2.1.5、SparkEnv中创建MapOutputTracker

SparkEnv中创建MapOutputTracker

    def registerOrLookupEndpoint(
        name: String, endpointCreator: => RpcEndpoint):
      RpcEndpointRef = {
      if (isDriver) {
        logInfo("Registering " + name)
        rpcEnv.setupEndpoint(name, endpointCreator)
      } else {
        RpcUtils.makeDriverRef(name, conf, rpcEnv)
      }
    }

    val broadcastManager = new BroadcastManager(isDriver, conf, securityManager)

    //创建MapOutputTracker 区分Driver, Executor
    val mapOutputTracker = if (isDriver) {
      //Driver需要BroadcastManager
      new MapOutputTrackerMaster(conf, broadcastManager, isLocal)
    } else {
      new MapOutputTrackerWorker(conf)
    }

    // Have to assign trackerEndpoint after initialization as MapOutputTrackerEndpoint
    // requires the MapOutputTracker itself
    mapOutputTracker.trackerEndpoint = registerOrLookupEndpoint(MapOutputTracker.ENDPOINT_NAME,
      new MapOutputTrackerMasterEndpoint(
        rpcEnv, mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], conf))

MapOutputTracker是用于跟踪map阶段任务的输出状态， 此状态便于reduce阶段获取地址及中间输出结果，每个map或reduce都有唯一的标识（mapId, reduceId），

MapOutputTracker是基于Master/Slave的架构，Master（Driver）负责存储当前Application上所有Shuffle的Map输出元数据信息，而Slave（Executor）可以通过rpc对Master上的Map输出状态信息进行查询。

区分Driver, executor的MapOutputTracker

    //创建MapOutputTracker 区分Driver, Executor
    val mapOutputTracker = if (isDriver) {
      //Driver需要BroadcastManager
      new MapOutputTrackerMaster(conf, broadcastManager, isLocal)
    } else {
      new MapOutputTrackerWorker(conf)
    }

两者都实现了MapOutputTracker

/**
 * Class that keeps track of the location of the map output of
 * a stage. This is abstract because different versions of MapOutputTracker
 * (driver and executor) use different HashMap to store its metadata.
 */
private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging