通过模板类简单实现Spark的JobServer

实验前后效果对比：

之前：执行13个节点，耗时16分钟

之后：同样13个节点，耗时3分钟

具体逻辑请参照代码及注释。

 import java.util.concurrent.{ExecutorService, Executors, TimeUnit}

 import akka.actor.{ActorSystem, Props}
 import com.alibaba.fastjson.JSONObject
 import xxx.listener.AddJobToQueueActor
 import xxx.listener.bean.{AppStatusMessage, SparkContextStatusMessage}
 import xxx.listener.utils.JSONUtil
 import xxx.listener.utils.JmsUtils._
 import xxx.main.SparkJob
 import xxx.main.utils.JsonUtils
 import com.typesafe.config.ConfigFactory
 import org.apache.commons.lang.StringUtils
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.{Logging, SparkConf, SparkContext}

 import scala.collection.mutable.Queue

 /**
   * Created by zpc on 2016/9/1.
   * JobServer实现模板。
   * 修正前：各个任务节点独立提交到spark平台，其中启动sparkContext和hiveContext会占用大量时间，大约40多秒。
   * 修改后：将统一用户，占用资源相同的节点利用JMS发送消息提交到同一个SparkContext上，默认设置为3个节点任务并行。
   * 实现：1.提交到queue中的msg为任务包含任务中型的子类及参数信息，接收到的任务不存在依赖关系，依赖的处理在消息发送端控制。
   *      前置任务执行结束，再发送下一节点任务。
   *      2.第一次提交时，任务的参数在args中获取。启动之后，启动jms的lister监听，通过actor将接收到的任务信息加入队列。
   *      3.通过反射调用SparkJob的各个子类（真正执行节点逻辑的类），SparkContext的默认timeout时间为30mins。
   *      4.节点执行结束，发送节点成功消息到web端，节点失败，发送错误日志及错误消息。
   *      程序退出，通过shutdownhook，发送sc关闭消息到web端。
   *      程序被关闭，如kill时，将等待队列及正在执行集合中的任务，发送失败消息到web端。
   *
   *
   */
 object ExecuteJobServer extends Logging {

   //等待执行的job所在的queue
   val jobWaitingQueue = new Queue[String]
   //当前正在执行的任务的集合
   val jobRunningSet = new scala.collection.mutable.HashSet[JSONObject]
   val timeout_mins = 30
   //最后运行任务时间
   var lastRunTime = System.currentTimeMillis()

   //spark context 对应的 applicationId, user, expId, resource
   var appId : String = ""
   var user: String = ""
   var expId: Long = 0
   var resource: String = ""
   //正在执行的job JSON
   var jobJson : JSONObject = null

   def main(args: Array[String]): Unit = {

     //进程杀死时，将正在执行或未执行的任务，发送失败消息到web端。
     Runtime.getRuntime().addShutdownHook(new HookMessage())
     //接收到的任务，可以同时提交时，线程数可以多设置,暂定为3
     val threadPool: ExecutorService = Executors.newFixedThreadPool(3)
     val sc = initSparkContext()
     val hiveContext = new HiveContext(sc)

     val list = JsonUtils.parseArray(args(0))
     val it = list.iterator
     while (it.hasNext) {
       val jobStr = it.next().toString
       if(expId == 0){
         val json = JSONUtil.toJSONString(jobStr)
         val param = json.getJSONObject("params")
         appId = sc.applicationId
         user = param.getString("user")
         expId = param.getLongValue("expId")
         var driver_memory = ""
         var num_executors = "spark.executor.instances"
         var executor_memory = ""
         sc.getConf.getAll.map( x => {
           if(x._1 != null && "spark.executor.instances".equals(x._1)) {
             num_executors = x._2
           }
           else if(x._1 != null && "spark.executor.memory".equals(x._1)){
             executor_memory = x._2.substring(0, x._2.length - 1)
           }else if(x._1 != null && "spark.driver.memory".equals(x._1)){
             driver_memory = x._2.substring(0, x._2.length - 1)
           }
         })

         resource = driver_memory + num_executors + executor_memory;
         logInfo("resource is : " +resource)
 //        resource = param.getString("driver-memory") + param.getString("num-executors") +  param.getString("executor-memory")
       }
       jobWaitingQueue.enqueue(jobStr)
     }

     /** 1.启动listener监听appId,接收queue中发送过来的JobMsg消息2.通过Queue发送消息通知web端，sc启动 **/
     val system = ActorSystem("mlp", ConfigFactory.load())
     val actor = system.actorOf(Props(new AddJobToQueueActor(appId, jobWaitingQueue)))
     createTopicListenerOfContextJobMsg("contextJobMsgListener", actor)
     informSparkContextStatus(true)

     while (jobWaitingQueue.size > 0 || !checkTimeOut) {
       while (jobWaitingQueue.size > 0) {
         lastRunTime = System.currentTimeMillis()
         val jobStr = jobWaitingQueue.dequeue()//.replace("\\", "")
         logInfo("***** ExecuteJobServer jobStr ***** jobStr: " + jobStr)
         val json = JSONUtil.toJSONString(jobStr)
         jobRunningSet.add(json)
         threadPool.execute(new ThreadSparkJob(json, hiveContext, sc))
         jobJson = json
       }
       Thread.sleep(1000)
     }

