Multiple spark jobs appending parquet data to same base path with partitioning

Question

I have multiple jobs that I want to execute in parallel that append daily data into the same path using partitioning.

e.g.

dataFrame.write().
             


        

Answer 1

Multiple write tasks for same path with "partitionBy", will FAILED when _temporary been delete in cleanupJob of FileOutputCommitter , like No such file or directory .

TEST CODE:

def batchTask[A](TASK_tag: String, taskData: TraversableOnce[A], batchSize: Int, fTask: A => Unit, fTaskId: A => String): Unit = {
  var list = new scala.collection.mutable.ArrayBuffer[(String, java.util.concurrent.Future[Int])]()
  val executors = java.util.concurrent.Executors.newFixedThreadPool(batchSize)
  try {
    taskData.foreach(d => {
      val task = executors.submit(new java.util.concurrent.Callable[Int] {
        override def call(): Int = {
          fTask(d)
          1
        }
      })
      list += ((fTaskId(d), task))
    })
    var count = 0
    list.foreach(r => if (!r._2.isCancelled) count += r._2.get())
  } finally {
    executors.shutdown()
  }
}
def testWriteFail(outPath: String)(implicit spark: SparkSession, sc: SparkContext): Unit = {
  println(s"try save: ${outPath}")
  import org.apache.spark.sql.functions._
  import spark.sqlContext.implicits._
  batchTask[Int]("test", 1 to 20, 6, t => {
    val df1 =
      Seq((1, "First Value", java.sql.Date.valueOf("2010-01-01")), (2, "Second Value", java.sql.Date.valueOf("2010-02-01")))
        .toDF("int_column", "string_column", "date_column")
        .withColumn("t0", lit(t))
    df1.repartition(1).write
      .mode("overwrite")
      .option("mapreduce.fileoutputcommitter.marksuccessfuljobs", false)
      .partitionBy("t0").csv(outPath)
  }, t => f"task.${t}%4d") // some Exception
  println(s"fail: count=${spark.read.csv(outPath).count()}")
}
try {
  testWriteFail(outPath + "/fail")
} catch {
  case e: Throwable =>
}

Failed

Use OutputCommitter :

package org.jar.spark.util
import java.io.IOException
/*
  * 用于 DataFrame 多任务写入同一个目录。
  * 
  * 1. 基于临时目录写入
  * 2. 如果【任务的输出】可能会有重叠，不要使用 overwrite 方式，以免误删除
  * 
  * 

  * Created by liao on 2018-12-02.
  */
object JMultiWrite {
  val JAR_Write_Cache_Flag = "jar.write.cache.flag"
  val JAR_Write_Cache_TaskId = "jar.write.cache.taskId"
  /** 自动删除目标目录下同名子目录 */
  val JAR_Write_Cache_Overwrite = "jar.write.cache.overwrite"
  implicit class ImplicitWrite[T](dw: org.apache.spark.sql.DataFrameWriter[T]) {
    /**
      * 输出到文件，需要在外面配置 option format mode 等
      *
      * @param outDir    输出目标目录
      * @param taskId    此次任务ID，用于隔离各任务的输出，必须具有唯一性
      * @param cacheDir  缓存目录，最好是 '_' 开头的目录，如 "_jarTaskCache"
      * @param overwrite 是否删除已经存在的目录，默认 false 表示 Append模式
      *                  （如果 并行任务可能有相同 子目录输出时，会冲掉，此时不要使用 overwrite）
      */
    def multiWrite(outDir: String, taskId: String, cacheDir: String = "_jarTaskCache", overwrite: Boolean = false): Boolean = {
      val p = path(outDir, cacheDir, taskId)
      dw.options(options(cacheDir, taskId))
        .option(JAR_Write_Cache_Overwrite, overwrite)
        .mode(org.apache.spark.sql.SaveMode.Overwrite)
        .save(p)
      true
    }
  }
  def options(cacheDir: String, taskId: String): Map[String, String] = {
    Map(JAR_Write_Cache_Flag -> cacheDir,
      JAR_Write_Cache_TaskId -> taskId,
      "mapreduce.fileoutputcommitter.marksuccessfuljobs" -> "false",
      "mapreduce.job.outputformat.class" -> classOf[JarOutputFormat].getName
    )
  }
  def path(outDir: String, cacheDir: String, taskId: String): String = {
    assert(outDir != "", "need OutDir")
    assert(cacheDir != "", "need CacheDir")
    assert(taskId != "", "needTaskId")
    outDir + "/" + cacheDir + "/" + taskId
  }
  /*-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-*/
  class JarOutputFormat extends org.apache.hadoop.mapreduce.lib.output.TextOutputFormat {
    var committer: org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter = _

