diff --git a/core/src/main/scala/spark/PairRDDFunctions.scala b/core/src/main/scala/spark/PairRDDFunctions.scala
index e7408e4352abfc2e53e20f91039e186ff72c139f..3d1b1ca268c8bdf251a276f6b46feb478f56645d 100644
--- a/core/src/main/scala/spark/PairRDDFunctions.scala
+++ b/core/src/main/scala/spark/PairRDDFunctions.scala
@@ -440,6 +440,23 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
cogroup(other1, other2, defaultPartitioner(self, other1, other2))
}
+ /**
+ * Return an RDD with the pairs from `this` whose keys are not in `other`.
+ *
+ * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
+ * RDD will be <= us.
+ */
+ def subtractByKey[W: ClassManifest](other: RDD[(K, W)]): RDD[(K, V)] =
+ subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.size)))
+
+ /** Return an RDD with the pairs from `this` whose keys are not in `other`. */
+ def subtractByKey[W: ClassManifest](other: RDD[(K, W)], numPartitions: Int): RDD[(K, V)] =
+ subtractByKey(other, new HashPartitioner(numPartitions))
+
+ /** Return an RDD with the pairs from `this` whose keys are not in `other`. */
+ def subtractByKey[W: ClassManifest](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] =
+ new SubtractedRDD[K, V, W](self, other, p)
+
/**
* Return the list of values in the RDD for key `key`. This operation is done efficiently if the
* RDD has a known partitioner by only searching the partition that the key maps to.
diff --git a/core/src/main/scala/spark/RDD.scala b/core/src/main/scala/spark/RDD.scala
index 584efa8adf5f3877c914cf54b0fd22eefc325963..9bd8a0f98daa8a47c2b625f68eb1af4b02c0629a 100644
--- a/core/src/main/scala/spark/RDD.scala
+++ b/core/src/main/scala/spark/RDD.scala
@@ -420,7 +420,23 @@ abstract class RDD[T: ClassManifest](
/**
* Return an RDD with the elements from `this` that are not in `other`.
*/
- def subtract(other: RDD[T], p: Partitioner): RDD[T] = new SubtractedRDD[T](this, other, p)
+ def subtract(other: RDD[T], p: Partitioner): RDD[T] = {
+ if (partitioner == Some(p)) {
+ // Our partitioner knows how to handle T (which, since we have a partitioner, is
+ // really (K, V)) so make a new Partitioner that will de-tuple our fake tuples
+ val p2 = new Partitioner() {
+ override def numPartitions = p.numPartitions
+ override def getPartition(k: Any) = p.getPartition(k.asInstanceOf[(Any, _)]._1)
+ }
+ // Unfortunately, since we're making a new p2, we'll get ShuffleDependencies
+ // anyway, and when calling .keys, will not have a partitioner set, even though
+ // the SubtractedRDD will, thanks to p2's de-tupled partitioning, already be
+ // partitioned by the right/real keys (e.g. p).
+ this.map(x => (x, null)).subtractByKey(other.map((_, null)), p2).keys
+ } else {
+ this.map(x => (x, null)).subtractByKey(other.map((_, null)), p).keys
+ }
+ }
/**
* Reduces the elements of this RDD using the specified commutative and associative binary operator.
diff --git a/core/src/main/scala/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/spark/rdd/SubtractedRDD.scala
index 43ec90cac5a95111534a7e15d9638c438b71dde5..0a025610626779209c575caadbcd2067fb34a232 100644
--- a/core/src/main/scala/spark/rdd/SubtractedRDD.scala
+++ b/core/src/main/scala/spark/rdd/SubtractedRDD.scala
@@ -1,7 +1,8 @@
package spark.rdd
-import java.util.{HashSet => JHashSet}
+import java.util.{HashMap => JHashMap}
import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
import spark.RDD
import spark.Partitioner
import spark.Dependency
@@ -27,10 +28,10 @@ import spark.OneToOneDependency
* you can use `rdd1`'s partitioner/partition size and not worry about running
* out of memory because of the size of `rdd2`.
*/
-private[spark] class SubtractedRDD[T: ClassManifest](
- @transient var rdd1: RDD[T],
- @transient var rdd2: RDD[T],
- part: Partitioner) extends RDD[T](rdd1.context, Nil) {
+private[spark] class SubtractedRDD[K: ClassManifest, V: ClassManifest, W: ClassManifest](
+ @transient var rdd1: RDD[(K, V)],
+ @transient var rdd2: RDD[(K, W)],
+ part: Partitioner) extends RDD[(K, V)](rdd1.context, Nil) {
override def getDependencies: Seq[Dependency[_]] = {
Seq(rdd1, rdd2).map { rdd =>
@@ -39,26 +40,7 @@ private[spark] class SubtractedRDD[T: ClassManifest](
new OneToOneDependency(rdd)
} else {
logInfo("Adding shuffle dependency with " + rdd)
- val mapSideCombinedRDD = rdd.mapPartitions(i => {
- val set = new JHashSet[T]()
- while (i.hasNext) {
- set.add(i.next)
- }
- set.iterator
- }, true)
- // ShuffleDependency requires a tuple (k, v), which it will partition by k.
