diff --git a/core/src/main/scala/spark/Aggregator.scala b/core/src/main/scala/spark/Aggregator.scala
index 6516bea157233444021b16eba6cb05b8d9722907..b0daa70cfdf14f3f59ee45ade35b251a1472c5e5 100644
--- a/core/src/main/scala/spark/Aggregator.scala
+++ b/core/src/main/scala/spark/Aggregator.scala
@@ -9,9 +9,9 @@ package spark
* known as map-side aggregations. When set to false,
* mergeCombiners function is not used.
*/
-class Aggregator[K, V, C] (
+case class Aggregator[K, V, C] (
val createCombiner: V => C,
val mergeValue: (C, V) => C,
val mergeCombiners: (C, C) => C,
val mapSideCombine: Boolean = true)
- extends Serializable
+
diff --git a/core/src/main/scala/spark/PairRDDFunctions.scala b/core/src/main/scala/spark/PairRDDFunctions.scala
index 64018f8c6bf1efaee6e5585fc6aae9c1c46dbc53..aa1d00c63c3f25b611236602e0867040b1bd9c23 100644
--- a/core/src/main/scala/spark/PairRDDFunctions.scala
+++ b/core/src/main/scala/spark/PairRDDFunctions.scala
@@ -1,11 +1,10 @@
package spark
import java.io.EOFException
-import java.net.URL
import java.io.ObjectInputStream
+import java.net.URL
+import java.util.{Date, HashMap => JHashMap}
import java.util.concurrent.atomic.AtomicLong
-import java.util.{HashMap => JHashMap}
-import java.util.Date
import java.text.SimpleDateFormat
import scala.collection.Map
@@ -50,9 +49,18 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
def combineByKey[C](createCombiner: V => C,
mergeValue: (C, V) => C,
mergeCombiners: (C, C) => C,
- partitioner: Partitioner): RDD[(K, C)] = {
- val aggregator = new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners)
- new ShuffledRDD(self, aggregator, partitioner)
+ partitioner: Partitioner,
+ mapSideCombine: Boolean = true): RDD[(K, C)] = {
+ val aggregator =
+ if (mapSideCombine) {
+ new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners)
+ } else {
+ // Don't apply map-side combiner.
+ // A sanity check to make sure mergeCombiners is not defined.
+ assert(mergeCombiners == null)
+ new Aggregator[K, V, C](createCombiner, mergeValue, null, false)
+ }
+ new ShuffledAggregatedRDD(self, aggregator, partitioner)
}
def combineByKey[C](createCombiner: V => C,
@@ -65,7 +73,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = {
combineByKey[V]((v: V) => v, func, func, partitioner)
}
-
+
def reduceByKeyLocally(func: (V, V) => V): Map[K, V] = {
def reducePartition(iter: Iterator[(K, V)]): Iterator[JHashMap[K, V]] = {
val map = new JHashMap[K, V]
@@ -116,13 +124,24 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
groupByKey(new HashPartitioner(numSplits))
}
- def partitionBy(partitioner: Partitioner): RDD[(K, V)] = {
- def createCombiner(v: V) = ArrayBuffer(v)
- def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v
- def mergeCombiners(b1: ArrayBuffer[V], b2: ArrayBuffer[V]) = b1 ++= b2
- val bufs = combineByKey[ArrayBuffer[V]](
- createCombiner _, mergeValue _, mergeCombiners _, partitioner)
- bufs.flatMapValues(buf => buf)
+ /**
+ * Repartition the RDD using the specified partitioner. If mapSideCombine is
+ * true, Spark will group values of the same key together on the map side
+ * before the repartitioning. If a large number of duplicated keys are
+ * expected, and the size of the keys are large, mapSideCombine should be set
+ * to true.
