Add Java API

Add distinct() method to RDD.

Fix bug in DoubleRDDFunctions.

core/src/main/scala/spark/RDD.scala

@@ -112,6 +112,8 @@ abstract class RDD[T: ClassManifest](@transient sc: SparkContext) extends Serial
   
   def filter(f: T => Boolean): RDD[T] = new FilteredRDD(this, sc.clean(f))
 
+  def distinct(): RDD[T] = map(x => (x, "")).reduceByKey((x, y) => x).map(_._1)
+
   def sample(withReplacement: Boolean, fraction: Double, seed: Int): RDD[T] =
     new SampledRDD(this, withReplacement, fraction, seed)
 
@@ -359,6 +361,14 @@ class MappedRDD[U: ClassManifest, T: ClassManifest](
   override def compute(split: Split) = prev.iterator(split).map(f)
 }
 
+class PartitioningPreservingMappedRDD[U: ClassManifest, T: ClassManifest](
+  prev: RDD[T],
+  f: T => U)
+  extends MappedRDD[U, T](prev, f) {
+
+  override val partitioner = prev.partitioner
+}
+
 class FlatMappedRDD[U: ClassManifest, T: ClassManifest](
     prev: RDD[T],
     f: T => TraversableOnce[U])

core/src/main/scala/spark/SparkContext.scala

@@ -48,10 +48,13 @@ import spark.storage.BlockManagerMaster
 class SparkContext(
     master: String,
     frameworkName: String,
-    val sparkHome: String = null,
+    val sparkHome: String,
-    val jars: Seq[String] = Nil)
+    val jars: Seq[String])
   extends Logging {
   
+  def this(master: String, frameworkName: String) = this(master, frameworkName, null, Nil)
+
+
   // Ensure logging is initialized before we spawn any threads
   initLogging()
 

core/src/main/scala/spark/api/java/JavaDoubleRDD.scala

@@ -0,0 +1,63 @@
+package spark.api.java
+
+import spark.RDD
+import spark.SparkContext.doubleRDDToDoubleRDDFunctions
+import spark.api.java.function.{Function => JFunction}
+
+import java.lang.Double
+
+class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, JavaDoubleRDD] {
+
+  val classManifest = implicitly[ClassManifest[Double]]
+
+  lazy val rdd = srdd.map(x => Double.valueOf(x))
+
+  def wrapRDD: (RDD[Double]) => JavaDoubleRDD = rdd => new JavaDoubleRDD(rdd.map(_.doubleValue))
+
+  // Common RDD functions
+
+  import JavaDoubleRDD.fromRDD
+
+  def cache(): JavaDoubleRDD = fromRDD(srdd.cache())
+
+  // first() has to be overriden here in order for its return type to be Double instead of Object.
+  override def first(): Double = srdd.first()
+
+  // Transformations (return a new RDD)
+
+  def distinct() = fromRDD(srdd.distinct())
+
+  def filter(f: JFunction[Double, java.lang.Boolean]) =
+    fromRDD(srdd.filter(x => f(x).booleanValue()))
+
+  def sample(withReplacement: Boolean, fraction: Double, seed: Int) =
+    fromRDD(srdd.sample(withReplacement, fraction, seed))
+
+  def union(other: JavaDoubleRDD) = fromRDD(srdd.union(other.srdd))
+
+  // Double RDD functions
+
+  def sum() = srdd.sum()
+
+  def stats() = srdd.stats()
+
+  def mean() = srdd.mean()
+
+  def variance() = srdd.variance()
+
+  def stdev() = srdd.stdev()
+
+  def meanApprox(timeout: Long, confidence: Double) = srdd.meanApprox(timeout, confidence)
+
+  def meanApprox(timeout: Long) = srdd.meanApprox(timeout)
+
+  def sumApprox(timeout: Long, confidence: Double) = srdd.sumApprox(timeout, confidence)
+
+  def sumApprox(timeout: Long) = srdd.sumApprox(timeout)
+}
+
+object JavaDoubleRDD {
+  def fromRDD(rdd: RDD[scala.Double]): JavaDoubleRDD = new JavaDoubleRDD(rdd)
+
+  implicit def toRDD(rdd: JavaDoubleRDD): RDD[scala.Double] = rdd.srdd
+}

