From 06e5e6627f3856b5c6e3e60cbb167044de9ef6d4 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@eecs.berkeley.edu>
Date: Sun, 17 Feb 2013 22:13:26 -0800
Subject: [PATCH] Renamed "splits" to "partitions"

---
 bagel/src/main/scala/spark/bagel/Bagel.scala  |  20 ++--
 .../bagel/examples/WikipediaPageRank.scala    |   6 +-
 .../WikipediaPageRankStandalone.scala         |   2 +-
 core/src/main/scala/spark/CacheManager.scala  |   4 +-
 .../main/scala/spark/DoubleRDDFunctions.scala |   4 +-
 .../main/scala/spark/PairRDDFunctions.scala   |  50 ++++----
 .../spark/{Split.scala => Partition.scala}    |   2 +-
 core/src/main/scala/spark/RDD.scala           |  76 ++++++------
 .../main/scala/spark/RDDCheckpointData.scala  |  12 +-
 core/src/main/scala/spark/SparkContext.scala  |  10 +-
 .../scala/spark/api/java/JavaDoubleRDD.scala  |   6 +-
 .../scala/spark/api/java/JavaPairRDD.scala    |  44 +++----
 .../main/scala/spark/api/java/JavaRDD.scala   |   6 +-
 .../scala/spark/api/java/JavaRDDLike.scala    |  12 +-
 .../scala/spark/api/python/PythonRDD.scala    |  10 +-
 .../partial/ApproximateActionListener.scala   |   2 +-
 core/src/main/scala/spark/rdd/BlockRDD.scala  |  16 +--
 .../main/scala/spark/rdd/CartesianRDD.scala   |  36 +++---
 .../main/scala/spark/rdd/CheckpointRDD.scala  |  20 ++--
 .../main/scala/spark/rdd/CoGroupedRDD.scala   |  22 ++--
 .../main/scala/spark/rdd/CoalescedRDD.scala   |  24 ++--
 .../main/scala/spark/rdd/FilteredRDD.scala    |   6 +-
 .../main/scala/spark/rdd/FlatMappedRDD.scala  |   6 +-
 .../src/main/scala/spark/rdd/GlommedRDD.scala |   6 +-
 core/src/main/scala/spark/rdd/HadoopRDD.scala |  20 ++--
 .../scala/spark/rdd/MapPartitionsRDD.scala    |   8 +-
 ....scala => MapPartitionsWithIndexRDD.scala} |  12 +-
 core/src/main/scala/spark/rdd/MappedRDD.scala |   6 +-
 .../main/scala/spark/rdd/NewHadoopRDD.scala   |  20 ++--
 .../spark/rdd/ParallelCollectionRDD.scala     |  18 +--
 .../scala/spark/rdd/PartitionPruningRDD.scala |  16 +--
 core/src/main/scala/spark/rdd/PipedRDD.scala  |   6 +-
 .../src/main/scala/spark/rdd/SampledRDD.scala |  16 +--
 .../main/scala/spark/rdd/ShuffledRDD.scala    |  10 +-
 core/src/main/scala/spark/rdd/UnionRDD.scala  |  30 ++---
 core/src/main/scala/spark/rdd/ZippedRDD.scala |  32 ++---
 .../scala/spark/scheduler/DAGScheduler.scala  |  14 +--
 .../scala/spark/scheduler/ResultTask.scala    |   6 +-
 .../spark/scheduler/ShuffleMapTask.scala      |   6 +-
 .../main/scala/spark/scheduler/Stage.scala    |   2 +-
 .../scala/spark/storage/BlockManager.scala    |   2 +-
 .../scala/spark/storage/StorageUtils.scala    |   2 +-
 .../test/scala/spark/CheckpointSuite.scala    | 110 +++++++++---------
 core/src/test/scala/spark/RDDSuite.scala      |  13 ++-
 core/src/test/scala/spark/ShuffleSuite.scala  |   2 +-
 core/src/test/scala/spark/SortingSuite.scala  |  10 +-
 .../spark/scheduler/DAGSchedulerSuite.scala   |  22 ++--
 .../spark/scheduler/TaskContextSuite.scala    |  10 +-
 48 files changed, 405 insertions(+), 390 deletions(-)
 rename core/src/main/scala/spark/{Split.scala => Partition.scala} (84%)
 rename core/src/main/scala/spark/rdd/{MapPartitionsWithSplitRDD.scala => MapPartitionsWithIndexRDD.scala} (57%)

