org.apache.spark.rpc.ThreadSafeRpcEndpoint Scala Examples

package org.apache.spark.sql.execution.streaming.state

import scala.collection.mutable

import org.apache.spark.SparkEnv
import org.apache.spark.internal.Logging
import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.scheduler.ExecutorCacheTaskLocation
import org.apache.spark.util.RpcUtils


private class StateStoreCoordinator(override val rpcEnv: RpcEnv)
    extends ThreadSafeRpcEndpoint with Logging {
  private val instances = new mutable.HashMap[StateStoreId, ExecutorCacheTaskLocation]

  override def receive: PartialFunction[Any, Unit] = {
    case ReportActiveInstance(id, host, executorId) =>
      logDebug(s"Reported state store $id is active at $executorId")
      instances.put(id, ExecutorCacheTaskLocation(host, executorId))
  }

  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case VerifyIfInstanceActive(id, execId) =>
      val response = instances.get(id) match {
        case Some(location) => location.executorId == execId
        case None => false
      }
      logDebug(s"Verified that state store $id is active: $response")
      context.reply(response)

    case GetLocation(id) =>
      val executorId = instances.get(id).map(_.toString)
      logDebug(s"Got location of the state store $id: $executorId")
      context.reply(executorId)

    case DeactivateInstances(checkpointLocation) =>
      val storeIdsToRemove =
        instances.keys.filter(_.checkpointLocation == checkpointLocation).toSeq
      instances --= storeIdsToRemove
      logDebug(s"Deactivating instances related to checkpoint location $checkpointLocation: " +
        storeIdsToRemove.mkString(", "))
      context.reply(true)

    case StopCoordinator =>
      stop() // Stop before replying to ensure that endpoint name has been deregistered
      logInfo("StateStoreCoordinator stopped")
      context.reply(true)
  }
} 

package org.apache.spark.scheduler.local

import java.io.File
import java.net.URL
import java.nio.ByteBuffer

import org.apache.spark.{SparkConf, SparkContext, SparkEnv, TaskState}
import org.apache.spark.TaskState.TaskState
import org.apache.spark.executor.{Executor, ExecutorBackend}
import org.apache.spark.internal.Logging
import org.apache.spark.launcher.{LauncherBackend, SparkAppHandle}
import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.scheduler._
import org.apache.spark.scheduler.cluster.ExecutorInfo

private case class ReviveOffers()

private case class StatusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer)

private case class KillTask(taskId: Long, interruptThread: Boolean)

private case class StopExecutor()


  def getUserClasspath(conf: SparkConf): Seq[URL] = {
    val userClassPathStr = conf.getOption("spark.executor.extraClassPath")
    userClassPathStr.map(_.split(File.pathSeparator)).toSeq.flatten.map(new File(_).toURI.toURL)
  }

  launcherBackend.connect()

  override def start() {
    val rpcEnv = SparkEnv.get.rpcEnv
    val executorEndpoint = new LocalEndpoint(rpcEnv, userClassPath, scheduler, this, totalCores)
    localEndpoint = rpcEnv.setupEndpoint("LocalSchedulerBackendEndpoint", executorEndpoint)
    listenerBus.post(SparkListenerExecutorAdded(
      System.currentTimeMillis,
      executorEndpoint.localExecutorId,
      new ExecutorInfo(executorEndpoint.localExecutorHostname, totalCores, Map.empty)))
    launcherBackend.setAppId(appId)
    launcherBackend.setState(SparkAppHandle.State.RUNNING)
  }

  override def stop() {
    stop(SparkAppHandle.State.FINISHED)
  }

  override def reviveOffers() {
    localEndpoint.send(ReviveOffers)
  }

  override def defaultParallelism(): Int =
    scheduler.conf.getInt("spark.default.parallelism", totalCores)

  override def killTask(taskId: Long, executorId: String, interruptThread: Boolean) {
    localEndpoint.send(KillTask(taskId, interruptThread))
  }

  override def statusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) {
    localEndpoint.send(StatusUpdate(taskId, state, serializedData))
  }

  override def applicationId(): String = appId

  private def stop(finalState: SparkAppHandle.State): Unit = {
    localEndpoint.ask(StopExecutor)
    try {
      launcherBackend.setState(finalState)
    } finally {
      launcherBackend.close()
    }
  }