     /**
       * jobWaittingQueue队列不再接收消息
       *
       */
     threadPool.shutdown()
     var loop = true
     do {
       //等待所有任务完成
       loop = !threadPool.awaitTermination(2, TimeUnit.SECONDS); //阻塞，直到线程池里所有任务结束
     } while (loop);
   }

   def checkTimeOut(): Boolean = {
     val nowTime = System.currentTimeMillis()
     if (jobRunningSet.isEmpty && (nowTime - lastRunTime) / (1000 * 60) > timeout_mins) {
       return true
     } else {
       return false
     }
   }

   class ThreadSparkJob(json: JSONObject, hiveContext: HiveContext, ctx: SparkContext) extends Runnable {
     override def run() {

       try{
         val classStr = json.get("class").toString
         val argsStr = json.get("params").toString
         val obj: SparkJob = Class.forName(classStr).getMethod("self").invoke(null).asInstanceOf[SparkJob]
 //        val obj: SparkJob = Class.forName(classStr).newInstance().asInstanceOf[SparkJob]
         obj.jobServer = true
         obj.failed = false
         obj.setContext(ctx)
         obj.setHiveContext(hiveContext)
         obj.main(Array(argsStr))
         //      InformJobSuccess(json)
         logInfo("***** jobRunningSet remove job json***** json: " + json.toJSONString )
         jobRunningSet.remove(json)
         lastRunTime = System.currentTimeMillis()
       }catch {
         case oom: OutOfMemoryError => {
           informJobFailure(oom.toString, json)
           jobRunningSet.remove(json)
           logInfo("***** SparkContext go to stop, reaseon: " + oom.getMessage )
           hiveContext.sparkContext.stop()
           //异常时，sc停止，driver程序停止
           System.exit(1)
         }
         case ex: Exception => {
           informJobFailure(ex.toString, json)
           jobRunningSet.remove(json)
           if(ex.toString.contains("stopped SparkContext")){
             logInfo("***** SparkContext go to stop, reaseon: " + ex.getMessage )
             hiveContext.sparkContext.stop()
             //异常时，sc停止，driver程序停止
             System.exit(1)
           }
         }
       }
     }
     def informJobFailure(errMsg: String, json: JSONObject) = {
       if(json != null) {
         val params = json.getJSONObject("params")
         val user = StringUtils.trimToEmpty(params.getString("user"))
         val expId = params.getLongValue("expId")
         val nodeId = params.getLongValue("nodeId")
         val message = new AppStatusMessage(user, expId, nodeId, "FAILURE", errMsg)
         logInfo("***** send informJobFailure message*****： expId: " + expId + "nodeId： " + nodeId)
         jobStatusTemplate send message
       }
     }
   }

   def initSparkContext(): SparkContext = {
     val conf = new SparkConf().setAppName("cbt-mlaas")
     new SparkContext(conf)
   }

   class HookMessage extends Thread {
     override def run() {
       var shouldInformStop = false
       informSparkContextStatus(false)
       while (jobWaitingQueue.size > 0) {
         val jobStr = jobWaitingQueue.dequeue()//.replace("\\", "")
         val json = JSONUtil.toJSONString(jobStr)
         informJobFailureInHook("SparkContext stopped, inform waiting job failed!", json)
         shouldInformStop = true
       }
       jobRunningSet.map(json => {
         informJobFailureInHook("SparkContext stopped, inform running job failed!", json);
         shouldInformStop = true
       })
       if (shouldInformStop) {
         informExpStop("SparkContext stopped, inform exp failed!", jobJson)
       }
     }
     def informJobFailureInHook(errMsg: String, json: JSONObject) = {
       if(json != null) {
         val params = json.getJSONObject("params")
         val user = StringUtils.trimToEmpty(params.getString("user"))
         val expId = params.getLongValue("expId")
         val nodeId = params.getLongValue("nodeId")
         val message = new AppStatusMessage(user, expId, nodeId, "FAILURE", errMsg)
         logInfo("***** send informJobFailure message*****： expId: " + expId + "nodeId： " + nodeId)
         jobStatusTemplate send message
       }
     }
     def informExpStop(errMsg: String, json: JSONObject) = {
       if(json != null) {
         val params = json.getJSONObject("params")
         val user = StringUtils.trimToEmpty(params.getString("user"))
         val expId = params.getLongValue("expId")
         val nodeId = params.getLongValue("nodeId")
         val message = new AppStatusMessage(user, expId, nodeId, "STOP", errMsg)
         logInfo("***** send informExpStop message*****： expId: " + expId + "nodeId： " + nodeId)
         jobStatusTemplate send message
       }
     }
   }

   def informSparkContextStatus(start : Boolean) = {
    val msg = new  SparkContextStatusMessage(appId, start, user, expId, resource)
     logInfo("***** send sparkContext start message*****： appId: " + appId + "start： " + start)
     sparkContextStatusTemplate send msg
   }

 }