+ */
+ def partitionBy(partitioner: Partitioner, mapSideCombine: Boolean = false): RDD[(K, V)] = {
+ if (mapSideCombine) {
+ def createCombiner(v: V) = ArrayBuffer(v)
+ def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v
+ def mergeCombiners(b1: ArrayBuffer[V], b2: ArrayBuffer[V]) = b1 ++= b2
+ val bufs = combineByKey[ArrayBuffer[V]](
+ createCombiner _, mergeValue _, mergeCombiners _, partitioner)
+ bufs.flatMapValues(buf => buf)
+ } else {
+ new RepartitionShuffledRDD(self, partitioner)
+ }
}
def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = {
@@ -194,17 +213,17 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
}
def collectAsMap(): Map[K, V] = HashMap(self.collect(): _*)
-
+
def mapValues[U](f: V => U): RDD[(K, U)] = {
val cleanF = self.context.clean(f)
new MappedValuesRDD(self, cleanF)
}
-
+
def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)] = {
val cleanF = self.context.clean(f)
new FlatMappedValuesRDD(self, cleanF)
}
-
+
def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Seq[V], Seq[W]))] = {
val cg = new CoGroupedRDD[K](
Seq(self.asInstanceOf[RDD[(_, _)]], other.asInstanceOf[RDD[(_, _)]]),
@@ -215,12 +234,12 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
(vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]])
}
}
-
+
def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner)
: RDD[(K, (Seq[V], Seq[W1], Seq[W2]))] = {
val cg = new CoGroupedRDD[K](
Seq(self.asInstanceOf[RDD[(_, _)]],
- other1.asInstanceOf[RDD[(_, _)]],
+ other1.asInstanceOf[RDD[(_, _)]],
other2.asInstanceOf[RDD[(_, _)]]),
partitioner)
val prfs = new PairRDDFunctions[K, Seq[Seq[_]]](cg)(classManifest[K], Manifests.seqSeqManifest)
@@ -289,7 +308,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String)(implicit fm: ClassManifest[F]) {
saveAsHadoopFile(path, getKeyClass, getValueClass, fm.erasure.asInstanceOf[Class[F]])
}
-
+
def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]](path: String)(implicit fm: ClassManifest[F]) {
saveAsNewAPIHadoopFile(path, getKeyClass, getValueClass, fm.erasure.asInstanceOf[Class[F]])
}
@@ -363,7 +382,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
FileOutputFormat.setOutputPath(conf, HadoopWriter.createPathFromString(path, conf))
saveAsHadoopDataset(conf)
}
-
+
def saveAsHadoopDataset(conf: JobConf) {
val outputFormatClass = conf.getOutputFormat
val keyClass = conf.getOutputKeyClass
@@ -377,7 +396,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
if (valueClass == null) {
throw new SparkException("Output value class not set")
}
-
+
logInfo("Saving as hadoop file of type (" + keyClass.getSimpleName+ ", " + valueClass.getSimpleName+ ")")
val writer = new HadoopWriter(conf)
@@ -390,14 +409,14 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
writer.setup(context.stageId, context.splitId, attemptNumber)
writer.open()
-
+
var count = 0
while(iter.hasNext) {
val record = iter.next
count += 1
writer.write(record._1.asInstanceOf[AnyRef], record._2.asInstanceOf[AnyRef])
}
-
+
writer.close()
writer.commit()
}
@@ -413,28 +432,14 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
class OrderedRDDFunctions[K <% Ordered[K]: ClassManifest, V: ClassManifest](
self: RDD[(K, V)])
- extends Logging
+ extends Logging
with Serializable {
def sortByKey(ascending: Boolean = true): RDD[(K,V)] = {
- val rangePartitionedRDD = self.partitionBy(new RangePartitioner(self.splits.size, self, ascending))
- new SortedRDD(rangePartitionedRDD, ascending)
+ new ShuffledSortedRDD(self, ascending)
}
}
-class SortedRDD[K <% Ordered[K], V](prev: RDD[(K, V)], ascending: Boolean)
- extends RDD[(K, V)](prev.context) {
-
- override def splits = prev.splits
- override val partitioner = prev.partitioner
- override val dependencies = List(new OneToOneDependency(prev))
-
- override def compute(split: Split) = {
- prev.iterator(split).toArray
- .sortWith((x, y) => if (ascending) x._1 < y._1 else x._1 > y._1).iterator
- }
-}
-
class MappedValuesRDD[K, V, U](prev: RDD[(K, V)], f: V => U) extends RDD[(K, U)](prev.context) {
override def splits = prev.splits
override val dependencies = List(new OneToOneDependency(prev))
@@ -444,7 +449,7 @@ class MappedValuesRDD[K, V, U](prev: RDD[(K, V)], f: V => U) extends RDD[(K, U)]
class FlatMappedValuesRDD[K, V, U](prev: RDD[(K, V)], f: V => TraversableOnce[U])
extends RDD[(K, U)](prev.context) {
-
+
override def splits = prev.splits
override val dependencies = List(new OneToOneDependency(prev))
override val partitioner = prev.partitioner
diff --git a/core/src/main/scala/spark/ShuffledRDD.scala b/core/src/main/scala/spark/ShuffledRDD.scala
index 3616d8e47ec85ffb7e819e2544ff20d29bbb9d41..a7346060b3ccb542abb5ee50a2697db7555de4ca 100644
--- a/core/src/main/scala/spark/ShuffledRDD.scala
+++ b/core/src/main/scala/spark/ShuffledRDD.scala
@@ -1,29 +1,89 @@
package spark
+import scala.collection.mutable.ArrayBuffer
import java.util.{HashMap => JHashMap}
+
class ShuffledRDDSplit(val idx: Int) extends Split {
override val index = idx
override def hashCode(): Int = idx
}
-class ShuffledRDD[K, V, C](
+
+/**
+ * The resulting RDD from a shuffle (e.g. repartitioning of data).
+ */
+abstract class ShuffledRDD[K, V, C](
@transient parent: RDD[(K, V)],
aggregator: Aggregator[K, V, C],
- part : Partitioner)
+ part : Partitioner)
extends RDD[(K, C)](parent.context) {
- //override val partitioner = Some(part)
+
override val partitioner = Some(part)
-
+
@transient
val splits_ = Array.tabulate[Split](part.numPartitions)(i => new ShuffledRDDSplit(i))
override def splits = splits_
-
+
override def preferredLocations(split: Split) = Nil
-
+
val dep = new ShuffleDependency(context.newShuffleId, parent, aggregator, part)
override val dependencies = List(dep)
+}
+
+
+/**
+ * Repartition a key-value pair RDD.
+ */
+class RepartitionShuffledRDD[K, V](
+ @transient parent: RDD[(K, V)],
+ part : Partitioner)
+ extends ShuffledRDD[K, V, V](
+ parent,
+ Aggregator[K, V, V](null, null, null, false),
+ part) {
+
+ override def compute(split: Split): Iterator[(K, V)] = {
+ val buf = new ArrayBuffer[(K, V)]
+ val fetcher = SparkEnv.get.shuffleFetcher
+ def addTupleToBuffer(k: K, v: V) = { buf += Tuple(k, v) }
+ fetcher.fetch[K, V](dep.shuffleId, split.index, addTupleToBuffer)
+ buf.iterator
+ }
+}
+
+
+/**
+ * A sort-based shuffle (that doesn't apply aggregation). It does so by first
+ * repartitioning the RDD by range, and then sort within each range.
+ */
+class ShuffledSortedRDD[K <% Ordered[K]: ClassManifest, V](
+ @transient parent: RDD[(K, V)],
+ ascending: Boolean)
+ extends RepartitionShuffledRDD[K, V](
+ parent,
+ new RangePartitioner(parent.splits.size, parent, ascending)) {
+
+ override def compute(split: Split): Iterator[(K, V)] = {
+ // By separating this from RepartitionShuffledRDD, we avoided a
+ // buf.iterator.toArray call, thus avoiding building up the buffer twice.
+ val buf = new ArrayBuffer[(K, V)]
+ def addTupleToBuffer(k: K, v: V) = { buf += Tuple(k, v) }
+ SparkEnv.get.shuffleFetcher.fetch[K, V](dep.shuffleId, split.index, addTupleToBuffer)
+ buf.sortWith((x, y) => if (ascending) x._1 < y._1 else x._1 > y._1).iterator
+ }
+}
+
+
+/**
+ * The resulting RDD from shuffle and running (hash-based) aggregation.
+ */
+class ShuffledAggregatedRDD[K, V, C](
+ @transient parent: RDD[(K, V)],
+ aggregator: Aggregator[K, V, C],
+ part : Partitioner)
+ extends ShuffledRDD[K, V, C](parent, aggregator, part) {
override def compute(split: Split): Iterator[(K, C)] = {
val combiners = new JHashMap[K, C]
diff --git a/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala
index 3bcc588015d0186325b09017d61432865eb9012c..745aa0c93915b3b6836f38d9e1d364ea6bb3c277 100644
--- a/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala
@@ -44,7 +44,8 @@ object ShuffleMapTask {
}
// Since both the JarSet and FileSet have the same format this is used for both.