} 

package org.apache.spark.storage

import scala.concurrent.{ExecutionContext, Future}

import org.apache.spark.{MapOutputTracker, SparkEnv}
import org.apache.spark.internal.Logging
import org.apache.spark.rpc.{RpcCallContext, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.storage.BlockManagerMessages._
import org.apache.spark.util.{ThreadUtils, Utils}


private[storage]
class BlockManagerSlaveEndpoint(
    override val rpcEnv: RpcEnv,
    blockManager: BlockManager,
    mapOutputTracker: MapOutputTracker)
  extends ThreadSafeRpcEndpoint with Logging {

  private val asyncThreadPool =
    ThreadUtils.newDaemonCachedThreadPool("block-manager-slave-async-thread-pool")
  private implicit val asyncExecutionContext = ExecutionContext.fromExecutorService(asyncThreadPool)

  // Operations that involve removing blocks may be slow and should be done asynchronously
  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case RemoveBlock(blockId) =>
      doAsync[Boolean]("removing block " + blockId, context) {
        blockManager.removeBlock(blockId)
        true
      }

    case RemoveRdd(rddId) =>
      doAsync[Int]("removing RDD " + rddId, context) {
        blockManager.removeRdd(rddId)
      }

    case RemoveShuffle(shuffleId) =>
      doAsync[Boolean]("removing shuffle " + shuffleId, context) {
        if (mapOutputTracker != null) {
          mapOutputTracker.unregisterShuffle(shuffleId)
        }
        SparkEnv.get.shuffleManager.unregisterShuffle(shuffleId)
      }

    case RemoveBroadcast(broadcastId, _) =>
      doAsync[Int]("removing broadcast " + broadcastId, context) {
        blockManager.removeBroadcast(broadcastId, tellMaster = true)
      }

    case GetBlockStatus(blockId, _) =>
      context.reply(blockManager.getStatus(blockId))

    case GetMatchingBlockIds(filter, _) =>
      context.reply(blockManager.getMatchingBlockIds(filter))

    case TriggerThreadDump =>
      context.reply(Utils.getThreadDump())
  }

  private def doAsync[T](actionMessage: String, context: RpcCallContext)(body: => T) {
    val future = Future {
      logDebug(actionMessage)
      body
    }
    future.onSuccess { case response =>
      logDebug("Done " + actionMessage + ", response is " + response)
      context.reply(response)
      logDebug("Sent response: " + response + " to " + context.senderAddress)
    }
    future.onFailure { case t: Throwable =>
      logError("Error in " + actionMessage, t)
      context.sendFailure(t)
    }
  }

  override def onStop(): Unit = {
    asyncThreadPool.shutdownNow()
  }
} 

package org.apache.spark.sql.execution.streaming.state

import java.util.UUID

import scala.collection.mutable

import org.apache.spark.SparkEnv
import org.apache.spark.internal.Logging
import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.scheduler.ExecutorCacheTaskLocation
import org.apache.spark.util.RpcUtils


private class StateStoreCoordinator(override val rpcEnv: RpcEnv)
    extends ThreadSafeRpcEndpoint with Logging {
  private val instances = new mutable.HashMap[StateStoreProviderId, ExecutorCacheTaskLocation]

  override def receive: PartialFunction[Any, Unit] = {
    case ReportActiveInstance(id, host, executorId) =>
      logDebug(s"Reported state store $id is active at $executorId")
      instances.put(id, ExecutorCacheTaskLocation(host, executorId))
  }

  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case VerifyIfInstanceActive(id, execId) =>
      val response = instances.get(id) match {
        case Some(location) => location.executorId == execId
        case None => false
      }
      logDebug(s"Verified that state store $id is active: $response")
      context.reply(response)

    case GetLocation(id) =>
      val executorId = instances.get(id).map(_.toString)
      logDebug(s"Got location of the state store $id: $executorId")
      context.reply(executorId)

    case DeactivateInstances(runId) =>
      val storeIdsToRemove =
        instances.keys.filter(_.queryRunId == runId).toSeq
      instances --= storeIdsToRemove
      logDebug(s"Deactivating instances related to checkpoint location $runId: " +
        storeIdsToRemove.mkString(", "))
      context.reply(true)

    case StopCoordinator =>
      stop() // Stop before replying to ensure that endpoint name has been deregistered
      logInfo("StateStoreCoordinator stopped")
      context.reply(true)
  }
} 

package org.apache.spark.sql.execution.streaming.continuous.shuffle

import java.util.concurrent._
import java.util.concurrent.atomic.AtomicBoolean

import org.apache.spark.internal.Logging
import org.apache.spark.rpc.{RpcCallContext, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.sql.catalyst.expressions.UnsafeRow
import org.apache.spark.util.NextIterator


      override def getNext(): UnsafeRow = {
        var nextRow: UnsafeRow = null
        while (!finished && nextRow == null) {
          completion.poll(epochIntervalMs, TimeUnit.MILLISECONDS) match {
            case null =>
              // Try again if the poll didn't wait long enough to get a real result.
              // But we should be getting at least an epoch marker every checkpoint interval.
              val writerIdsUncommitted = writerEpochMarkersReceived.zipWithIndex.collect {
                case (flag, idx) if !flag => idx
              }
              logWarning(
                s"Completion service failed to make progress after $epochIntervalMs ms. Waiting " +
                  s"for writers ${writerIdsUncommitted.mkString(",")} to send epoch markers.")

            // The completion service guarantees this future will be available immediately.
            case future => future.get() match {
              case ReceiverRow(writerId, r) =>
                // Start reading the next element in the queue we just took from.
                completion.submit(completionTask(writerId))
                nextRow = r
              case ReceiverEpochMarker(writerId) =>
                // Don't read any more from this queue. If all the writers have sent epoch markers,
                // the epoch is over; otherwise we need to loop again to poll from the remaining
                // writers.
                writerEpochMarkersReceived(writerId) = true
                if (writerEpochMarkersReceived.forall(_ == true)) {
                  finished = true
                }
            }
          }
        }

        nextRow
      }

      override def close(): Unit = {
        executor.shutdownNow()
      }
    }
  }
} 

package org.apache.spark.sql.execution.streaming

import org.apache.spark.SparkEnv
import org.apache.spark.rpc.{RpcCallContext, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.sources.v2.reader.streaming.PartitionOffset

case class ContinuousRecordPartitionOffset(partitionId: Int, offset: Int) extends PartitionOffset
case class GetRecord(offset: ContinuousRecordPartitionOffset)


  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case GetRecord(ContinuousRecordPartitionOffset(partitionId, offset)) =>
      lock.synchronized {
        val bufOffset = offset - startOffsets(partitionId)
        val buf = buckets(partitionId)
        val record = if (buf.size <= bufOffset) None else Some(buf(bufOffset))

        context.reply(record.map(InternalRow(_)))
      }
  }
} 

package org.apache.spark.sql.oap.rpc

import scala.collection.mutable

import org.apache.spark.internal.Logging
import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.scheduler.LiveListenerBus
import org.apache.spark.sql.oap.listener.SparkListenerCustomInfoUpdate
import org.apache.spark.sql.oap.rpc.OapMessages._


private[spark] class OapRpcManagerMaster(oapRpcManagerMasterEndpoint: OapRpcManagerMasterEndpoint)
  extends OapRpcManager with Logging {

  private def sendOneWayMessageToExecutors(message: OapMessage): Unit = {
    oapRpcManagerMasterEndpoint.rpcEndpointRefByExecutor.foreach {
      case (_, slaveEndpoint) => slaveEndpoint.send(message)
    }
  }

  override private[spark] def send(message: OapMessage): Unit = {
    sendOneWayMessageToExecutors(message)
  }
}

private[spark] object OapRpcManagerMaster {
  val DRIVER_ENDPOINT_NAME = "OapRpcManagerMaster"
}

private[spark] class OapRpcManagerMasterEndpoint(
    override val rpcEnv: RpcEnv,
    listenerBus: LiveListenerBus)
  extends ThreadSafeRpcEndpoint with Logging {

  // Mapping from executor ID to RpcEndpointRef.
  private[rpc] val rpcEndpointRefByExecutor = new mutable.HashMap[String, RpcEndpointRef]

