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package spark
import akka.actor._
import akka.dispatch._
import akka.pattern.ask
import akka.remote._
import akka.util.Duration
import akka.util.Timeout
import akka.util.duration._
import scala.collection.mutable.ArrayBuffer
import scala.collection.mutable.HashMap
import scala.collection.mutable.HashSet
import spark.storage.BlockManager
import spark.storage.StorageLevel
private[spark] sealed trait CacheTrackerMessage
private[spark] case class AddedToCache(rddId: Int, partition: Int, host: String, size: Long = 0L)
extends CacheTrackerMessage
private[spark] case class DroppedFromCache(rddId: Int, partition: Int, host: String, size: Long = 0L)
extends CacheTrackerMessage
private[spark] case class MemoryCacheLost(host: String) extends CacheTrackerMessage
private[spark] case class RegisterRDD(rddId: Int, numPartitions: Int) extends CacheTrackerMessage
private[spark] case class SlaveCacheStarted(host: String, size: Long) extends CacheTrackerMessage
private[spark] case object GetCacheStatus extends CacheTrackerMessage
private[spark] case object GetCacheLocations extends CacheTrackerMessage
private[spark] case object StopCacheTracker extends CacheTrackerMessage
private[spark] class CacheTrackerActor extends Actor with Logging {
// TODO: Should probably store (String, CacheType) tuples
private val locs = new HashMap[Int, Array[List[String]]]
/**
* A map from the slave's host name to its cache size.
*/
private val slaveCapacity = new HashMap[String, Long]
private val slaveUsage = new HashMap[String, Long]
private def getCacheUsage(host: String): Long = slaveUsage.getOrElse(host, 0L)
private def getCacheCapacity(host: String): Long = slaveCapacity.getOrElse(host, 0L)
private def getCacheAvailable(host: String): Long = getCacheCapacity(host) - getCacheUsage(host)
def receive = {
case SlaveCacheStarted(host: String, size: Long) =>
slaveCapacity.put(host, size)
slaveUsage.put(host, 0)
sender ! true
case RegisterRDD(rddId: Int, numPartitions: Int) =>
logInfo("Registering RDD " + rddId + " with " + numPartitions + " partitions")
locs(rddId) = Array.fill[List[String]](numPartitions)(Nil)
sender ! true
case AddedToCache(rddId, partition, host, size) =>
slaveUsage.put(host, getCacheUsage(host) + size)
locs(rddId)(partition) = host :: locs(rddId)(partition)
sender ! true
case DroppedFromCache(rddId, partition, host, size) =>
slaveUsage.put(host, getCacheUsage(host) - size)
// Do a sanity check to make sure usage is greater than 0.
locs(rddId)(partition) = locs(rddId)(partition).filterNot(_ == host)
sender ! true
case MemoryCacheLost(host) =>
logInfo("Memory cache lost on " + host)
for ((id, locations) <- locs) {
for (i <- 0 until locations.length) {
locations(i) = locations(i).filterNot(_ == host)
}
}
sender ! true
case GetCacheLocations =>
logInfo("Asked for current cache locations")
sender ! locs.map{case (rrdId, array) => (rrdId -> array.clone())}
case GetCacheStatus =>
val status = slaveCapacity.map { case (host, capacity) =>
(host, capacity, getCacheUsage(host))
}.toSeq
sender ! status
case StopCacheTracker =>
logInfo("Stopping CacheTrackerActor")
sender ! true
context.stop(self)
}
}
private[spark] class CacheTracker(actorSystem: ActorSystem, isMaster: Boolean, blockManager: BlockManager)
extends Logging {
// Tracker actor on the master, or remote reference to it on workers
val ip: String = System.getProperty("spark.master.host", "localhost")
val port: Int = System.getProperty("spark.master.port", "7077").toInt
val actorName: String = "CacheTracker"
val timeout = 10.seconds
var trackerActor: ActorRef = if (isMaster) {
val actor = actorSystem.actorOf(Props[CacheTrackerActor], name = actorName)
logInfo("Registered CacheTrackerActor actor")
actor
} else {
val url = "akka://spark@%s:%s/user/%s".format(ip, port, actorName)
actorSystem.actorFor(url)
}
val registeredRddIds = new HashSet[Int]
// Remembers which splits are currently being loaded (on worker nodes)
val loading = new HashSet[String]
// Send a message to the trackerActor and get its result within a default timeout, or
// throw a SparkException if this fails.