core/src/main/scala/spark/api/java/JavaPairRDD.scala

@@ -0,0 +1,272 @@
+package spark.api.java
+
+import spark.SparkContext.rddToPairRDDFunctions
+import spark.api.java.function.{Function2 => JFunction2}
+import spark.api.java.function.{Function => JFunction}
+import spark.partial.BoundedDouble
+import spark.partial.PartialResult
+import spark._
+
+import java.util.{List => JList}
+import java.util.Comparator
+
+import scala.Tuple2
+import scala.collection.Map
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.mapred.JobConf
+import org.apache.hadoop.mapred.OutputFormat
+import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat}
+import org.apache.hadoop.conf.Configuration
+
+class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManifest[K],
+  implicit val vManifest: ClassManifest[V]) extends JavaRDDLike[(K, V), JavaPairRDD[K, V]] {
+
+  def wrapRDD = JavaPairRDD.fromRDD _
+
+  def classManifest = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K, V]]]
+
+  import JavaPairRDD._
+
+  // Common RDD functions
+
+  def cache() = new JavaPairRDD[K, V](rdd.cache())
+
+  // Transformations (return a new RDD)
+
+  def distinct() = new JavaPairRDD[K, V](rdd.distinct())
+
+  def filter(f: Function[(K, V), java.lang.Boolean]) =
+    new JavaPairRDD[K, V](rdd.filter(x => f(x).booleanValue()))
+
+  def sample(withReplacement: Boolean, fraction: Double, seed: Int) =
+    new JavaPairRDD[K, V](rdd.sample(withReplacement, fraction, seed))
+
+  def union(other: JavaPairRDD[K, V]) = new JavaPairRDD[K, V](rdd.union(other.rdd))
+
+  // first() has to be overridden here so that the generated method has the signature
+  // 'public scala.Tuple2 first()'; if the trait's definition is used,
+  // then the method has the signature 'public java.lang.Object first()',
+  // causing NoSuchMethodErrors at runtime.
+  override def first(): (K, V) = rdd.first()
+
+  // Pair RDD functions
+
+  def combineByKey[C](createCombiner: Function[V, C],
+    mergeValue: JFunction2[C, V, C],
+    mergeCombiners: JFunction2[C, C, C],
+    partitioner: Partitioner): JavaPairRDD[K, C] = {
+    implicit val cm: ClassManifest[C] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[C]]
+    fromRDD(rdd.combineByKey(
+      createCombiner,
+      mergeValue,
+      mergeCombiners,
+      partitioner
+    ))
+  }
+
+  def combineByKey[C](createCombiner: JFunction[V, C],
+    mergeValue: JFunction2[C, V, C],
+    mergeCombiners: JFunction2[C, C, C],
+    numSplits: Int): JavaPairRDD[K, C] =
+    combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numSplits))
+
+  def reduceByKey(partitioner: Partitioner, func: JFunction2[V, V, V]): JavaPairRDD[K, V] =
+    fromRDD(rdd.reduceByKey(partitioner, func))
+
+  def reduceByKeyLocally(func: JFunction2[V, V, V]) = {
+    rdd.reduceByKeyLocally(func)
+  }
+
+  def countByKey() = rdd.countByKey()
+
+  def countByKeyApprox(timeout: Long): PartialResult[java.util.Map[K, BoundedDouble]] =
+    rdd.countByKeyApprox(timeout).map(mapAsJavaMap)
+
+  def countByKeyApprox(timeout: Long, confidence: Double = 0.95)
+  : PartialResult[java.util.Map[K, BoundedDouble]] =
+    rdd.countByKeyApprox(timeout, confidence).map(mapAsJavaMap)
+
+  def reduceByKey(func: JFunction2[V, V, V], numSplits: Int): JavaPairRDD[K, V] =
+    fromRDD(rdd.reduceByKey(func, numSplits))
+
+  def groupByKey(partitioner: Partitioner): JavaPairRDD[K, JList[V]] =
+    fromRDD(groupByResultToJava(rdd.groupByKey(partitioner)))
+
+  def groupByKey(numSplits: Int): JavaPairRDD[K, JList[V]] =
+    fromRDD(groupByResultToJava(rdd.groupByKey(numSplits)))
+
+  def partitionBy(partitioner: Partitioner): JavaPairRDD[K, V] =
+    fromRDD(rdd.partitionBy(partitioner))
+
+  def join[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (V, W)] =
+    fromRDD(rdd.join(other, partitioner))
+
+  def leftOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
+  : JavaPairRDD[K, (V, Option[W])] =
+    fromRDD(rdd.leftOuterJoin(other, partitioner))
+
+  def rightOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
+  : JavaPairRDD[K, (Option[V], W)] =
+    fromRDD(rdd.rightOuterJoin(other, partitioner))
+
+  def combineByKey[C](createCombiner: JFunction[V, C],
+    mergeValue: JFunction2[C, V, C],
+    mergeCombiners: JFunction2[C, C, C]): JavaPairRDD[K, C] = {
+    implicit val cm: ClassManifest[C] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[C]]
+    fromRDD(combineByKey(createCombiner, mergeValue, mergeCombiners))
+  }
+
+  def reduceByKey(func: JFunction2[V, V, V]): JavaPairRDD[K, V] = {
+    val partitioner = rdd.defaultPartitioner(rdd)
+    fromRDD(reduceByKey(partitioner, func))
+  }
+
+  def groupByKey(): JavaPairRDD[K, JList[V]] =
+    fromRDD(groupByResultToJava(rdd.groupByKey()))
+
+  def join[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, W)] =
+    fromRDD(rdd.join(other))
+
+  def join[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (V, W)] =
+    fromRDD(rdd.join(other, numSplits))
+
+  def leftOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, Option[W])] =
+    fromRDD(rdd.leftOuterJoin(other))
+
+  def leftOuterJoin[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (V, Option[W])] =
+    fromRDD(rdd.leftOuterJoin(other, numSplits))
+
+  def rightOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Option[V], W)] =
+    fromRDD(rdd.rightOuterJoin(other))
+
+  def rightOuterJoin[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (Option[V], W)] =
+    fromRDD(rdd.rightOuterJoin(other, numSplits))
+
+  def collectAsMap(): Map[K, V] = rdd.collectAsMap()
+
+  def mapValues[U](f: Function[V, U]): JavaPairRDD[K, U] = {
+    implicit val cm: ClassManifest[U] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]]
+    fromRDD(rdd.mapValues(f))
+  }
+
+  def flatMapValues[U](f: JFunction[V, java.lang.Iterable[U]]): JavaPairRDD[K, U] = {
+    import scala.collection.JavaConverters._
+    def fn = (x: V) => f.apply(x).asScala
+    implicit val cm: ClassManifest[U] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]]
+    fromRDD(rdd.flatMapValues(fn))
+  }
+
+  def cogroup[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
+  : JavaPairRDD[K, (JList[V], JList[W])] =
+    fromRDD(cogroupResultToJava(rdd.cogroup(other, partitioner)))
+
+  def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], partitioner: Partitioner)
+  : JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] =
+    fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, partitioner)))
+
+  def cogroup[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JList[V], JList[W])] =
+    fromRDD(cogroupResultToJava(rdd.cogroup(other)))
+
+  def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2])
+  : JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] =
+    fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2)))
+
+  def cogroup[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (JList[V], JList[W])]
+  = fromRDD(cogroupResultToJava(rdd.cogroup(other, numSplits)))
+
+  def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], numSplits: Int)
+  : JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] =
+    fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, numSplits)))
+
+  def groupWith[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JList[V], JList[W])] =
+    fromRDD(cogroupResultToJava(rdd.groupWith(other)))
+
+  def groupWith[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2])
+  : JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] =
+    fromRDD(cogroupResult2ToJava(rdd.groupWith(other1, other2)))
+
+  def lookup(key: K): JList[V] = seqAsJavaList(rdd.lookup(key))
+
+  def saveAsHadoopFile[F <: OutputFormat[_, _]](
+    path: String,
+    keyClass: Class[_],
+    valueClass: Class[_],
+    outputFormatClass: Class[F],
+    conf: JobConf) {
+    rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, conf)
+  }
+
+  def saveAsHadoopFile[F <: OutputFormat[_, _]](
+    path: String,
+    keyClass: Class[_],
+    valueClass: Class[_],
+    outputFormatClass: Class[F]) {
+    rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass)
+  }
+
+  def saveAsNewAPIHadoopFile[F <: NewOutputFormat[_, _]](
+    path: String,
+    keyClass: Class[_],
+    valueClass: Class[_],
+    outputFormatClass: Class[F],
+    conf: Configuration) {
+    rdd.saveAsNewAPIHadoopFile(path, keyClass, valueClass, outputFormatClass, conf)
+  }
+
+  def saveAsNewAPIHadoopFile[F <: NewOutputFormat[_, _]](
+    path: String,
+    keyClass: Class[_],
+    valueClass: Class[_],
+    outputFormatClass: Class[F]) {
+    rdd.saveAsNewAPIHadoopFile(path, keyClass, valueClass, outputFormatClass)
+  }
+
+  def saveAsHadoopDataset(conf: JobConf) {
+    rdd.saveAsHadoopDataset(conf)
+  }
+
+
+  // Ordered RDD Functions
+  def sortByKey(): JavaPairRDD[K, V] = sortByKey(true)
+
+  def sortByKey(ascending: Boolean): JavaPairRDD[K, V] = {
+    val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[K]]
+    sortByKey(comp, true)
+  }
+
+  def sortByKey(comp: Comparator[K]): JavaPairRDD[K, V] = sortByKey(comp, true)
+
+  def sortByKey(comp: Comparator[K], ascending: Boolean): JavaPairRDD[K, V] = {
+    class KeyOrdering(val a: K) extends Ordered[K] {
+      override def compare(b: K) = comp.compare(a, b)
+    }
+    implicit def toOrdered(x: K): Ordered[K] = new KeyOrdering(x)
+    fromRDD(new OrderedRDDFunctions(rdd).sortByKey(ascending))
+  }
+}
+
+object JavaPairRDD {
+  def groupByResultToJava[K, T](rdd: RDD[(K, Seq[T])])(implicit kcm: ClassManifest[K],
+    vcm: ClassManifest[T]): RDD[(K, JList[T])] =
+    new PartitioningPreservingMappedRDD(rdd, (x: (K, Seq[T])) => (x._1, seqAsJavaList(x._2)))
+
+  def cogroupResultToJava[W, K, V](rdd: RDD[(K, (Seq[V], Seq[W]))])
+  : RDD[(K, (JList[V], JList[W]))] = new PartitioningPreservingMappedRDD(rdd,
+    (x: (K, (Seq[V], Seq[W]))) => (x._1, (seqAsJavaList(x._2._1), seqAsJavaList(x._2._2))))
+
+  def cogroupResult2ToJava[W1, W2, K, V](rdd: RDD[(K, (Seq[V], Seq[W1], Seq[W2]))])
+  : RDD[(K, (JList[V], JList[W1], JList[W2]))] = new PartitioningPreservingMappedRDD(rdd,
+    (x: (K, (Seq[V], Seq[W1], Seq[W2]))) => (x._1, (seqAsJavaList(x._2._1),
+      seqAsJavaList(x._2._2),
+      seqAsJavaList(x._2._3))))
+
+  def fromRDD[K: ClassManifest, V: ClassManifest](rdd: RDD[(K, V)]): JavaPairRDD[K, V] =
+    new JavaPairRDD[K, V](rdd)
+
+  implicit def toRDD[K, V](rdd: JavaPairRDD[K, V]): RDD[(K, V)] = rdd.rdd
+}

core/src/main/scala/spark/api/java/JavaRDD.scala

@@ -0,0 +1,34 @@
+package spark.api.java
+
+import spark._
+import spark.api.java.function.{Function => JFunction}
+
+class JavaRDD[T](val rdd: RDD[T])(implicit val classManifest: ClassManifest[T]) extends
+JavaRDDLike[T, JavaRDD[T]] {
+
+  def wrapRDD = JavaRDD.fromRDD
+
+  // Common RDD functions
+
+  def cache() = wrapRDD(rdd.cache())
+
+  // Transformations (return a new RDD)
+
+  def distinct() = wrapRDD(rdd.distinct())
+
+  def filter(f: JFunction[T, java.lang.Boolean]) = wrapRDD(rdd.filter((x => f(x).booleanValue())))
+
+  def sample(withReplacement: Boolean, fraction: Double, seed: Int) =
+    wrapRDD(rdd.sample(withReplacement, fraction, seed))
+
+  def union(other: JavaRDD[T]) = wrapRDD(rdd.union(other.rdd))
+
+}
+
+object JavaRDD {
+
+  implicit def fromRDD[T: ClassManifest](rdd: RDD[T]): JavaRDD[T] = new JavaRDD[T](rdd)
+
+  implicit def toRDD[T](rdd: JavaRDD[T]): RDD[T] = rdd.rdd
+}
+