- // We need this to partition to map to the same place as the k for
- // OneToOneDependency, which means:
- // - for already-tupled RDD[(A, B)], into getPartition(a)
- // - for non-tupled RDD[C], into getPartition(c)
- val part2 = new Partitioner() {
- def numPartitions = part.numPartitions
- def getPartition(key: Any) = key match {
- case (k, v) => part.getPartition(k)
- case k => part.getPartition(k)
- }
- }
- new ShuffleDependency(mapSideCombinedRDD.map((_, null)), part2)
+ new ShuffleDependency(rdd.asInstanceOf[RDD[(K, Any)]], part)
}
}
}
@@ -81,22 +63,32 @@ private[spark] class SubtractedRDD[T: ClassManifest](
override val partitioner = Some(part)
- override def compute(p: Partition, context: TaskContext): Iterator[T] = {
+ override def compute(p: Partition, context: TaskContext): Iterator[(K, V)] = {
val partition = p.asInstanceOf[CoGroupPartition]
- val set = new JHashSet[T]
- def integrate(dep: CoGroupSplitDep, op: T => Unit) = dep match {
+ val map = new JHashMap[K, ArrayBuffer[V]]
+ def getSeq(k: K): ArrayBuffer[V] = {
+ val seq = map.get(k)
+ if (seq != null) {
+ seq
+ } else {
+ val seq = new ArrayBuffer[V]()
+ map.put(k, seq)
+ seq
+ }
+ }
+ def integrate(dep: CoGroupSplitDep, op: ((K, V)) => Unit) = dep match {
case NarrowCoGroupSplitDep(rdd, _, itsSplit) =>
- for (k <- rdd.iterator(itsSplit, context))
- op(k.asInstanceOf[T])
+ for (t <- rdd.iterator(itsSplit, context))
+ op(t.asInstanceOf[(K, V)])
case ShuffleCoGroupSplitDep(shuffleId) =>
- for ((k, _) <- SparkEnv.get.shuffleFetcher.fetch(shuffleId, partition.index, context.taskMetrics))
- op(k.asInstanceOf[T])
+ for (t <- SparkEnv.get.shuffleFetcher.fetch(shuffleId, partition.index, context.taskMetrics))
+ op(t.asInstanceOf[(K, V)])
}
- // the first dep is rdd1; add all keys to the set
- integrate(partition.deps(0), set.add)
- // the second dep is rdd2; remove all of its keys from the set
- integrate(partition.deps(1), set.remove)
- set.iterator
+ // the first dep is rdd1; add all values to the map
+ integrate(partition.deps(0), t => getSeq(t._1) += t._2)
+ // the second dep is rdd2; remove all of its keys
+ integrate(partition.deps(1), t => map.remove(t._1))
+ map.iterator.map { t => t._2.iterator.map { (t._1, _) } }.flatten
}
override def clearDependencies() {
@@ -105,4 +97,4 @@ private[spark] class SubtractedRDD[T: ClassManifest](
rdd2 = null
}
-}
\ No newline at end of file
+}
diff --git a/core/src/test/scala/spark/ShuffleSuite.scala b/core/src/test/scala/spark/ShuffleSuite.scala
index 8411291b2caa31e86f58bbf17f39fbf68020a669..2b2a90defa4e902a8db7fb5ab2bc13da411b5913 100644
--- a/core/src/test/scala/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/spark/ShuffleSuite.scala
@@ -272,13 +272,39 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
}
// partitionBy so we have a narrow dependency
val a = sc.parallelize(Array((1, "a"), (2, "b"), (3, "c"))).partitionBy(p)
- println(sc.runJob(a, (i: Iterator[(Int, String)]) => i.toList).toList)
// more partitions/no partitioner so a shuffle dependency
val b = sc.parallelize(Array((2, "b"), (3, "cc"), (4, "d")), 4)
val c = a.subtract(b)
assert(c.collect().toSet === Set((1, "a"), (3, "c")))
+ // Ideally we could keep the original partitioner...
+ assert(c.partitioner === None)
+ }
+
+ test("subtractByKey") {
+ sc = new SparkContext("local", "test")
+ val a = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 2)
+ val b = sc.parallelize(Array((2, 20), (3, 30), (4, 40)), 4)
+ val c = a.subtractByKey(b)
+ assert(c.collect().toSet === Set((1, "a"), (1, "a")))
+ assert(c.partitions.size === a.partitions.size)
+ }
+
+ test("subtractByKey with narrow dependency") {
+ sc = new SparkContext("local", "test")
+ // use a deterministic partitioner
+ val p = new Partitioner() {
+ def numPartitions = 5
+ def getPartition(key: Any) = key.asInstanceOf[Int]
+ }
+ // partitionBy so we have a narrow dependency
+ val a = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c"))).partitionBy(p)
+ // more partitions/no partitioner so a shuffle dependency
+ val b = sc.parallelize(Array((2, "b"), (3, "cc"), (4, "d")), 4)
+ val c = a.subtractByKey(b)
+ assert(c.collect().toSet === Set((1, "a"), (1, "a")))
assert(c.partitioner.get === p)
}
+
}
object ShuffleSuite {