def askTracker(message: Any): Any = {
try {
val future = trackerActor.ask(message)(timeout)
return Await.result(future, timeout)
} catch {
case e: Exception =>
throw new SparkException("Error communicating with CacheTracker", e)
}
}
// Send a one-way message to the trackerActor, to which we expect it to reply with true.
def communicate(message: Any) {
if (askTracker(message) != true) {
throw new SparkException("Error reply received from CacheTracker")
}
}
// Registers an RDD (on master only)
def registerRDD(rddId: Int, numPartitions: Int) {
registeredRddIds.synchronized {
if (!registeredRddIds.contains(rddId)) {
logInfo("Registering RDD ID " + rddId + " with cache")
registeredRddIds += rddId
communicate(RegisterRDD(rddId, numPartitions))
}
}
}
// For BlockManager.scala only
def cacheLost(host: String) {
communicate(MemoryCacheLost(host))
logInfo("CacheTracker successfully removed entries on " + host)
}
// Get the usage status of slave caches. Each tuple in the returned sequence
// is in the form of (host name, capacity, usage).
def getCacheStatus(): Seq[(String, Long, Long)] = {
askTracker(GetCacheStatus).asInstanceOf[Seq[(String, Long, Long)]]
}
// For BlockManager.scala only
def notifyFromBlockManager(t: AddedToCache) {
communicate(t)
}
// Get a snapshot of the currently known locations
def getLocationsSnapshot(): HashMap[Int, Array[List[String]]] = {
askTracker(GetCacheLocations).asInstanceOf[HashMap[Int, Array[List[String]]]]
}
// Gets or computes an RDD split
def getOrCompute[T](rdd: RDD[T], split: Split, storageLevel: StorageLevel): Iterator[T] = {
val key = "rdd_%d_%d".format(rdd.id, split.index)
logInfo("Cache key is " + key)
blockManager.get(key) match {
case Some(cachedValues) =>
// Split is in cache, so just return its values
logInfo("Found partition in cache!")
return cachedValues.asInstanceOf[Iterator[T]]
case None =>
// Mark the split as loading (unless someone else marks it first)
loading.synchronized {
if (loading.contains(key)) {
logInfo("Loading contains " + key + ", waiting...")
while (loading.contains(key)) {
try {loading.wait()} catch {case _ =>}
}
logInfo("Loading no longer contains " + key + ", so returning cached result")
// See whether someone else has successfully loaded it. The main way this would fail
// is for the RDD-level cache eviction policy if someone else has loaded the same RDD
// partition but we didn't want to make space for it. However, that case is unlikely
// because it's unlikely that two threads would work on the same RDD partition. One
// downside of the current code is that threads wait serially if this does happen.
blockManager.get(key) match {
case Some(values) =>
return values.asInstanceOf[Iterator[T]]
case None =>
logInfo("Whoever was loading " + key + " failed; we'll try it ourselves")
loading.add(key)
}
} else {
loading.add(key)
}
}
// If we got here, we have to load the split
// Tell the master that we're doing so
//val host = System.getProperty("spark.hostname", Utils.localHostName)
//val future = trackerActor !! AddedToCache(rdd.id, split.index, host)
// TODO: fetch any remote copy of the split that may be available
// TODO: also register a listener for when it unloads
logInfo("Computing partition " + split)
val elements = new ArrayBuffer[Any]
elements ++= rdd.compute(split)
try {
// Try to put this block in the blockManager
blockManager.put(key, elements, storageLevel, true)
//future.apply() // Wait for the reply from the cache tracker
} finally {
loading.synchronized {
loading.remove(key)
loading.notifyAll()
}
}
return elements.iterator.asInstanceOf[Iterator[T]]
}
}
// Called by the Cache to report that an entry has been dropped from it
def dropEntry(rddId: Int, partition: Int) {
communicate(DroppedFromCache(rddId, partition, Utils.localHostName()))
}
def stop() {
communicate(StopCacheTracker)
registeredRddIds.clear()
trackerActor = null
}
}
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