  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case RegisterOapRpcManager(executorId, slaveEndpoint) =>
      context.reply(handleRegistration(executorId, slaveEndpoint))
    case _ =>
  }

  override def receive: PartialFunction[Any, Unit] = {
    case heartbeat: Heartbeat => handleHeartbeat(heartbeat)
    case message: OapMessage => handleNormalOapMessage(message)
    case _ =>
  }

  private def handleRegistration(executorId: String, ref: RpcEndpointRef): Boolean = {
    rpcEndpointRefByExecutor += ((executorId, ref))
    true
  }

  private def handleNormalOapMessage(message: OapMessage) = message match {
    case _: Heartbeat => throw new IllegalArgumentException(
      "This is only to deal with non-heartbeat messages")
    case DummyMessage(id, someContent) =>
      val c = this.getClass.getMethods
      logWarning(s"Dummy message received on Driver with id: $id, content: $someContent")
    case _ =>
  }

  private def handleHeartbeat(heartbeat: Heartbeat) = heartbeat match {
    case FiberCacheHeartbeat(executorId, blockManagerId, content) =>
      listenerBus.post(SparkListenerCustomInfoUpdate(
        blockManagerId.host, executorId, "OapFiberCacheHeartBeatMessager", content))
    case FiberCacheMetricsHeartbeat(executorId, blockManagerId, content) =>
      listenerBus.post(SparkListenerCustomInfoUpdate(
        blockManagerId.host, executorId, "FiberCacheManagerMessager", content))
    case _ =>
  }
} 

package org.apache.spark.sql.oap.rpc

import java.util.concurrent.TimeUnit

import org.apache.spark.SparkConf
import org.apache.spark.internal.Logging
import org.apache.spark.rpc.{RpcEndpointRef, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.sql.execution.datasources.oap.filecache.{CacheStats, FiberCacheManager}
import org.apache.spark.sql.internal.oap.OapConf
import org.apache.spark.sql.oap.adapter.RpcEndpointRefAdapter
import org.apache.spark.sql.oap.rpc.OapMessages._
import org.apache.spark.storage.BlockManager
import org.apache.spark.util.{ThreadUtils, Utils}


private[spark] class OapRpcManagerSlave(
    rpcEnv: RpcEnv,
    val driverEndpoint: RpcEndpointRef,
    executorId: String,
    blockManager: BlockManager,
    fiberCacheManager: FiberCacheManager,
    conf: SparkConf) extends OapRpcManager {

  // Send OapHeartbeatMessage to Driver timed
  private val oapHeartbeater =
    ThreadUtils.newDaemonSingleThreadScheduledExecutor("driver-heartbeater")

  private val slaveEndpoint = rpcEnv.setupEndpoint(
    s"OapRpcManagerSlave_$executorId", new OapRpcManagerSlaveEndpoint(rpcEnv, fiberCacheManager))

  initialize()
  startOapHeartbeater()

  protected def heartbeatMessages: Array[() => Heartbeat] = {
    Array(
      () => FiberCacheHeartbeat(
        executorId, blockManager.blockManagerId, fiberCacheManager.status()),
      () => FiberCacheMetricsHeartbeat(executorId, blockManager.blockManagerId,
        CacheStats.status(fiberCacheManager.cacheStats, conf)))
  }

  private def initialize() = {
    RpcEndpointRefAdapter.askSync[Boolean](
      driverEndpoint, RegisterOapRpcManager(executorId, slaveEndpoint))
  }

  override private[spark] def send(message: OapMessage): Unit = {
    driverEndpoint.send(message)
  }

  private[sql] def startOapHeartbeater(): Unit = {

    def reportHeartbeat(): Unit = {
      // OapRpcManagerSlave is created in SparkEnv. Before we start the heartbeat, we need make
      // sure the SparkEnv has been created and the block manager has been initialized. We check
      // blockManagerId as it will be set after initialization.
      if (blockManager.blockManagerId != null) {
        heartbeatMessages.map(_.apply()).foreach(send)
      }
    }

    val intervalMs = conf.getTimeAsMs(
      OapConf.OAP_HEARTBEAT_INTERVAL.key, OapConf.OAP_HEARTBEAT_INTERVAL.defaultValue.get)

