org.apache.spark.deploy.yarn.Client.scala中的monitorApplication方法：

/**

   * Report the state of an application until it has exited, either successfully or

   * due to some failure, then return a pair of the yarn application state (FINISHED, FAILED,

   * KILLED, or RUNNING) and the final application state (UNDEFINED, SUCCEEDED, FAILED,

   * or KILLED).

   *

   * @param appId ID of the application to monitor.

   * @param returnOnRunning Whether to also return the application state when it is RUNNING.

   * @param logApplicationReport Whether to log details of the application report every iteration.

   * @return A pair of the yarn application state and the final application state.

   
 
*/

  def monitorApplication(

      appId: ApplicationId,

      returnOnRunning: Boolean = false,

      logApplicationReport: Boolean = true): (YarnApplicationState, FinalApplicationStatus) = {

    val interval = sparkConf.getLong("spark.yarn.report.interval", 1000)

    var lastState: YarnApplicationState = null
 


    while (true) {

      Thread.sleep(interval)

      val report: ApplicationReport =

        try {

          getApplicationReport(appId)

        } catch {

          case e: ApplicationNotFoundException =>

            logError(s"Application $appId not found.")

            return (YarnApplicationState.KILLED, FinalApplicationStatus.KILLED)

          
 
case NonFatal(e) =>

            logError(s"Failed to contact YARN for application $appId.", e)

            return (YarnApplicationState.FAILED, FinalApplicationStatus.FAILED)

        }

      val state = report.getYarnApplicationState

      if (logApplicationReport) {

        logInfo(s"Application report for $appId (state: $state)")

        // If DEBUG is enabled, log report details every iteration

        // Otherwise, log them every time the application changes state

        if (log.isDebugEnabled) {

          logDebug(formatReportDetails(report))

        } else if (lastState != state) {

          logInfo(formatReportDetails(report))

        }

      }

      if (lastState != state) {

        state match {

          case YarnApplicationState.RUNNING =>

            reportLauncherState(SparkAppHandle.State.RUNNING)

          case YarnApplicationState.FINISHED =>

//            reportLauncherState(SparkAppHandle.State.FINISHED)

            report.getFinalApplicationStatus match {

              case FinalApplicationStatus.FAILED =>

                reportLauncherState(SparkAppHandle.State.FAILED)

              case FinalApplicationStatus.KILLED =>

                reportLauncherState(SparkAppHandle.State.KILLED)

              case _ =>

                reportLauncherState(SparkAppHandle.State.FINISHED)

            }

          case YarnApplicationState.FAILED =>

            reportLauncherState(SparkAppHandle.State.FAILED)

          case YarnApplicationState.KILLED =>

            reportLauncherState(SparkAppHandle.State.KILLED)

          case _ =>

        }

      }

      if (state == YarnApplicationState.FINISHED ||

        state == YarnApplicationState.FAILED ||

        state == YarnApplicationState.KILLED) {

        cleanupStagingDir(appId)

        return (state, report.getFinalApplicationStatus)

      }

      if (returnOnRunning && state == YarnApplicationState.RUNNING) {

        return (state, report.getFinalApplicationStatus)

      }

      lastState = state

    }

    // Never reached, but keeps compiler happy

    throw new SparkException("While loop is depleted! This should never happen...")

  }

其中：

      if (lastState != state) {

        state match {

          case YarnApplicationState.RUNNING =>

            reportLauncherState(SparkAppHandle.State.RUNNING)

          case YarnApplicationState.FINISHED =>

//            reportLauncherState(SparkAppHandle.State.FINISHED)

            report.getFinalApplicationStatus match {

              case FinalApplicationStatus.FAILED =>

                reportLauncherState(SparkAppHandle.State.FAILED)

              case FinalApplicationStatus.KILLED =>

                reportLauncherState(SparkAppHandle.State.KILLED)

              case _ =>

                reportLauncherState(SparkAppHandle.State.FINISHED)

            }

          case YarnApplicationState.FAILED =>

            reportLauncherState(SparkAppHandle.State.FAILED)

          case YarnApplicationState.KILLED =>

            reportLauncherState(SparkAppHandle.State.KILLED)

          case _ =>

        }

      }

yarn state為finished的時候的狀態細分不夠明確，將原來的 reportLauncherState(SparkAppHandle.State.FAILED)註釋掉，改成：

report.getFinalApplicationStatus match {

              case FinalApplicationStatus.FAILED =>

                reportLauncherState(SparkAppHandle.State.FAILED)

              case FinalApplicationStatus.KILLED =>

                reportLauncherState(SparkAppHandle.State.KILLED)

              case _ =>

                reportLauncherState(SparkAppHandle.State.FINISHED)

            }

因為完成狀態的final state可能很多種狀態，KILLED、FAILED、SUCCESS都可能是final state。
如果只返回一個finished狀態給SparkLauncher的SparkAppHandle的話，其實我們在自己的程式碼中是無法知道這個spark 任務到底是成功了還是失敗了，只知道它完成了。
所以要細分一下完成狀態，自己用SparkLauncher提交JOB的時候可以監控JOB在失敗的時候報警。
此BUG在spark1.6.0中存在對應CDH5.7到CDH5.9的spark都有這個問題，新的版本中已經修復此BUG。
如果在使用CDH版本的spark,那麼就自己改一下程式碼重新編譯打包一下，部署一個自己的spark on yarn服務吧。