core/src/main/scala/spark/api/java/JavaRDDLike.scala

@@ -0,0 +1,149 @@
+package spark.api.java
+
+import spark.{PartitioningPreservingMappedRDD, Split, RDD}
+import spark.api.java.JavaPairRDD._
+import spark.api.java.function.{Function2 => JFunction2, Function => JFunction, _}
+import spark.partial.{PartialResult, BoundedDouble}
+
+import java.util.{List => JList}
+
+import scala.collection.JavaConversions._
+import java.lang
+import scala.Tuple2
+
+trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
+  def wrapRDD: (RDD[T] => This)
+
+  implicit def classManifest: ClassManifest[T]
+
+  def rdd: RDD[T]
+
+  def context = rdd.context
+
+  def id = rdd.id
+
+  def getStorageLevel = rdd.getStorageLevel
+
+  def iterator(split: Split): java.util.Iterator[T] = asJavaIterator(rdd.iterator(split))
+
+  // Transformations (return a new RDD)
+
+  def map[R](f: JFunction[T, R]): JavaRDD[R] =
+    new JavaRDD(rdd.map(f)(f.returnType()))(f.returnType())
+
+  def map[R](f: DoubleFunction[T]): JavaDoubleRDD =
+    new JavaDoubleRDD(rdd.map(x => f(x).doubleValue()))
+
+  def map[K2, V2](f: PairFunction[T, K2, V2]): JavaPairRDD[K2, V2] = {
+    def cm = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K2, V2]]]
+    new JavaPairRDD(rdd.map(f)(cm))(f.keyType(), f.valueType())
+  }
+
+  def flatMap[U](f: FlatMapFunction[T, U]): JavaRDD[U] = {
+    import scala.collection.JavaConverters._
+    def fn = (x: T) => f.apply(x).asScala
+    JavaRDD.fromRDD(rdd.flatMap(fn)(f.elementType()))(f.elementType())
+  }
+
+  def flatMap(f: DoubleFlatMapFunction[T]): JavaDoubleRDD = {
+    import scala.collection.JavaConverters._
+    def fn = (x: T) => f.apply(x).asScala
+    new JavaDoubleRDD(new PartitioningPreservingMappedRDD(rdd.flatMap(fn),
+      ((x: java.lang.Double) => x.doubleValue())))
+  }
+
+  def flatMap[K, V](f: PairFlatMapFunction[T, K, V]): JavaPairRDD[K, V] = {
+    import scala.collection.JavaConverters._
+    def fn = (x: T) => f.apply(x).asScala
+    def cm = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K, V]]]
+    new JavaPairRDD(rdd.flatMap(fn)(cm))(f.keyType(), f.valueType())
+  }
+
+  def cartesian[U](other: JavaRDDLike[U, _]): JavaPairRDD[T, U] =
+    JavaPairRDD.fromRDD(rdd.cartesian(other.rdd)(other.classManifest))(classManifest,
+      other.classManifest)
+
+  def groupBy[K](f: JFunction[T, K]): JavaPairRDD[K, JList[T]] = {
+    implicit val kcm: ClassManifest[K] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]]
+    implicit val vcm: ClassManifest[JList[T]] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[JList[T]]]
+    JavaPairRDD.fromRDD(groupByResultToJava(rdd.groupBy(f)(f.returnType)))(kcm, vcm)
+  }
+
+  def groupBy[K](f: JFunction[T, K], numSplits: Int): JavaPairRDD[K, JList[T]] = {
+    implicit val kcm: ClassManifest[K] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]]
+    implicit val vcm: ClassManifest[JList[T]] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[JList[T]]]
+    JavaPairRDD.fromRDD(groupByResultToJava(rdd.groupBy(f, numSplits)(f.returnType)))(kcm, vcm)
+  }
+
+  def pipe(command: String): JavaRDD[String] = rdd.pipe(command)
+
+  def pipe(command: JList[String]): JavaRDD[String] =
+    rdd.pipe(asScalaBuffer(command))
+
+  def pipe(command: JList[String], env: java.util.Map[String, String]): JavaRDD[String] =
+    rdd.pipe(asScalaBuffer(command), mapAsScalaMap(env))
+
+  // Actions (launch a job to return a value to the user program)
+
+  def foreach(f: VoidFunction[T]) {
+    val cleanF = rdd.context.clean(f)
+    rdd.foreach(cleanF)
+  }
+
+  def collect(): JList[T] = {
+    import scala.collection.JavaConversions._
+    val arr: java.util.Collection[T] = rdd.collect().toSeq
+    new java.util.ArrayList(arr)
+  }
+
+  def reduce(f: JFunction2[T, T, T]): T = rdd.reduce(f)
+
+  def fold(zeroValue: T)(f: JFunction2[T, T, T]): T =
+    rdd.fold(zeroValue)(f)
+
+  def aggregate[U](zeroValue: U)(seqOp: JFunction2[U, T, U],
+    combOp: JFunction2[U, U, U]): U =
+    rdd.aggregate(zeroValue)(seqOp, combOp)(seqOp.returnType)
+
+  def count() = rdd.count()
+
+  def countApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble] =
+    rdd.countApprox(timeout, confidence)
+
+  def countApprox(timeout: Long): PartialResult[BoundedDouble] =
+    rdd.countApprox(timeout)
+
+  def countByValue(): java.util.Map[T, java.lang.Long] =
+    mapAsJavaMap(rdd.countByValue().map((x => (x._1, new lang.Long(x._2)))))
+
+  def countByValueApprox(
+    timeout: Long,
+    confidence: Double
+    ): PartialResult[java.util.Map[T, BoundedDouble]] =
+    rdd.countByValueApprox(timeout, confidence).map(mapAsJavaMap)
+
+  def countByValueApprox(timeout: Long): PartialResult[java.util.Map[T, BoundedDouble]] =
+    rdd.countByValueApprox(timeout).map(mapAsJavaMap)
+
+  def take(num: Int): JList[T] = {
+    import scala.collection.JavaConversions._
+    val arr: java.util.Collection[T] = rdd.take(num).toSeq
+    new java.util.ArrayList(arr)
+  }
+
+  def takeSample(withReplacement: Boolean, num: Int, seed: Int): JList[T] = {
+    import scala.collection.JavaConversions._
+    val arr: java.util.Collection[T] = rdd.takeSample(withReplacement, num, seed).toSeq
+    new java.util.ArrayList(arr)
+  }
+
+  def first(): T = rdd.first()
+
+  def saveAsTextFile(path: String) = rdd.saveAsTextFile(path)
+
+  def saveAsObjectFile(path: String) = rdd.saveAsObjectFile(path)
+}

