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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark
import scala.collection.mutable.{ArrayBuffer, HashSet}
import org.apache.spark.storage.{BlockManager, StorageLevel}
import org.apache.spark.rdd.RDD
/** Spark class responsible for passing RDDs split contents to the BlockManager and making
sure a node doesn't load two copies of an RDD at once.
*/
private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
private val loading = new HashSet[String]
/** Gets or computes an RDD split. Used by RDD.iterator() when an RDD is cached. */
def getOrCompute[T](rdd: RDD[T], split: Partition, context: TaskContext, 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) =>
// Partition 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 _ : Throwable =>}
}
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)
}
}
try {
// If we got here, we have to load the split
logInfo("Computing partition " + split)
val computedValues = rdd.computeOrReadCheckpoint(split, context)
// Persist the result, so long as the task is not running locally
if (context.runningLocally) { return computedValues }
val elements = new ArrayBuffer[Any]
elements ++= computedValues
blockManager.put(key, elements, storageLevel, true)
return elements.iterator.asInstanceOf[Iterator[T]]
} finally {
loading.synchronized {
loading.remove(key)
loading.notifyAll()
}
}
}
}
}
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