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 * 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
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * See the License for the specific language governing permissions and
 * limitations under the License.


import java.util.{Date, UUID}

import scala.collection.mutable
import scala.util.Try

import org.apache.hadoop.conf.Configurable
import org.apache.hadoop.fs.Path
import org.apache.hadoop.mapreduce._
import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl

import org.apache.spark.internal.Logging
import org.apache.spark.mapred.SparkHadoopMapRedUtil

 * An [[FileCommitProtocol]] implementation backed by an underlying Hadoop OutputCommitter
 * (from the newer mapreduce API, not the old mapred API).
 * Unlike Hadoop's OutputCommitter, this implementation is serializable.
 * @param jobId the job's or stage's id
 * @param path the job's output path, or null if committer acts as a noop
 * @param dynamicPartitionOverwrite If true, Spark will overwrite partition directories at runtime
 *                                  dynamically, i.e., we first write files under a staging
 *                                  directory with partition path, e.g.
 *                                  /path/to/staging/a=1/b=1/xxx.parquet. When committing the job,
 *                                  we first clean up the corresponding partition directories at
 *                                  destination path, e.g. /path/to/destination/a=1/b=1, and move
 *                                  files from staging directory to the corresponding partition
 *                                  directories under destination path.
class HadoopMapReduceCommitProtocol(
    jobId: String,
    path: String,
    dynamicPartitionOverwrite: Boolean = false)
  extends FileCommitProtocol with Serializable with Logging {

  import FileCommitProtocol._

  /** OutputCommitter from Hadoop is not serializable so marking it transient. */
  @transient private var committer: OutputCommitter = _

   * Checks whether there are files to be committed to a valid output location.
   * As committing and aborting a job occurs on driver, where `addedAbsPathFiles` is always null,
   * it is necessary to check whether a valid output path is specified.
   * [[HadoopMapReduceCommitProtocol#path]] need not be a valid [[org.apache.hadoop.fs.Path]] for
   * committers not writing to distributed file systems.
  private val hasValidPath = Try { new Path(path) }.isSuccess

   * Tracks files staged by this task for absolute output paths. These outputs are not managed by
   * the Hadoop OutputCommitter, so we must move these to their final locations on job commit.
   * The mapping is from the temp output path to the final desired output path of the file.
  @transient private var addedAbsPathFiles: mutable.Map[String, String] = null

   * Tracks partitions with default path that have new files written into them by this task,
   * e.g. a=1/b=2. Files under these partitions will be saved into staging directory and moved to
   * destination directory at the end, if `dynamicPartitionOverwrite` is true.
  @transient private var partitionPaths: mutable.Set[String] = null

   * The staging directory of this write job. Spark uses it to deal with files with absolute output
   * path, or writing data into partitioned directory with dynamicPartitionOverwrite=true.
  private def stagingDir = new Path(path, ".spark-staging-" + jobId)

  protected def setupCommitter(context: TaskAttemptContext): OutputCommitter = {
    val format = context.getOutputFormatClass.newInstance()
    // If OutputFormat is Configurable, we should set conf to it.
    format match {
      case c: Configurable => c.setConf(context.getConfiguration)
      case _ => ()

  override def newTaskTempFile(
      taskContext: TaskAttemptContext, dir: Option[String], ext: String): String = {
    val filename = getFilename(taskContext, ext)

    val stagingDir: Path = committer match {
      case _ if dynamicPartitionOverwrite =>
          "The dataset to be written must be partitioned when dynamicPartitionOverwrite is true.")
        partitionPaths += dir.get
      // For FileOutputCommitter it has its own staging path called "work path".
      case f: FileOutputCommitter =>
        new Path(Option(f.getWorkPath).map(_.toString).getOrElse(path))
      case _ => new Path(path)
    } { d =>
      new Path(new Path(stagingDir, d), filename).toString
    }.getOrElse {
      new Path(stagingDir, filename).toString

  override def newTaskTempFileAbsPath(
      taskContext: TaskAttemptContext, absoluteDir: String, ext: String): String = {
    val filename = getFilename(taskContext, ext)
    val absOutputPath = new Path(absoluteDir, filename).toString