core/src/main/scala/spark/api/java/JavaSparkContext.scala

@@ -0,0 +1,219 @@
+package spark.api.java
+
+import spark.{AccumulatorParam, RDD, SparkContext}
+import spark.SparkContext.IntAccumulatorParam
+import spark.SparkContext.DoubleAccumulatorParam
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.mapred.InputFormat
+import org.apache.hadoop.mapred.JobConf
+
+import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
+
+
+import scala.collection.JavaConversions
+
+class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWorkaround {
+
+  def this(master: String, frameworkName: String) = this(new SparkContext(master, frameworkName))
+
+  val env = sc.env
+
+  def parallelize[T](list: java.util.List[T], numSlices: Int): JavaRDD[T] = {
+    implicit val cm: ClassManifest[T] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]]
+    sc.parallelize(JavaConversions.asScalaBuffer(list), numSlices)
+  }
+
+  def parallelize[T](list: java.util.List[T]): JavaRDD[T] =
+    parallelize(list, sc.defaultParallelism)
+
+
+  def parallelizePairs[K, V](list: java.util.List[Tuple2[K, V]], numSlices: Int)
+  : JavaPairRDD[K, V] = {
+    implicit val kcm: ClassManifest[K] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]]
+    implicit val vcm: ClassManifest[V] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V]]
+    JavaPairRDD.fromRDD(sc.parallelize(JavaConversions.asScalaBuffer(list), numSlices))
+  }
+
+  def parallelizePairs[K, V](list: java.util.List[Tuple2[K, V]]): JavaPairRDD[K, V] =
+    parallelizePairs(list, sc.defaultParallelism)
+
+  def parallelizeDoubles(list: java.util.List[java.lang.Double], numSlices: Int): JavaDoubleRDD =
+    JavaDoubleRDD.fromRDD(sc.parallelize(JavaConversions.asScalaBuffer(list).map(_.doubleValue()),
+      numSlices))
+
+  def parallelizeDoubles(list: java.util.List[java.lang.Double]): JavaDoubleRDD =
+    parallelizeDoubles(list, sc.defaultParallelism)
+
+  def textFile(path: String): JavaRDD[String] = sc.textFile(path)
+
+  def textFile(path: String, minSplits: Int): JavaRDD[String] = sc.textFile(path, minSplits)
+
+  /**Get an RDD for a Hadoop SequenceFile with given key and value types */
+  def sequenceFile[K, V](path: String,
+    keyClass: Class[K],
+    valueClass: Class[V],
+    minSplits: Int
+    ): JavaPairRDD[K, V] = {
+    implicit val kcm = ClassManifest.fromClass(keyClass)
+    implicit val vcm = ClassManifest.fromClass(valueClass)
+    new JavaPairRDD(sc.sequenceFile(path, keyClass, valueClass, minSplits))
+  }
+
+  def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V]):
+  JavaPairRDD[K, V] = {
+    implicit val kcm = ClassManifest.fromClass(keyClass)
+    implicit val vcm = ClassManifest.fromClass(valueClass)
+    new JavaPairRDD(sc.sequenceFile(path, keyClass, valueClass))
+  }
+
+  /**
+   * Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and
+   * BytesWritable values that contain a serialized partition. This is still an experimental storage
+   * format and may not be supported exactly as is in future Spark releases. It will also be pretty
+   * slow if you use the default serializer (Java serialization), though the nice thing about it is
+   * that there's very little effort required to save arbitrary objects.
+   */
+  def objectFile[T](path: String, minSplits: Int): JavaRDD[T] = {
+    implicit val cm: ClassManifest[T] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]]
+    sc.objectFile(path, minSplits)(cm)
+  }
+
+  def objectFile[T](path: String): JavaRDD[T] = {
+    implicit val cm: ClassManifest[T] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]]
+    sc.objectFile(path)(cm)
+  }
+
+  /**
+   * Get an RDD for a Hadoop-readable dataset from a Hadooop JobConf giving its InputFormat and any
+   * other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable,
+   * etc).
+   */
+  def hadoopRDD[K, V, F <: InputFormat[K, V]](
+    conf: JobConf,
+    inputFormatClass: Class[F],
+    keyClass: Class[K],
+    valueClass: Class[V],
+    minSplits: Int
+    ): JavaPairRDD[K, V] = {
+    implicit val kcm = ClassManifest.fromClass(keyClass)
+    implicit val vcm = ClassManifest.fromClass(valueClass)
+    new JavaPairRDD(sc.hadoopRDD(conf, inputFormatClass, keyClass, valueClass, minSplits))
+  }
+
+  def hadoopRDD[K, V, F <: InputFormat[K, V]](
+    conf: JobConf,
+    inputFormatClass: Class[F],
+    keyClass: Class[K],
+    valueClass: Class[V]
+    ): JavaPairRDD[K, V] = {
+    implicit val kcm = ClassManifest.fromClass(keyClass)
+    implicit val vcm = ClassManifest.fromClass(valueClass)
+    new JavaPairRDD(sc.hadoopRDD(conf, inputFormatClass, keyClass, valueClass))
+  }
+
+  /**Get an RDD for a Hadoop file with an arbitrary InputFormat */
+  def hadoopFile[K, V, F <: InputFormat[K, V]](
+    path: String,
+    inputFormatClass: Class[F],
+    keyClass: Class[K],
+    valueClass: Class[V],
+    minSplits: Int
+    ): JavaPairRDD[K, V] = {
+    implicit val kcm = ClassManifest.fromClass(keyClass)
+    implicit val vcm = ClassManifest.fromClass(valueClass)
+    new JavaPairRDD(sc.hadoopFile(path, inputFormatClass, keyClass, valueClass, minSplits))
+  }
+
+  def hadoopFile[K, V, F <: InputFormat[K, V]](
+    path: String,
+    inputFormatClass: Class[F],
+    keyClass: Class[K],
+    valueClass: Class[V]
+    ): JavaPairRDD[K, V] = {
+    implicit val kcm = ClassManifest.fromClass(keyClass)
+    implicit val vcm = ClassManifest.fromClass(valueClass)
+    new JavaPairRDD(sc.hadoopFile(path,
+      inputFormatClass, keyClass, valueClass))
+  }
+
+  /**
+   * Get an RDD for a given Hadoop file with an arbitrary new API InputFormat
+   * and extra configuration options to pass to the input format.
+   */
+  def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]](
+    path: String,
+    fClass: Class[F],
+    kClass: Class[K],
+    vClass: Class[V],
+    conf: Configuration): JavaPairRDD[K, V] = {
+    implicit val kcm = ClassManifest.fromClass(kClass)
+    implicit val vcm = ClassManifest.fromClass(vClass)
+    new JavaPairRDD(sc.newAPIHadoopFile(path, fClass, kClass, vClass, conf))
+  }
+
+  /**
+   * Get an RDD for a given Hadoop file with an arbitrary new API InputFormat
+   * and extra configuration options to pass to the input format.
+   */
+  def newAPIHadoopRDD[K, V, F <: NewInputFormat[K, V]](
+    conf: Configuration,
+    fClass: Class[F],
+    kClass: Class[K],
+    vClass: Class[V]): JavaPairRDD[K, V] = {
+    implicit val kcm = ClassManifest.fromClass(kClass)
+    implicit val vcm = ClassManifest.fromClass(vClass)
+    new JavaPairRDD(sc.newAPIHadoopRDD(conf, fClass, kClass, vClass))
+  }
+
+  @Override
+  def union[T](jrdds: java.util.List[JavaRDD[T]]): JavaRDD[T] = {
+    val rdds: Seq[RDD[T]] = asScalaBuffer(jrdds).map(_.rdd)
+    implicit val cm: ClassManifest[T] = jrdds.head.classManifest
+    sc.union(rdds: _*)(cm)
+  }
+
+  @Override
+  def union[K, V](jrdds: java.util.List[JavaPairRDD[K, V]]): JavaPairRDD[K, V] = {
+    val rdds: Seq[RDD[(K, V)]] = asScalaBuffer(jrdds).map(_.rdd)
+    implicit val cm: ClassManifest[(K, V)] = jrdds.head.classManifest
+    implicit val kcm: ClassManifest[K] = jrdds.head.kManifest
+    implicit val vcm: ClassManifest[V] = jrdds.head.vManifest
+    new JavaPairRDD(sc.union(rdds: _*)(cm))(kcm, vcm)
+  }
+
+  @Override
+  def union(jrdds: java.util.List[JavaDoubleRDD]): JavaDoubleRDD = {
+    val rdds: Seq[RDD[Double]] = asScalaBuffer(jrdds).map(_.srdd)
+    new JavaDoubleRDD(sc.union(rdds: _*))
+  }
+
+  def intAccumulator(initialValue: Int) = sc.accumulator(initialValue)(IntAccumulatorParam)
+
+  def doubleAccumulator(initialValue: Double) =
+    sc.accumulator(initialValue)(DoubleAccumulatorParam)
+
+  def accumulator[T](initialValue: T, accumulatorParam: AccumulatorParam[T]) =
+    sc.accumulator(initialValue)(accumulatorParam)
+
+  def broadcast[T](value: T) = sc.broadcast(value)
+
+  def stop() {
+    sc.stop()
+  }
+
+  def getSparkHome() = sc.getSparkHome()
+}
+
+object JavaSparkContext {
+  implicit def fromSparkContext(sc: SparkContext): JavaSparkContext = new JavaSparkContext(sc)
+
+  implicit def toSparkContext(jsc: JavaSparkContext): SparkContext = jsc.sc
+}

core/src/main/scala/spark/api/java/JavaSparkContextVarargsWorkaround.java

@@ -0,0 +1,25 @@
+package spark.api.java;
+
+import java.util.Arrays;
+import java.util.List;
+
+// See
+// http://scala-programming-language.1934581.n4.nabble.com/Workaround-for-implementing-java-varargs-in-2-7-2-final-tp1944767p1944772.html
+abstract class JavaSparkContextVarargsWorkaround {
+  public <T> JavaRDD<T> union(JavaRDD<T> ... rdds) {
+    return union(Arrays.asList(rdds));
+  }
+
+  public JavaDoubleRDD union(JavaDoubleRDD ... rdds) {
+    return union(Arrays.asList(rdds));
+  }
+
+  public <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V> ... rdds) {
+    return union(Arrays.asList(rdds));
+  }
+
+  abstract public <T> JavaRDD<T> union(List<JavaRDD<T>> rdds);
+  abstract public JavaDoubleRDD union(List<JavaDoubleRDD> rdds);
+  abstract public <K, V> JavaPairRDD<K, V> union(List<JavaPairRDD<K, V>> rdds);
+
+}

core/src/main/scala/spark/api/java/function/DoubleFlatMapFunction.java

@@ -0,0 +1,13 @@
+package spark.api.java.function;
+
+
+import scala.runtime.AbstractFunction1;
+
+import java.io.Serializable;
+
+// DoubleFlatMapFunction does not extend FlatMapFunction because flatMap is
+// overloaded for both FlatMapFunction and DoubleFlatMapFunction.
+public abstract class DoubleFlatMapFunction<T> extends AbstractFunction1<T, Iterable<Double>>
+  implements Serializable {
+  public abstract Iterable<Double> apply(T t);
+}

core/src/main/scala/spark/api/java/function/DoubleFunction.java

@@ -0,0 +1,13 @@
+package spark.api.java.function;
+
+
+import scala.runtime.AbstractFunction1;
+
+import java.io.Serializable;
+
+// DoubleFunction does not extend Function because some UDF functions, like map,
+// are overloaded for both Function and DoubleFunction.
+public abstract class DoubleFunction<T> extends AbstractFunction1<T, Double>
+  implements Serializable {
+  public abstract Double apply(T t);
+}

core/src/main/scala/spark/api/java/function/FlatMapFunction.scala

@@ -0,0 +1,7 @@
+package spark.api.java.function
+
+abstract class FlatMapFunction[T, R] extends Function[T, java.lang.Iterable[R]] {
+  def apply(x: T) : java.lang.Iterable[R]
+
+  def elementType() : ClassManifest[R] = ClassManifest.Any.asInstanceOf[ClassManifest[R]]
+}