    // Wait a random interval so the heartbeats don't end up in sync
    val initialDelay = intervalMs + (math.random * intervalMs).asInstanceOf[Int]

    val heartbeatTask = new Runnable() {
      override def run(): Unit = Utils.logUncaughtExceptions(reportHeartbeat())
    }
    oapHeartbeater.scheduleAtFixedRate(
      heartbeatTask, initialDelay, intervalMs, TimeUnit.MILLISECONDS)
  }

  override private[spark] def stop(): Unit = {
    oapHeartbeater.shutdown()
  }
}

private[spark] class OapRpcManagerSlaveEndpoint(
    override val rpcEnv: RpcEnv, fiberCacheManager: FiberCacheManager)
  extends ThreadSafeRpcEndpoint with Logging {

  override def receive: PartialFunction[Any, Unit] = {
    case message: OapMessage => handleOapMessage(message)
    case _ =>
  }

  private def handleOapMessage(message: OapMessage): Unit = message match {
    case CacheDrop(indexName) => fiberCacheManager.releaseIndexCache(indexName)
    case _ =>
  }
} 

package org.apache.spark.storage

import scala.concurrent.{ExecutionContext, Future}

import org.apache.spark.{MapOutputTracker, SparkEnv}
import org.apache.spark.internal.Logging
import org.apache.spark.rpc.{RpcCallContext, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.storage.BlockManagerMessages._
import org.apache.spark.util.{ThreadUtils, Utils}


private[storage]
class BlockManagerSlaveEndpoint(
    override val rpcEnv: RpcEnv,
    blockManager: BlockManager,
    mapOutputTracker: MapOutputTracker)
  extends ThreadSafeRpcEndpoint with Logging {

  private val asyncThreadPool =
    ThreadUtils.newDaemonCachedThreadPool("block-manager-slave-async-thread-pool")
  private implicit val asyncExecutionContext = ExecutionContext.fromExecutorService(asyncThreadPool)

  // Operations that involve removing blocks may be slow and should be done asynchronously
  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case RemoveBlock(blockId) =>
      doAsync[Boolean]("removing block " + blockId, context) {
        blockManager.removeBlock(blockId)
        true
      }

    case RemoveRdd(rddId) =>
      doAsync[Int]("removing RDD " + rddId, context) {
        blockManager.removeRdd(rddId)
      }

    case RemoveShuffle(shuffleId) =>
      doAsync[Boolean]("removing shuffle " + shuffleId, context) {
        if (mapOutputTracker != null) {
          mapOutputTracker.unregisterShuffle(shuffleId)
        }
        SparkEnv.get.shuffleManager.unregisterShuffle(shuffleId)
      }

    case RemoveBroadcast(broadcastId, _) =>
      doAsync[Int]("removing broadcast " + broadcastId, context) {
        blockManager.removeBroadcast(broadcastId, tellMaster = true)
      }

    case GetBlockStatus(blockId, _) =>
      context.reply(blockManager.getStatus(blockId))

    case GetMatchingBlockIds(filter, _) =>
      context.reply(blockManager.getMatchingBlockIds(filter))

    case TriggerThreadDump =>
      context.reply(Utils.getThreadDump())
  }

  private def doAsync[T](actionMessage: String, context: RpcCallContext)(body: => T) {
    val future = Future {
      logDebug(actionMessage)
      body
    }
    future.onSuccess { case response =>
      logDebug("Done " + actionMessage + ", response is " + response)
      context.reply(response)
      logDebug("Sent response: " + response + " to " + context.senderAddress)
    }
    future.onFailure { case t: Throwable =>
      logError("Error in " + actionMessage, t)
      context.sendFailure(t)
    }
  }

  override def onStop(): Unit = {
    asyncThreadPool.shutdownNow()
  }
} 

package org.apache.spark.storage

import scala.concurrent.{ExecutionContext, Future}

import org.apache.spark.internal.Logging
import org.apache.spark.rpc.{RpcCallContext, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.storage.BlockManagerMessages._
import org.apache.spark.util.{ThreadUtils, Utils}
import org.apache.spark.{MapOutputTracker, SparkEnv}


private[storage]
class BlockManagerSlaveEndpoint(
    override val rpcEnv: RpcEnv,
    blockManager: BlockManager,
    mapOutputTracker: MapOutputTracker)
  extends ThreadSafeRpcEndpoint with Logging {

  private val user = Utils.getCurrentUserName

  private val asyncThreadPool =
    ThreadUtils.newDaemonCachedThreadPool("block-manager-slave-async-thread-pool")
  private implicit val asyncExecutionContext = ExecutionContext.fromExecutorService(asyncThreadPool)

