
    val metrics: Map[String, SQLMetric] = BasicWriteJobStatsTracker.metrics
    val serializableHadoopConf = new SerializableConfiguration(hadoopConf)
    val statsTracker = new BasicWriteJobStatsTracker(serializableHadoopConf, metrics)
    // TODO: after partitioning is supported in V2:
    //       1. filter out partition columns in `dataColumns`.
    //       2. Don't use Seq.empty for `partitionColumns`.
    new WriteJobDescription(
      uuid = UUID.randomUUID().toString,
      serializableHadoopConf = new SerializableConfiguration(job.getConfiguration),
      outputWriterFactory = outputWriterFactory,
      allColumns = allColumns,
      dataColumns = allColumns,
      partitionColumns = Seq.empty,
      bucketSpec = None,
      path = pathName,
      customPartitionLocations = Map.empty,
      maxRecordsPerFile = caseInsensitiveOptions.get("maxRecordsPerFile").map(_.toLong)
      timeZoneId = caseInsensitiveOptions.get(DateTimeUtils.TIMEZONE_OPTION)
      statsTrackers = Seq(statsTracker)


spark 源码目录


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spark BatchScanExec 源码

spark CacheTableExec 源码

spark ContinuousScanExec 源码

spark CreateIndexExec 源码

spark CreateNamespaceExec 源码

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