core/src/main/scala/spark/api/java/function/Function.java

@@ -0,0 +1,21 @@
+package spark.api.java.function;
+
+import scala.reflect.ClassManifest;
+import scala.reflect.ClassManifest$;
+import scala.runtime.AbstractFunction1;
+
+import java.io.Serializable;
+
+
+/**
+ * Base class for functions whose return types do not have special RDDs; DoubleFunction is
+ * handled separately, to allow DoubleRDDs to be constructed when mapping RDDs to doubles.
+ */
+public abstract class Function<T, R> extends AbstractFunction1<T, R> implements Serializable {
+  public abstract R apply(T t);
+
+  public ClassManifest<R> returnType() {
+    return (ClassManifest<R>) ClassManifest$.MODULE$.fromClass(Object.class);
+  }
+}
+

core/src/main/scala/spark/api/java/function/Function2.java

@@ -0,0 +1,17 @@
+package spark.api.java.function;
+
+import scala.reflect.ClassManifest;
+import scala.reflect.ClassManifest$;
+import scala.runtime.AbstractFunction2;
+
+import java.io.Serializable;
+
+public abstract class Function2<T1, T2, R> extends AbstractFunction2<T1, T2, R>
+  implements Serializable {
+  public ClassManifest<R> returnType() {
+    return (ClassManifest<R>) ClassManifest$.MODULE$.fromClass(Object.class);
+  }
+
+  public abstract R apply(T1 t1, T2 t2);
+}
+

core/src/main/scala/spark/api/java/function/PairFlatMapFunction.java

@@ -0,0 +1,25 @@
+package spark.api.java.function;
+
+import scala.Tuple2;
+import scala.reflect.ClassManifest;
+import scala.reflect.ClassManifest$;
+import scala.runtime.AbstractFunction1;
+
+import java.io.Serializable;
+
+// PairFlatMapFunction does not extend FlatMapFunction because flatMap is
+// overloaded for both FlatMapFunction and PairFlatMapFunction.
+public abstract class PairFlatMapFunction<T, K, V> extends AbstractFunction1<T, Iterable<Tuple2<K,
+  V>>> implements Serializable {
+
+  public ClassManifest<K> keyType() {
+    return (ClassManifest<K>) ClassManifest$.MODULE$.fromClass(Object.class);
+  }
+
+  public ClassManifest<V> valueType() {
+    return (ClassManifest<V>) ClassManifest$.MODULE$.fromClass(Object.class);
+  }
+
+  public abstract Iterable<Tuple2<K, V>> apply(T t);
+
+}

core/src/main/scala/spark/api/java/function/PairFunction.java

@@ -0,0 +1,25 @@
+package spark.api.java.function;
+
+import scala.Tuple2;
+import scala.reflect.ClassManifest;
+import scala.reflect.ClassManifest$;
+import scala.runtime.AbstractFunction1;
+
+import java.io.Serializable;
+
+// PairFunction does not extend Function because some UDF functions, like map,
+// are overloaded for both Function and PairFunction.
+public abstract class PairFunction<T, K, V> extends AbstractFunction1<T, Tuple2<K,
+  V>> implements Serializable {
+
+  public ClassManifest<K> keyType() {
+    return (ClassManifest<K>) ClassManifest$.MODULE$.fromClass(Object.class);
+  }
+
+  public ClassManifest<V> valueType() {
+    return (ClassManifest<V>) ClassManifest$.MODULE$.fromClass(Object.class);
+  }
+
+  public abstract Tuple2<K, V> apply(T t);
+
+}

core/src/main/scala/spark/api/java/function/VoidFunction.scala

@@ -0,0 +1,12 @@
+package spark.api.java.function
+
+// This allows Java users to write void methods without having to return Unit.
+abstract class VoidFunction[T] extends Serializable {
+  def apply(t: T) : Unit
+}
+
+// VoidFunction cannot extend AbstractFunction1 (because that would force users to explicitly
+// return Unit), so it is implicitly converted to a Function1[T, Unit]:
+object VoidFunction {
+  implicit def toFunction[T](f: VoidFunction[T]) : Function1[T, Unit] = ((x : T) => f(x))
+}

core/src/main/scala/spark/partial/PartialResult.scala

@@ -57,6 +57,32 @@ class PartialResult[R](initialVal: R, isFinal: Boolean) {
     }
   }
 
+  /**
+   * Transform this PartialResult into a PartialResult of type T.
+   */
+  def map[T](f: R => T) : PartialResult[T] = {
+    new PartialResult[T](f(initialVal), isFinal) {
+       override def getFinalValue() : T = synchronized {
+         f(PartialResult.this.getFinalValue())
+       }
+       override def onComplete(handler: T => Unit): PartialResult[T] = synchronized {
+         PartialResult.this.onComplete(handler.compose(f)).map(f)
+       }
+      override def onFail(handler: Exception => Unit) {
+        synchronized {
+          PartialResult.this.onFail(handler)
+        }
+      }
+      override def toString : String = synchronized {
+        PartialResult.this.getFinalValueInternal() match {
+          case Some(value) => "(final: " + f(value) + ")"
+          case None => "(partial: " + initialValue + ")"
+        }
+      }
+      def getFinalValueInternal() = PartialResult.this.getFinalValueInternal().map(f)
+    }
+  }
+
   private[spark] def setFinalValue(value: R) {
     synchronized {
       if (finalValue != None) {
@@ -70,6 +96,8 @@ class PartialResult[R](initialVal: R, isFinal: Boolean) {
     }
   }
 
+  private def getFinalValueInternal() = finalValue
+
   private[spark] def setFailure(exception: Exception) {
     synchronized {
       if (failure != None) {

core/src/main/scala/spark/util/StatCounter.scala

@@ -5,7 +5,7 @@ package spark.util
  * numerically robust way. Includes support for merging two StatCounters. Based on Welford and
  * Chan's algorithms described at http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance.
  */
-class StatCounter(values: TraversableOnce[Double]) {
+class StatCounter(values: TraversableOnce[Double]) extends Serializable {
   private var n: Long = 0     // Running count of our values
   private var mu: Double = 0  // Running mean of our values
   private var m2: Double = 0  // Running variance numerator (sum of (x - mean)^2)