  // Operations that involve removing blocks may be slow and should be done asynchronously
  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case RemoveBlock(blockId) =>
      doAsync[Boolean]("removing block " + blockId, context) {
        blockManager.removeBlock(blockId)
        true
      }

    case RemoveRdd(rddId) =>
      doAsync[Int]("removing RDD " + rddId, context) {
        blockManager.removeRdd(rddId)
      }

    case RemoveShuffle(shuffleId) =>
      doAsync[Boolean]("removing shuffle " + shuffleId, context) {
        if (mapOutputTracker != null) {
          mapOutputTracker.unregisterShuffle(shuffleId)
        }
        SparkEnv.get(user).shuffleManager.unregisterShuffle(shuffleId)
      }

    case RemoveBroadcast(broadcastId, _) =>
      doAsync[Int]("removing broadcast " + broadcastId, context) {
        blockManager.removeBroadcast(broadcastId, tellMaster = true)
      }

    case GetBlockStatus(blockId, _) =>
      context.reply(blockManager.getStatus(blockId))

    case GetMatchingBlockIds(filter, _) =>
      context.reply(blockManager.getMatchingBlockIds(filter))

    case TriggerThreadDump =>
      context.reply(Utils.getThreadDump())
  }

  private def doAsync[T](actionMessage: String, context: RpcCallContext)(body: => T) {
    val future = Future {
      logDebug(actionMessage)
      body
    }
    future.onSuccess { case response =>
      logDebug("Done " + actionMessage + ", response is " + response)
      context.reply(response)
      logDebug("Sent response: " + response + " to " + context.senderAddress)
    }
    future.onFailure { case t: Throwable =>
      logError("Error in " + actionMessage, t)
      context.sendFailure(t)
    }
  }

  override def onStop(): Unit = {
    asyncThreadPool.shutdownNow()
  }
} 

package org.apache.spark.storage

import scala.concurrent.{ExecutionContext, Future}

import org.apache.spark.{MapOutputTracker, SparkEnv}
import org.apache.spark.internal.Logging
import org.apache.spark.rpc.{RpcCallContext, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.storage.BlockManagerMessages._
import org.apache.spark.util.{ThreadUtils, Utils}


private[storage]
class BlockManagerSlaveEndpoint(
    override val rpcEnv: RpcEnv,
    blockManager: BlockManager,
    mapOutputTracker: MapOutputTracker)
  extends ThreadSafeRpcEndpoint with Logging {

  private val asyncThreadPool =
    ThreadUtils.newDaemonCachedThreadPool("block-manager-slave-async-thread-pool")
  private implicit val asyncExecutionContext = ExecutionContext.fromExecutorService(asyncThreadPool)

  // Operations that involve removing blocks may be slow and should be done asynchronously
  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case RemoveBlock(blockId) =>
      doAsync[Boolean]("removing block " + blockId, context) {
        blockManager.removeBlock(blockId)
        true
      }

    case RemoveRdd(rddId) =>
      doAsync[Int]("removing RDD " + rddId, context) {
        blockManager.removeRdd(rddId)
      }

    case RemoveShuffle(shuffleId) =>
      doAsync[Boolean]("removing shuffle " + shuffleId, context) {
        if (mapOutputTracker != null) {
          mapOutputTracker.unregisterShuffle(shuffleId)
        }
        SparkEnv.get.shuffleManager.unregisterShuffle(shuffleId)
      }

    case RemoveBroadcast(broadcastId, _) =>
      doAsync[Int]("removing broadcast " + broadcastId, context) {
        blockManager.removeBroadcast(broadcastId, tellMaster = true)
      }

    case GetBlockStatus(blockId, _) =>
      context.reply(blockManager.getStatus(blockId))

    case GetMatchingBlockIds(filter, _) =>
      context.reply(blockManager.getMatchingBlockIds(filter))

    case TriggerThreadDump =>
      context.reply(Utils.getThreadDump())

    case ReplicateBlock(blockId, replicas, maxReplicas) =>
      context.reply(blockManager.replicateBlock(blockId, replicas.toSet, maxReplicas))