    override def getOutputCommitter(context: org.apache.hadoop.mapreduce.TaskAttemptContext): org.apache.hadoop.mapreduce.OutputCommitter = {
      if (this.committer == null) {
        val output = org.apache.hadoop.mapreduce.lib.output.FileOutputFormat.getOutputPath(context)
        this.committer = new JarOutputCommitter(output, context)
      }
      this.committer
    }
  }
  class JarOutputCommitter(output: org.apache.hadoop.fs.Path, context: org.apache.hadoop.mapreduce.TaskAttemptContext)
    extends org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter(output, context) {
    override def commitJob(context: org.apache.hadoop.mapreduce.JobContext): Unit = {
      val finalOutput = this.output
      val cacheFlag = context.getConfiguration.get(JAR_Write_Cache_Flag, "")
      val myTaskId = context.getConfiguration.get(JAR_Write_Cache_TaskId, "")
      val overwrite = context.getConfiguration.getBoolean(JAR_Write_Cache_Overwrite, false)
      val hasCacheFlag = finalOutput.getName == myTaskId && finalOutput.getParent.getName == cacheFlag
      val finalReal = if (hasCacheFlag) finalOutput.getParent.getParent else finalOutput // 确定最终目录
      // 遍历输出目录
      val fs = finalOutput.getFileSystem(context.getConfiguration)
      val jobAttemptPath = getJobAttemptPath(context)
      val arr$ = fs.listStatus(jobAttemptPath, new org.apache.hadoop.fs.PathFilter {
        override def accept(path: org.apache.hadoop.fs.Path): Boolean = !"_temporary".equals(path.getName())
      })
      if (hasCacheFlag && overwrite) // 移除同名子目录
      {
        if (fs.isDirectory(finalReal)) arr$.foreach(stat =>
          if (fs.isDirectory(stat.getPath)) fs.listStatus(stat.getPath).foreach(stat2 => {
            val p1 = stat2.getPath
            val p2 = new org.apache.hadoop.fs.Path(finalReal, p1.getName)
            if (fs.isDirectory(p1) && fs.isDirectory(p2) && !fs.delete(p2, true)) throw new IOException("Failed to delete " + p2)
          })
        )
      }
      arr$.foreach(stat => {
        mergePaths(fs, stat, finalReal)
      })
      cleanupJob(context)
      if (hasCacheFlag) { // 移除缓存目录
        try {
          fs.delete(finalOutput, false)
          val pp = finalOutput.getParent
          if (fs.listStatus(pp).isEmpty)
            fs.delete(pp, false)
        } catch {
          case e: Exception =>
        }
      }
      // 不用输出 _SUCCESS 了
      //if (context.getConfiguration.getBoolean("mapreduce.fileoutputcommitter.marksuccessfuljobs", true)) {
      //  val markerPath = new org.apache.hadoop.fs.Path(this.outputPath, "_SUCCESS")
      //  fs.create(markerPath).close()
      //}
    }
  }
  @throws[IOException]
  def mergePaths(fs: org.apache.hadoop.fs.FileSystem, from: org.apache.hadoop.fs.FileStatus, to: org.apache.hadoop.fs.Path): Unit = {
    if (from.isFile) {
      if (fs.exists(to) && !fs.delete(to, true)) throw new IOException("Failed to delete " + to)
      if (!fs.rename(from.getPath, to)) throw new IOException("Failed to rename " + from + " to " + to)
    }
    else if (from.isDirectory) if (fs.exists(to)) {
      val toStat = fs.getFileStatus(to)
      if (!toStat.isDirectory) {
        if (!fs.delete(to, true)) throw new IOException("Failed to delete " + to)
        if (!fs.rename(from.getPath, to)) throw new IOException("Failed to rename " + from + " to " + to)
      }
      else {
        val arr$ = fs.listStatus(from.getPath)
        for (subFrom <- arr$) {
          mergePaths(fs, subFrom, new org.apache.hadoop.fs.Path(to, subFrom.getPath.getName))
        }
      }
    }
    else if (!fs.rename(from.getPath, to)) throw new IOException("Failed to rename " + from + " to " + to)
  }
}

And then:

def testWriteOk(outPath: String)(implicit spark: SparkSession, sc: SparkContext): Unit = {
  println(s"try save: ${outPath}")
  import org.apache.spark.sql.functions._
  import org.jar.spark.util.JMultiWrite.ImplicitWrite // 导入工具
  import spark.sqlContext.implicits._
  batchTask[Int]("test.ok", 1 to 20, 6, t => {
    val taskId = t.toString
    val df1 =
      Seq((1, "First Value", java.sql.Date.valueOf("2010-01-01")), (2, "Second Value", java.sql.Date.valueOf("2010-02-01")))
        .toDF("int_column", "string_column", "date_column")
        .withColumn("t0", lit(taskId))
    df1.repartition(1).write
      .partitionBy("t0")
      .format("csv")
      .multiWrite(outPath, taskId, overwrite = true) // 这里使用了 overwrite ，如果分区有重叠，请不要使用 overwrite
  }, t => f"task.${t}%4d")
  println(s"ok: count=${spark.read.csv(outPath).count()}") // 40
}
try {
  testWriteOk(outPath + "/ok")
} catch {
  case e: Throwable =>
}

Success:

$  ls ok/
t0=1  t0=10 t0=11 t0=12 t0=13 t0=14 t0=15 t0=16 t0=17 t0=18 t0=19 t0=2  t0=20 t0=3  t0=4  t0=5  t0=6  t0=7  t0=8  t0=9

The same applies to other output formats, pay attention to the use of overwrite .

Test on spark 2.11.8.

Thanks for @Tal Joffe