core/src/test/scala/spark/JavaAPISuite.java

@@ -0,0 +1,465 @@
+package spark;
+
+import com.google.common.base.Charsets;
+import com.google.common.io.Files;
+import org.apache.hadoop.io.IntWritable;
+import org.apache.hadoop.io.Text;
+import org.apache.hadoop.mapred.SequenceFileInputFormat;
+import org.apache.hadoop.mapred.SequenceFileOutputFormat;
+import org.apache.hadoop.mapreduce.Job;
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import scala.Tuple2;
+
+import spark.api.java.JavaDoubleRDD;
+import spark.api.java.JavaPairRDD;
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.*;
+import spark.partial.BoundedDouble;
+import spark.partial.PartialResult;
+import spark.util.StatCounter;
+
+import java.io.File;
+import java.io.IOException;
+import java.io.Serializable;
+import java.util.*;
+
+// The test suite itself is Serializable so that anonymous Function implementations can be
+// serialized, as an alternative to converting these anonymous classes to static inner classes;
+// see http://stackoverflow.com/questions/758570/.
+public class JavaAPISuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaAPISuite");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  static class ReverseIntComparator implements Comparator<Integer>, Serializable {
+
+    @Override
+    public int compare(Integer a, Integer b) {
+      if (a > b) return -1;
+      else if (a < b) return 1;
+      else return 0;
+    }
+  };
+
+  @Test
+  public void sparkContextUnion() {
+    // Union of non-specialized JavaRDDs
+    List<String> strings = Arrays.asList("Hello", "World");
+    JavaRDD<String> s1 = sc.parallelize(strings);
+    JavaRDD<String> s2 = sc.parallelize(strings);
+    // Varargs
+    JavaRDD<String> sUnion = sc.union(s1, s2);
+    Assert.assertEquals(4, sUnion.count());
+    // List
+    List<JavaRDD<String>> srdds = new ArrayList<JavaRDD<String>>();
+    srdds.add(s1);
+    srdds.add(s2);
+    sUnion = sc.union(srdds);
+    Assert.assertEquals(4, sUnion.count());
+
+    // Union of JavaDoubleRDDs
+    List<Double> doubles = Arrays.asList(1.0, 2.0);
+    JavaDoubleRDD d1 = sc.parallelizeDoubles(doubles);
+    JavaDoubleRDD d2 = sc.parallelizeDoubles(doubles);
+    JavaDoubleRDD dUnion = sc.union(d1, d2);
+    Assert.assertEquals(4, dUnion.count());
+
+    // Union of JavaPairRDDs
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
+    pairs.add(new Tuple2<Integer, Integer>(1, 2));
+    pairs.add(new Tuple2<Integer, Integer>(3, 4));
+    JavaPairRDD<Integer, Integer> p1 = sc.parallelizePairs(pairs);
+    JavaPairRDD<Integer, Integer> p2 = sc.parallelizePairs(pairs);
+    JavaPairRDD<Integer, Integer> pUnion = sc.union(p1, p2);
+    Assert.assertEquals(4, pUnion.count());
+  }
+
+  @Test
+  public void sortByKey() {
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
+    pairs.add(new Tuple2<Integer, Integer>(0, 4));
+    pairs.add(new Tuple2<Integer, Integer>(3, 2));
+    pairs.add(new Tuple2<Integer, Integer>(-1, 1));
+
+    JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
+
+    // Default comparator
+    JavaPairRDD<Integer, Integer> sortedRDD = rdd.sortByKey();
+    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    List<Tuple2<Integer, Integer>> sortedPairs = sortedRDD.collect();
+    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
+
+    // Custom comparator
+    sortedRDD = rdd.sortByKey(new ReverseIntComparator(), false);
+    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    sortedPairs = sortedRDD.collect();
+    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
+  }
+
+  @Test
+  public void foreach() {
+    JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello", "World"));
+    rdd.foreach(new VoidFunction<String>() {
+      @Override
+      public void apply(String s) {
+        System.out.println(s);
+      }
+    });
+  }
+
+  @Test
+  public void groupBy() {
+    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13));
+    Function<Integer, Boolean> isOdd = new Function<Integer, Boolean>() {
+      @Override
+      public Boolean apply(Integer x) {
+        return x % 2 == 0;
+      }
+    };
+    JavaPairRDD<Boolean, List<Integer>> oddsAndEvens = rdd.groupBy(isOdd);
+    Assert.assertEquals(2, oddsAndEvens.count());
+    Assert.assertEquals(2, oddsAndEvens.lookup(true).get(0).size());  // Evens
+    Assert.assertEquals(5, oddsAndEvens.lookup(false).get(0).size()); // Odds
+
+    oddsAndEvens = rdd.groupBy(isOdd, 1);
+    Assert.assertEquals(2, oddsAndEvens.count());
+    Assert.assertEquals(2, oddsAndEvens.lookup(true).get(0).size());  // Evens
+    Assert.assertEquals(5, oddsAndEvens.lookup(false).get(0).size()); // Odds
+  }
+
+  @Test
+  public void cogroup() {
+    JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<String, String>("Apples", "Fruit"),
+      new Tuple2<String, String>("Oranges", "Fruit"),
+      new Tuple2<String, String>("Oranges", "Citrus")
+      ));
+    JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<String, Integer>("Oranges", 2),
+      new Tuple2<String, Integer>("Apples", 3)
+    ));
+    JavaPairRDD<String, Tuple2<List<String>, List<Integer>>> cogrouped = categories.cogroup(prices);
+    Assert.assertEquals("[Fruit, Citrus]", cogrouped.lookup("Oranges").get(0)._1().toString());
+    Assert.assertEquals("[2]", cogrouped.lookup("Oranges").get(0)._2().toString());
+
+    cogrouped.collect();
+  }
+
+  @Test
+  public void foldReduce() {
+    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13));
+    Function2<Integer, Integer, Integer> add = new Function2<Integer, Integer, Integer>() {
+      @Override
+      public Integer apply(Integer a, Integer b) {
+        return a + b;
+      }
+    };
+
+    int sum = rdd.fold(0, add);
+    Assert.assertEquals(33, sum);
+
+    sum = rdd.reduce(add);
+    Assert.assertEquals(33, sum);
+  }
+
+  @Test
+  public void approximateResults() {
+    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13));
+    Map<Integer, Long> countsByValue = rdd.countByValue();
+    Assert.assertEquals(2, countsByValue.get(1).longValue());
+    Assert.assertEquals(1, countsByValue.get(13).longValue());
+
+    PartialResult<Map<Integer, BoundedDouble>> approx = rdd.countByValueApprox(1);
+    Map<Integer, BoundedDouble> finalValue = approx.getFinalValue();
+    Assert.assertEquals(2.0, finalValue.get(1).mean(), 0.01);
+    Assert.assertEquals(1.0, finalValue.get(13).mean(), 0.01);
+  }
+
+  @Test
+  public void take() {
+    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13));
+    Assert.assertEquals(1, rdd.first().intValue());
+    List<Integer> firstTwo = rdd.take(2);
+    List<Integer> sample = rdd.takeSample(false, 2, 42);
+  }
+
+  @Test
+  public void cartesian() {
+    JavaDoubleRDD doubleRDD = sc.parallelizeDoubles(Arrays.asList(1.0, 1.0, 2.0, 3.0, 5.0, 8.0));
+    JavaRDD<String> stringRDD = sc.parallelize(Arrays.asList("Hello", "World"));
+    JavaPairRDD<String, Double> cartesian = stringRDD.cartesian(doubleRDD);
+    Assert.assertEquals(new Tuple2<String, Double>("Hello", 1.0), cartesian.first());
+  }
+
+  @Test
+  public void javaDoubleRDD() {
+    JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 1.0, 2.0, 3.0, 5.0, 8.0));
+    JavaDoubleRDD distinct = rdd.distinct();
+    Assert.assertEquals(5, distinct.count());
+    JavaDoubleRDD filter = rdd.filter(new Function<Double, Boolean>() {
+      @Override
+      public Boolean apply(Double x) {
+        return x > 2.0;
+      }
+    });
+    Assert.assertEquals(3, filter.count());
+    JavaDoubleRDD union = rdd.union(rdd);
+    Assert.assertEquals(12, union.count());
+    union = union.cache();
+    Assert.assertEquals(12, union.count());
+
+    Assert.assertEquals(20, rdd.sum(), 0.01);
+    StatCounter stats = rdd.stats();
+    Assert.assertEquals(20, stats.sum(), 0.01);
+    Assert.assertEquals(20/6.0, rdd.mean(), 0.01);
+    Assert.assertEquals(20/6.0, rdd.mean(), 0.01);
+    Assert.assertEquals(6.22222, rdd.variance(), 0.01);
+    Assert.assertEquals(2.49444, rdd.stdev(), 0.01);
+
+    Double first = rdd.first();
+    List<Double> take = rdd.take(5);
+  }
+
+  @Test
+  public void map() {
+    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
+    JavaDoubleRDD doubles = rdd.map(new DoubleFunction<Integer>() {
+      @Override
+      public Double apply(Integer x) {
+        return 1.0 * x;
+      }
+    }).cache();
+    JavaPairRDD<Integer, Integer> pairs = rdd.map(new PairFunction<Integer, Integer, Integer>() {
+      @Override
+      public Tuple2<Integer, Integer> apply(Integer x) {
+        return new Tuple2<Integer, Integer>(x, x);
+      }
+    }).cache();
+    JavaRDD<String> strings = rdd.map(new Function<Integer, String>() {
+      @Override
+      public String apply(Integer x) {
+        return x.toString();
+      }
+    }).cache();
+  }
+
+  @Test
+  public void flatMap() {
+    JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello World!",
+      "The quick brown fox jumps over the lazy dog."));
+    JavaRDD<String> words = rdd.flatMap(new FlatMapFunction<String, String>() {
+      @Override
+      public Iterable<String> apply(String x) {
+        return Arrays.asList(x.split(" "));
+      }
+    });
+    Assert.assertEquals("Hello", words.first());
+    Assert.assertEquals(11, words.count());
+
+    JavaPairRDD<String, String> pairs = rdd.flatMap(
+      new PairFlatMapFunction<String, String, String>() {
+
+        @Override
+        public Iterable<Tuple2<String, String>> apply(String s) {
+          List<Tuple2<String, String>> pairs = new LinkedList<Tuple2<String, String>>();
+          for (String word : s.split(" ")) pairs.add(new Tuple2<String, String>(word, word));
+          return pairs;
+        }
+      }
+    );
+    Assert.assertEquals(new Tuple2<String, String>("Hello", "Hello"), pairs.first());
+    Assert.assertEquals(11, pairs.count());
+
+    JavaDoubleRDD doubles = rdd.flatMap(new DoubleFlatMapFunction<String>() {
+      @Override
+      public Iterable<Double> apply(String s) {
+        List<Double> lengths = new LinkedList<Double>();
+        for (String word : s.split(" ")) lengths.add(word.length() * 1.0);
+        return lengths;
+      }
+    });
+    Double x = doubles.first();
+    Assert.assertEquals(5.0, doubles.first().doubleValue(), 0.01);
+    Assert.assertEquals(11, pairs.count());
+  }
+
+  // File input / output tests are largely adapted from FileSuite:
+
+  @Test
+  public void textFiles() throws IOException {
+    File tempDir = Files.createTempDir();
+    String outputDir = new File(tempDir, "output").getAbsolutePath();
+    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4));
+    rdd.saveAsTextFile(outputDir);
+    // Read the plain text file and check it's OK
+    File outputFile = new File(outputDir, "part-00000");
+    String content = Files.toString(outputFile, Charsets.UTF_8);
+    Assert.assertEquals("1\n2\n3\n4\n", content);
+    // Also try reading it in as a text file RDD
+    List<String> expected = Arrays.asList("1", "2", "3", "4");
+    JavaRDD<String> readRDD = sc.textFile(outputDir);
+    Assert.assertEquals(expected, readRDD.collect());
+  }
+
+  @Test
+  public void sequenceFile() {
+    File tempDir = Files.createTempDir();
+    String outputDir = new File(tempDir, "output").getAbsolutePath();
+    List<Tuple2<Integer, String>> pairs = Arrays.asList(
+      new Tuple2<Integer, String>(1, "a"),
+      new Tuple2<Integer, String>(2, "aa"),
+      new Tuple2<Integer, String>(3, "aaa")
+    );
+    JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
+
+    rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
+      @Override
+      public Tuple2<IntWritable, Text> apply(Tuple2<Integer, String> pair) {
+        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+      }
+    }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class);
+
+    // Try reading the output back as an object file
+    JavaPairRDD<Integer, String> readRDD = sc.sequenceFile(outputDir, IntWritable.class,
+      Text.class).map(new PairFunction<Tuple2<IntWritable, Text>, Integer, String>() {
+      @Override
+      public Tuple2<Integer, String> apply(Tuple2<IntWritable, Text> pair) {
+        return new Tuple2<Integer, String>(pair._1().get(), pair._2().toString());
+      }
+    });
+    Assert.assertEquals(pairs, readRDD.collect());
+  }
+
+  @Test
+  public void writeWithNewAPIHadoopFile() {
+    File tempDir = Files.createTempDir();
+    String outputDir = new File(tempDir, "output").getAbsolutePath();
+    List<Tuple2<Integer, String>> pairs = Arrays.asList(
+      new Tuple2<Integer, String>(1, "a"),
+      new Tuple2<Integer, String>(2, "aa"),
+      new Tuple2<Integer, String>(3, "aaa")
+    );
+    JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
+
+    rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
+      @Override
+      public Tuple2<IntWritable, Text> apply(Tuple2<Integer, String> pair) {
+        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+      }
+    }).saveAsNewAPIHadoopFile(outputDir, IntWritable.class, Text.class,
+      org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat.class);
+
+    JavaPairRDD<IntWritable, Text> output = sc.sequenceFile(outputDir, IntWritable.class,
+      Text.class);
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
+      String>() {
+      @Override
+      public String apply(Tuple2<IntWritable, Text> x) {
+        return x.toString();
+      }
+    }).collect().toString());
+  }
+
+  @Test
+  public void readWithNewAPIHadoopFile() throws IOException {
+    File tempDir = Files.createTempDir();
+    String outputDir = new File(tempDir, "output").getAbsolutePath();
+    List<Tuple2<Integer, String>> pairs = Arrays.asList(
+      new Tuple2<Integer, String>(1, "a"),
+      new Tuple2<Integer, String>(2, "aa"),
+      new Tuple2<Integer, String>(3, "aaa")
+    );
+    JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
+
+    rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
+      @Override
+      public Tuple2<IntWritable, Text> apply(Tuple2<Integer, String> pair) {
+        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+      }
+    }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class);
+
+    JavaPairRDD<IntWritable, Text> output = sc.newAPIHadoopFile(outputDir,
+      org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat.class, IntWritable.class,
+      Text.class, new Job().getConfiguration());
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
+      String>() {
+      @Override
+      public String apply(Tuple2<IntWritable, Text> x) {
+        return x.toString();
+      }
+    }).collect().toString());
+  }
+
+  @Test
+  public void objectFilesOfInts() {
+    File tempDir = Files.createTempDir();
+    String outputDir = new File(tempDir, "output").getAbsolutePath();
+    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4));
+    rdd.saveAsObjectFile(outputDir);
+    // Try reading the output back as an object file
+    List<Integer> expected = Arrays.asList(1, 2, 3, 4);
+    JavaRDD<Integer> readRDD = sc.objectFile(outputDir);
+    Assert.assertEquals(expected, readRDD.collect());
+  }
+
+  @Test
+  public void objectFilesOfComplexTypes() {
+    File tempDir = Files.createTempDir();
+    String outputDir = new File(tempDir, "output").getAbsolutePath();
+    List<Tuple2<Integer, String>> pairs = Arrays.asList(
+      new Tuple2<Integer, String>(1, "a"),
+      new Tuple2<Integer, String>(2, "aa"),
+      new Tuple2<Integer, String>(3, "aaa")
+    );
+    JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
+    rdd.saveAsObjectFile(outputDir);
+    // Try reading the output back as an object file
+    JavaRDD<Tuple2<Integer, String>> readRDD = sc.objectFile(outputDir);
+    Assert.assertEquals(pairs, readRDD.collect());
+  }
+
+  @Test
+  public void hadoopFile() {
+    File tempDir = Files.createTempDir();
+    String outputDir = new File(tempDir, "output").getAbsolutePath();
+    List<Tuple2<Integer, String>> pairs = Arrays.asList(
+      new Tuple2<Integer, String>(1, "a"),
+      new Tuple2<Integer, String>(2, "aa"),
+      new Tuple2<Integer, String>(3, "aaa")
+    );
+    JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
+
+    rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
+      @Override
+      public Tuple2<IntWritable, Text> apply(Tuple2<Integer, String> pair) {
+        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+      }
+    }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class);
+
+    JavaPairRDD<IntWritable, Text> output = sc.hadoopFile(outputDir,
+      SequenceFileInputFormat.class, IntWritable.class, Text.class);
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
+      String>() {
+      @Override
+      public String apply(Tuple2<IntWritable, Text> x) {
+        return x.toString();
+      }
+    }).collect().toString());
+  }
+}