  }

  private def doAsync[T](actionMessage: String, context: RpcCallContext)(body: => T) {
    val future = Future {
      logDebug(actionMessage)
      body
    }
    future.foreach { response =>
      logDebug(s"Done $actionMessage, response is $response")
      context.reply(response)
      logDebug(s"Sent response: $response to ${context.senderAddress}")
    }
    future.failed.foreach { t =>
      logError(s"Error in $actionMessage", t)
      context.sendFailure(t)
    }
  }

  override def onStop(): Unit = {
    asyncThreadPool.shutdownNow()
  }
} 

package org.apache.spark.storage

import scala.concurrent.{ExecutionContext, Future}

import org.apache.spark.{Logging, MapOutputTracker, SparkEnv}
import org.apache.spark.rpc.{RpcCallContext, RpcEnv, ThreadSafeRpcEndpoint}
import org.apache.spark.storage.BlockManagerMessages._
import org.apache.spark.util.{ThreadUtils, Utils}


private[storage]
class BlockManagerSlaveEndpoint(
    override val rpcEnv: RpcEnv,
    blockManager: BlockManager,
    mapOutputTracker: MapOutputTracker)
  extends ThreadSafeRpcEndpoint with Logging {

  private val asyncThreadPool =
    ThreadUtils.newDaemonCachedThreadPool("block-manager-slave-async-thread-pool")
  private implicit val asyncExecutionContext = ExecutionContext.fromExecutorService(asyncThreadPool)

  // Operations that involve removing blocks may be slow and should be done asynchronously
  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case RemoveBlock(blockId) =>
      doAsync[Boolean]("removing block " + blockId, context) {
        blockManager.removeBlock(blockId)
        true
      }

    case RemoveRdd(rddId) =>
      doAsync[Int]("removing RDD " + rddId, context) {
        blockManager.removeRdd(rddId)
      }

    case RemoveShuffle(shuffleId) =>
      doAsync[Boolean]("removing shuffle " + shuffleId, context) {
        if (mapOutputTracker != null) {
          mapOutputTracker.unregisterShuffle(shuffleId)
        }
        SparkEnv.get.shuffleManager.unregisterShuffle(shuffleId)
      }

    case RemoveBroadcast(broadcastId, _) =>
      doAsync[Int]("removing broadcast " + broadcastId, context) {
        blockManager.removeBroadcast(broadcastId, tellMaster = true)
      }

    case GetBlockStatus(blockId, _) =>
      context.reply(blockManager.getStatus(blockId))

    case GetMatchingBlockIds(filter, _) =>
      context.reply(blockManager.getMatchingBlockIds(filter))

    case TriggerThreadDump =>
      context.reply(Utils.getThreadDump())
  }

  private def doAsync[T](actionMessage: String, context: RpcCallContext)(body: => T) {
    val future = Future {
      logDebug(actionMessage)
      body
    }
    future.onSuccess { case response =>
      logDebug("Done " + actionMessage + ", response is " + response)
      context.reply(response)
      logDebug("Sent response: " + response + " to " + context.senderAddress)
    }
    future.onFailure { case t: Throwable =>
      logError("Error in " + actionMessage, t)
      context.sendFailure(t)
    }
  }

  override def onStop(): Unit = {
    asyncThreadPool.shutdownNow()
  }
} 

package org.apache.spark.groupon.metrics.util

import org.apache.spark.groupon.metrics._
import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcEnv}

class TestMetricsRpcEndpoint(override val rpcEnv: RpcEnv) extends ThreadSafeRpcEndpoint {
  val metricStore = new scala.collection.mutable.ArrayBuffer[MetricMessage]()

  override def receive: PartialFunction[Any, Unit] = {
    case msg: MetricMessage => {
      metricStore += msg
    }
  }

  def clear(): Unit = {
    metricStore.clear()
  }

  def getMetricNames: Seq[String] = {
    metricStore.map(metricMessage => metricMessage.metricName).toSeq
  }

  def getMetricValues: Seq[AnyVal] = {
    metricStore.map(metricMessage => metricMessage.value).toSeq
  }
}