- def serializeFileSet(set : HashMap[String, Long], stageId: Int, cache : JHashMap[Int, Array[Byte]]) : Array[Byte] = {
+ def serializeFileSet(
+ set : HashMap[String, Long], stageId: Int, cache : JHashMap[Int, Array[Byte]]) : Array[Byte] = {
val old = cache.get(stageId)
if (old != null) {
return old
@@ -59,7 +60,6 @@ object ShuffleMapTask {
}
}
-
def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], ShuffleDependency[_,_,_]) = {
synchronized {
val loader = Thread.currentThread.getContextClassLoader
@@ -113,7 +113,8 @@ class ShuffleMapTask(
out.writeInt(bytes.length)
out.write(bytes)
- val fileSetBytes = ShuffleMapTask.serializeFileSet(fileSet, stageId, ShuffleMapTask.fileSetCache)
+ val fileSetBytes = ShuffleMapTask.serializeFileSet(
+ fileSet, stageId, ShuffleMapTask.fileSetCache)
out.writeInt(fileSetBytes.length)
out.write(fileSetBytes)
val jarSetBytes = ShuffleMapTask.serializeFileSet(jarSet, stageId, ShuffleMapTask.jarSetCache)
@@ -172,7 +173,7 @@ class ShuffleMapTask(
buckets.map(_.iterator)
} else {
// No combiners (no map-side aggregation). Simply partition the map output.
- val buckets = Array.tabulate(numOutputSplits)(_ => new ArrayBuffer[(Any, Any)])
+ val buckets = Array.fill(numOutputSplits)(new ArrayBuffer[(Any, Any)])
for (elem <- rdd.iterator(split)) {
val pair = elem.asInstanceOf[(Any, Any)]
val bucketId = partitioner.getPartition(pair._1)
diff --git a/core/src/test/scala/spark/ShuffleSuite.scala b/core/src/test/scala/spark/ShuffleSuite.scala
index f622c413f7c5f8a51707f4edecb8eb4fc905e1d5..9d7e2591f1f9bd73ec0e756d01b9a8c9d4177c6f 100644
--- a/core/src/test/scala/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/spark/ShuffleSuite.scala
@@ -15,16 +15,16 @@ import scala.collection.mutable.ArrayBuffer
import SparkContext._
class ShuffleSuite extends FunSuite with ShouldMatchers with BeforeAndAfter {
-
+
var sc: SparkContext = _
-
+
after {
if (sc != null) {
sc.stop()
sc = null
}
}
-
+
test("groupByKey") {
sc = new SparkContext("local", "test")
val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)))
@@ -57,7 +57,7 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with BeforeAndAfter {
val valuesFor2 = groups.find(_._1 == 2).get._2
assert(valuesFor2.toList.sorted === List(1))
}
-
+
test("groupByKey with many output partitions") {
sc = new SparkContext("local", "test")
val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)))
@@ -187,7 +187,7 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with BeforeAndAfter {
(4, (ArrayBuffer(), ArrayBuffer('w')))
))
}
-
+
test("zero-partition RDD") {
sc = new SparkContext("local", "test")
val emptyDir = Files.createTempDir()
@@ -195,7 +195,7 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with BeforeAndAfter {
assert(file.splits.size == 0)
assert(file.collect().toList === Nil)
// Test that a shuffle on the file works, because this used to be a bug
- assert(file.map(line => (line, 1)).reduceByKey(_ + _).collect().toList === Nil)
+ assert(file.map(line => (line, 1)).reduceByKey(_ + _).collect().toList === Nil)
}
test("map-side combine") {
@@ -212,7 +212,7 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with BeforeAndAfter {
_+_,
_+_,
false)
- val shuffledRdd = new ShuffledRDD(
+ val shuffledRdd = new ShuffledAggregatedRDD(
pairs, aggregator, new HashPartitioner(2))
assert(shuffledRdd.collect().toSet === Set((1, 8), (2, 1)))
@@ -220,7 +220,7 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with BeforeAndAfter {
// not see an exception because mergeCombine should not have been called.
val aggregatorWithException = new Aggregator[Int, Int, Int](
(v: Int) => v, _+_, ShuffleSuite.mergeCombineException, false)
- val shuffledRdd1 = new ShuffledRDD(
+ val shuffledRdd1 = new ShuffledAggregatedRDD(
pairs, aggregatorWithException, new HashPartitioner(2))
assert(shuffledRdd1.collect().toSet === Set((1, 8), (2, 1)))
@@ -228,7 +228,7 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with BeforeAndAfter {
// expect to see an exception thrown.
val aggregatorWithException1 = new Aggregator[Int, Int, Int](
(v: Int) => v, _+_, ShuffleSuite.mergeCombineException)
- val shuffledRdd2 = new ShuffledRDD(
+ val shuffledRdd2 = new ShuffledAggregatedRDD(
pairs, aggregatorWithException1, new HashPartitioner(2))
evaluating { shuffledRdd2.collect() } should produce [SparkException]
}