examples/src/main/java/spark/examples/JavaLR.java

@@ -0,0 +1,127 @@
+package spark.examples;
+
+import scala.util.Random;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.Function;
+import spark.api.java.function.Function2;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+public class JavaLR {
+
+  static int N = 10000;  // Number of data points
+  static int D = 10;   // Number of dimensions
+  static double R = 0.7;  // Scaling factor
+  static int ITERATIONS = 5;
+  static Random rand = new Random(42);
+
+  static class DataPoint implements Serializable {
+    public DataPoint(double[] x, int y) {
+      this.x = x;
+      this.y = y;
+    }
+    double[] x;
+    int y;
+  }
+
+  static DataPoint generatePoint(int i) {
+    int y = (i % 2 == 0) ? -1 : 1;
+    double[] x = new double[D];
+    for (int j = 0; j < D; j++) {
+      x[j] = rand.nextGaussian() + y * R;
+    }
+    return new DataPoint(x, y);
+  }
+
+  static List<DataPoint> generateData() {
+    List<DataPoint> points = new ArrayList<DataPoint>(N);
+    for (int i = 0; i < N; i++) {
+      points.add(generatePoint(i));
+    }
+    return points;
+  }
+
+  static class VectorSum extends Function2<double[], double[], double[]> {
+
+    @Override
+    public double[] apply(double[] a, double[] b) {
+      double[] result = new double[D];
+      for (int j = 0; j < D; j++) {
+        result[j] = a[j] + b[j];
+      }
+      return result;
+    }
+  }
+
+  static class ComputeGradient extends Function<DataPoint, double[]> {
+
+    double[] weights;
+
+    public ComputeGradient(double[] weights) {
+      this.weights = weights;
+    }
+
+    @Override
+    public double[] apply(DataPoint p) {
+      double[] gradient = new double[D];
+      for (int i = 0; i < D; i++) {
+        double dot = dot(weights, p.x);
+        gradient[i] = (1 / (1 + Math.exp(-p.y * dot)) - 1) * p.y * p.x[i];
+      }
+      return gradient;
+    }
+  }
+
+  public static double dot(double[] a, double[] b) {
+    double x = 0;
+    for (int i = 0; i < D; i++) {
+      x += a[i] * b[i];
+    }
+    return x;
+  }
+
+  public static void printWeights(double[] a) {
+    System.out.println(Arrays.toString(a));
+  }
+
+  public static void main(String[] args) {
+
+      if (args.length == 0) {
+        System.err.println("Usage: JavaLR <host> [<slices>]");
+        System.exit(1);
+      }
+
+      JavaSparkContext sc = new JavaSparkContext(args[0], "JavaLR");
+      Integer numSlices =  (args.length > 1) ? Integer.parseInt(args[1]): 2;
+      List<DataPoint> data = generateData();
+
+      // Initialize w to a random value
+      double[] w = new double[D];
+      for (int i = 0; i < D; i++) {
+        w[i] = 2 * rand.nextDouble() - 1;
+      }
+
+      System.out.print("Initial w: ");
+      printWeights(w);
+
+      for (int i = 1; i <= ITERATIONS; i++) {
+        System.out.println("On iteration " + i);
+
+        double[] gradient = sc.parallelize(data, numSlices).map(
+          new ComputeGradient(w)
+        ).reduce(new VectorSum());
+
+        for (int j = 0; j < D; j++) {
+          w[j] -= gradient[j];
+        }
+
+      }
+
+      System.out.print("Final w: ");
+      printWeights(w);
+      System.exit(0);
+    }
+}

