diff options
| -rw-r--r-- | core/src/main/scala/spark/PairRDDFunctions.scala | 17 | ||||
| -rw-r--r-- | core/src/main/scala/spark/RDD.scala | 18 | ||||
| -rw-r--r-- | core/src/main/scala/spark/rdd/SubtractedRDD.scala | 68 | ||||
| -rw-r--r-- | core/src/test/scala/spark/ShuffleSuite.scala | 28 |
4 files changed, 91 insertions, 40 deletions
diff --git a/core/src/main/scala/spark/PairRDDFunctions.scala b/core/src/main/scala/spark/PairRDDFunctions.scala index e7408e4352..3d1b1ca268 100644 --- a/core/src/main/scala/spark/PairRDDFunctions.scala +++ b/core/src/main/scala/spark/PairRDDFunctions.scala @@ -441,6 +441,23 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( } /** + * 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 584efa8adf..9bd8a0f98d 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 43ec90cac5..0a02561062 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 8411291b2c..2b2a90defa 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 { |