    // Include a UUID here to prevent file collisions for one task writing to different dirs.
    // In principle we could include hash(absoluteDir) instead but this is simpler.
    val tmpOutputPath = new Path(stagingDir, UUID.randomUUID().toString() + "-" + filename).toString

    addedAbsPathFiles(tmpOutputPath) = absOutputPath

  private def getFilename(taskContext: TaskAttemptContext, ext: String): String = {
    // The file name looks like part-00000-2dd664f9-d2c4-4ffe-878f-c6c70c1fb0cb_00003-c000.parquet
    // Note that %05d does not truncate the split number, so if we have more than 100000 tasks,
    // the file name is fine and won't overflow.
    val split = taskContext.getTaskAttemptID.getTaskID.getId

  override def setupJob(jobContext: JobContext): Unit = {
    // Setup IDs
    val jobId = SparkHadoopWriterUtils.createJobID(new Date, 0)
    val taskId = new TaskID(jobId, TaskType.MAP, 0)
    val taskAttemptId = new TaskAttemptID(taskId, 0)

    // Set up the configuration object
    jobContext.getConfiguration.set("", jobId.toString)
    jobContext.getConfiguration.set("", taskAttemptId.getTaskID.toString)
    jobContext.getConfiguration.set("", taskAttemptId.toString)
    jobContext.getConfiguration.setBoolean("mapreduce.task.ismap", true)
    jobContext.getConfiguration.setInt("mapreduce.task.partition", 0)

    val taskAttemptContext = new TaskAttemptContextImpl(jobContext.getConfiguration, taskAttemptId)
    committer = setupCommitter(taskAttemptContext)

  override def commitJob(jobContext: JobContext, taskCommits: Seq[TaskCommitMessage]): Unit = {

    if (hasValidPath) {
      val (allAbsPathFiles, allPartitionPaths) =[(Map[String, String], Set[String])]).unzip
      val fs = stagingDir.getFileSystem(jobContext.getConfiguration)

      val filesToMove = allAbsPathFiles.foldLeft(Map[String, String]())(_ ++ _)
      logDebug(s"Committing files staged for absolute locations $filesToMove")
      if (dynamicPartitionOverwrite) {
        val absPartitionPaths = Path(_).getParent).toSet
        logDebug(s"Clean up absolute partition directories for overwriting: $absPartitionPaths")
        absPartitionPaths.foreach(fs.delete(_, true))
      for ((src, dst) <- filesToMove) {
        fs.rename(new Path(src), new Path(dst))

      if (dynamicPartitionOverwrite) {
        val partitionPaths = allPartitionPaths.foldLeft(Set[String]())(_ ++ _)
        logDebug(s"Clean up default partition directories for overwriting: $partitionPaths")
        for (part <- partitionPaths) {
          val finalPartPath = new Path(path, part)
          if (!fs.delete(finalPartPath, true) && !fs.exists(finalPartPath.getParent)) {
            // According to the official hadoop FileSystem API spec, delete op should assume
            // the destination is no longer present regardless of return value, thus we do not
            // need to double check if finalPartPath exists before rename.
            // Also in our case, based on the spec, delete returns false only when finalPartPath
            // does not exist. When this happens, we need to take action if parent of finalPartPath
            // also does not exist(e.g. the scenario described on SPARK-23815), because
            // FileSystem API spec on rename op says the rename dest(finalPartPath) must have
            // a parent that exists, otherwise we may get unexpected result on the rename.
          fs.rename(new Path(stagingDir, part), finalPartPath)

      fs.delete(stagingDir, true)

  override def abortJob(jobContext: JobContext): Unit = {
    committer.abortJob(jobContext, JobStatus.State.FAILED)
    if (hasValidPath) {
      val fs = stagingDir.getFileSystem(jobContext.getConfiguration)
      fs.delete(stagingDir, true)

  override def setupTask(taskContext: TaskAttemptContext): Unit = {
    committer = setupCommitter(taskContext)
    addedAbsPathFiles = mutable.Map[String, String]()
    partitionPaths = mutable.Set[String]()

  override def commitTask(taskContext: TaskAttemptContext): TaskCommitMessage = {
    val attemptId = taskContext.getTaskAttemptID
      committer, taskContext, attemptId.getJobID.getId, attemptId.getTaskID.getId)
    new TaskCommitMessage(addedAbsPathFiles.toMap -> partitionPaths.toSet)

  override def abortTask(taskContext: TaskAttemptContext): Unit = {
    // best effort cleanup of other staged files
    for ((src, _) <- addedAbsPathFiles) {
      val tmp = new Path(src)
      tmp.getFileSystem(taskContext.getConfiguration).delete(tmp, false)

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