examples/src/main/java/spark/examples/JavaTC.java

@@ -0,0 +1,76 @@
+package spark.examples;
+
+import scala.Tuple2;
+import scala.util.Random;
+import spark.api.java.JavaPairRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.PairFunction;
+
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Set;
+
+public class JavaTC {
+
+  static int numEdges = 200;
+  static int numVertices = 100;
+  static Random rand = new Random(42);
+
+  static List<Tuple2<Integer, Integer>> generateGraph() {
+    Set<Tuple2<Integer, Integer>> edges = new HashSet<Tuple2<Integer, Integer>>(numEdges);
+    while (edges.size() < numEdges) {
+      int from = rand.nextInt(numVertices);
+      int to = rand.nextInt(numVertices);
+      Tuple2<Integer, Integer> e = new Tuple2<Integer, Integer>(from, to);
+      if (from != to) edges.add(e);
+    }
+    return new ArrayList(edges);
+  }
+
+  static class ProjectFn extends PairFunction<Tuple2<Integer, Tuple2<Integer, Integer>>,
+      Integer, Integer> {
+    static ProjectFn INSTANCE = new ProjectFn();
+    @Override
+    public Tuple2<Integer, Integer> apply(Tuple2<Integer, Tuple2<Integer, Integer>> triple) {
+      return new Tuple2<Integer, Integer>(triple._2()._2(), triple._2()._1());
+    }
+  }
+
+  public static void main(String[] args) {
+    if (args.length == 0) {
+      System.err.println("Usage: JavaTC <host> [<slices>]");
+      System.exit(1);
+    }
+
+    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaTC");
+    Integer slices = (args.length > 1) ? Integer.parseInt(args[1]): 2;
+    JavaPairRDD<Integer, Integer> tc = sc.parallelizePairs(generateGraph(), slices);
+
+    // Linear transitive closure: each round grows paths by one edge,
+    // by joining the graph's edges with the already-discovered paths.
+    // e.g. join the path (y, z) from the TC with the edge (x, y) from
+    // the graph to obtain the path (x, z).
+
+      // Because join() joins on keys, the edges are stored in reversed order.
+    JavaPairRDD<Integer, Integer> edges = tc.map(new PairFunction<Tuple2<Integer, Integer>,
+              Integer, Integer>() {
+      @Override
+      public Tuple2<Integer, Integer> apply(Tuple2<Integer, Integer> e) {
+        return new Tuple2<Integer, Integer>(e._2(), e._1());
+      }
+    });
+
+    long oldCount = 0;
+    do {
+      oldCount = tc.count();
+      // Perform the join, obtaining an RDD of (y, (z, x)) pairs,
+      // then project the result to obtain the new (x, z) paths.
+
+      tc = tc.union(tc.join(edges).map(ProjectFn.INSTANCE)).distinct();
+    } while (tc.count() != oldCount);
+
+    System.out.println("TC has " + tc.count() + " edges.");
+    System.exit(0);
+  }
+}

examples/src/main/java/spark/examples/JavaTest.java

@@ -0,0 +1,38 @@
+package spark.examples;
+
+import spark.api.java.JavaDoubleRDD;
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.DoubleFunction;
+
+import java.util.List;
+
+public class JavaTest {
+
+  public static class MapFunction extends DoubleFunction<String> {
+    @Override
+    public Double apply(String s) {
+      return java.lang.Double.parseDouble(s);
+    }
+  }
+
+  public static void main(String[] args) throws Exception {
+    
+    JavaSparkContext ctx = new JavaSparkContext("local", "JavaTest");
+    JavaRDD<String> lines = ctx.textFile("numbers.txt", 1).cache();
+    List<String> lineArr = lines.collect();
+    
+    for (String line : lineArr) {
+      System.out.println(line);
+    }
+
+    JavaDoubleRDD data = lines.map(new MapFunction()).cache();
+
+    System.out.println("output");
+    List<Double> output = data.collect();
+    for (Double num : output) {
+     System.out.println(num);
+    }
+    System.exit(0);
+  }
+}

examples/src/main/java/spark/examples/JavaWordCount.java

@@ -0,0 +1,61 @@
+package spark.examples;
+
+import scala.Tuple2;
+import scala.collection.immutable.StringOps;
+import spark.api.java.JavaPairRDD;
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.FlatMapFunction;
+import spark.api.java.function.Function2;
+import spark.api.java.function.PairFunction;
+
+import java.util.Arrays;
+import java.util.List;
+
+public class JavaWordCount {
+
+  public static class SplitFunction extends FlatMapFunction<String, String> {
+    @Override
+    public Iterable<String> apply(String s) {
+      StringOps op = new StringOps(s);
+      return Arrays.asList(op.split(' '));
+    }
+  }
+  
+  public static class MapFunction extends PairFunction<String, String, Integer> {
+    @Override
+    public Tuple2<String, Integer> apply(String s) {
+      return new Tuple2(s, 1);
+    }
+  }
+	
+  public static class ReduceFunction extends Function2<Integer, Integer, Integer> {
+    @Override
+    public Integer apply(Integer i1, Integer i2) {
+      return i1 + i2;
+    }
+  }
+  public static void main(String[] args) throws Exception {
+    JavaSparkContext ctx = new JavaSparkContext("local", "JavaWordCount");
+    JavaRDD<String> lines = ctx.textFile("numbers.txt", 1).cache();
+    List<String> lineArr = lines.collect();
+
+    for (String line : lineArr) {
+      System.out.println(line);
+    }
+
+    JavaRDD<String> words = lines.flatMap(new SplitFunction());
+    
+    JavaPairRDD<String, Integer> splits = words.map(new MapFunction());
+
+    JavaPairRDD<String, Integer> counts = splits.reduceByKey(new ReduceFunction());
+    
+    System.out.println("output");
+    List<Tuple2<String, Integer>> output = counts.collect();
+    for (Tuple2 tuple : output) {
+      System.out.print(tuple._1 + ": ");
+      System.out.println(tuple._2);
+    }
+    System.exit(0);
+  }
+}

examples/src/main/scala/spark/examples/SparkTC.scala

@@ -0,0 +1,53 @@
+package spark.examples
+
+import spark._
+import SparkContext._
+import scala.util.Random
+import scala.collection.mutable
+
+object SparkTC {
+
+  val numEdges = 200
+  val numVertices = 100
+  val rand = new Random(42)
+
+  def generateGraph = {
+    val edges: mutable.Set[(Int, Int)] = mutable.Set.empty
+    while (edges.size < numEdges) {
+      val from = rand.nextInt(numVertices)
+      val to = rand.nextInt(numVertices)
+      if (from != to) edges.+=((from, to))
+    }
+    edges.toSeq
+  }
+
+  def main(args: Array[String]) {
+    if (args.length == 0) {
+      System.err.println("Usage: SparkTC <host> [<slices>]")
+      System.exit(1)
+    }
+    val spark = new SparkContext(args(0), "SparkTC")
+    val slices = if (args.length > 1) args(1).toInt else 2
+    var tc = spark.parallelize(generateGraph, slices)
+
+    // Linear transitive closure: each round grows paths by one edge,
+    // by joining the graph's edges with the already-discovered paths.
+    // e.g. join the path (y, z) from the TC with the edge (x, y) from
+    // the graph to obtain the path (x, z).
+
+    // Because join() joins on keys, the edges are stored in reversed order.
+    val edges = tc.map(x => (x._2, x._1))
+
+    // This join is iterated until a fixed point is reached.
+    var oldCount = 0L
+    do {
+      oldCount = tc.count()
+      // Perform the join, obtaining an RDD of (y, (z, x)) pairs,
+      // then project the result to obtain the new (x, z) paths.
+      tc = tc.union(tc.join(edges).map(x => (x._2._2, x._2._1))).distinct()
+    } while (tc.count() != oldCount)
+
+    println("TC has " + tc.count() + " edges.")
+    System.exit(0)
+  }
+}

project/SparkBuild.scala

@@ -31,7 +31,8 @@ object SparkBuild extends Build {
     libraryDependencies ++= Seq(
       "org.eclipse.jetty" % "jetty-server" % "7.5.3.v20111011",
       "org.scalatest" %% "scalatest" % "1.6.1" % "test",
-      "org.scalacheck" %% "scalacheck" % "1.9" % "test"
+      "org.scalacheck" %% "scalacheck" % "1.9" % "test",
+      "com.novocode" % "junit-interface" % "0.8" % "test"
     ),
     parallelExecution := false,
     /* Workaround for issue #206 (fixed after SBT 0.11.0) */