利用Spark监听listener来监控任务完成进度

一、背景

当时在做数据湖的项目，需要使用Spark SQL做数据ETL，即并发地将全表数据从RDBMS经过数据转换等导入到HDFS中。由于Web UI上需要显示ETL的进度，因此需要能够指导当前导了多少个row。但是由于是多个executor并发地读取数据，而如何获取每个executor导了多少个row就是一个问题了，Spark SQL本身并没有提供这样的API。本文将介绍如何使用Spark监听listener来预估任务完成的进度。

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二、实现方法

• 首先，自定义一个监听类，并继承SparkListener并override方法；
• 实例化该监听类得到监听器对象，sparkcontex添加该监听器对象即可。

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三、业务代码示例

import org.apache.spark.scheduler._
import org.slf4j.LoggerFactory

/*
* This class is used to listen the progress of submitted spark job
* The number of completed tasks will be counted
* In this way, the rough progress of submitted spark job can be estimated
* */
class MySparkListener(instanceName:String,schemaName:String,tableName:String,ceilNum:Long,rowCount:Long,parallelismNum:Int,partitionNum:Int) extends SparkListener{

  val logger = LoggerFactory.getLogger(classOf[MySparkListener])
  var taskCount: Int = 0
  override def onApplicationStart(applicationStart: SparkListenerApplicationStart): Unit = {
    super.onApplicationStart(applicationStart)
    logger.info("\n\n\n>>>>>> Spark application started")
  }

  override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit = {
    super.onApplicationEnd(applicationEnd)
    logger.info("\n\n\n>>>>>> Spark application ended")
  }

  override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = {
    super.onJobEnd(jobEnd)
  }

  override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = {
    super.onStageCompleted(stageCompleted)
  }

  override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
    super.onTaskEnd(taskEnd)
    taskCount = taskCount + 1
    if(taskCount <= parallelismNum + 1){
      val sparkJobProgress = Math.floor(rowCount*(0.1+taskCount*0.76/(parallelismNum+1))).toLong
      if(sparkJobProgress <= rowCount){
          // 处理逻辑，更新进度......
      }
     }
   }
}



object Main {
  def main(args: Array[String]): Unit = {
      val sparkSession = SparkSession.builder().master("yarn").appName("Datalake")getOrCreate()
      val sc = sparkSession.sparkContext
       logger.info(">>>>>> start spark listener")
       val sparkListener = new      MySparkListener(instanceName,schemaName,tableName,ceilNum,rowCount,parallelismNum,partitionNum)
       sc.addSparkListener(sparkListener)

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如有错误，敬请指正！