diff --git a/bagel/src/main/scala/spark/bagel/Bagel.scala b/bagel/src/main/scala/spark/bagel/Bagel.scala
index fa0ba4a573..094e57dacb 100644
--- a/bagel/src/main/scala/spark/bagel/Bagel.scala
+++ b/bagel/src/main/scala/spark/bagel/Bagel.scala
@@ -14,11 +14,11 @@ object Bagel extends Logging {
     combiner: Combiner[M, C],
     aggregator: Option[Aggregator[V, A]],
     partitioner: Partitioner,
-    numSplits: Int
+    numPartitions: Int
   )(
     compute: (V, Option[C], Option[A], Int) => (V, Array[M])
   ): RDD[(K, V)] = {
-    val splits = if (numSplits != 0) numSplits else sc.defaultParallelism
+    val splits = if (numPartitions != 0) numPartitions else sc.defaultParallelism
 
     var superstep = 0
     var verts = vertices
@@ -56,12 +56,12 @@ object Bagel extends Logging {
     messages: RDD[(K, M)],
     combiner: Combiner[M, C],
     partitioner: Partitioner,
-    numSplits: Int
+    numPartitions: Int
   )(
     compute: (V, Option[C], Int) => (V, Array[M])
   ): RDD[(K, V)] = {
     run[K, V, M, C, Nothing](
-      sc, vertices, messages, combiner, None, partitioner, numSplits)(
+      sc, vertices, messages, combiner, None, partitioner, numPartitions)(
       addAggregatorArg[K, V, M, C](compute))
   }
 
@@ -70,13 +70,13 @@ object Bagel extends Logging {
     vertices: RDD[(K, V)],
     messages: RDD[(K, M)],
     combiner: Combiner[M, C],
-    numSplits: Int
+    numPartitions: Int
   )(
     compute: (V, Option[C], Int) => (V, Array[M])
   ): RDD[(K, V)] = {
-    val part = new HashPartitioner(numSplits)
+    val part = new HashPartitioner(numPartitions)
     run[K, V, M, C, Nothing](
-      sc, vertices, messages, combiner, None, part, numSplits)(
+      sc, vertices, messages, combiner, None, part, numPartitions)(
       addAggregatorArg[K, V, M, C](compute))
   }
 
@@ -84,13 +84,13 @@ object Bagel extends Logging {
     sc: SparkContext,
     vertices: RDD[(K, V)],
     messages: RDD[(K, M)],
-    numSplits: Int
+    numPartitions: Int
   )(
     compute: (V, Option[Array[M]], Int) => (V, Array[M])
   ): RDD[(K, V)] = {
-    val part = new HashPartitioner(numSplits)
+    val part = new HashPartitioner(numPartitions)
     run[K, V, M, Array[M], Nothing](
-      sc, vertices, messages, new DefaultCombiner(), None, part, numSplits)(
+      sc, vertices, messages, new DefaultCombiner(), None, part, numPartitions)(
       addAggregatorArg[K, V, M, Array[M]](compute))
   }
 
diff --git a/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRank.scala b/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRank.scala
index 03843019c0..bc32663e0f 100644
--- a/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRank.scala
+++ b/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRank.scala
@@ -16,7 +16,7 @@ import scala.xml.{XML,NodeSeq}
 object WikipediaPageRank {
   def main(args: Array[String]) {
     if (args.length < 5) {
-      System.err.println("Usage: WikipediaPageRank <inputFile> <threshold> <numSplits> <host> <usePartitioner>")
+      System.err.println("Usage: WikipediaPageRank <inputFile> <threshold> <numPartitions> <host> <usePartitioner>")
       System.exit(-1)
     }
 
@@ -25,7 +25,7 @@ object WikipediaPageRank {
 
     val inputFile = args(0)
     val threshold = args(1).toDouble
-    val numSplits = args(2).toInt
+    val numPartitions = args(2).toInt
     val host = args(3)
     val usePartitioner = args(4).toBoolean
     val sc = new SparkContext(host, "WikipediaPageRank")
@@ -69,7 +69,7 @@ object WikipediaPageRank {
     val result =
         Bagel.run(
           sc, vertices, messages, combiner = new PRCombiner(),
-          numSplits = numSplits)(
+          numPartitions = numPartitions)(
           utils.computeWithCombiner(numVertices, epsilon))
 
     // Print the result
diff --git a/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRankStandalone.scala b/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRankStandalone.scala
index 06cc8c748b..9d9d80d809 100644
--- a/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRankStandalone.scala
+++ b/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRankStandalone.scala
@@ -88,7 +88,7 @@ object WikipediaPageRankStandalone {
     n: Long,
     partitioner: Partitioner,
     usePartitioner: Boolean,
-    numSplits: Int
+    numPartitions: Int
   ): RDD[(String, Double)] = {
     var ranks = links.mapValues { edges => defaultRank }
     for (i <- 1 to numIterations) {
diff --git a/core/src/main/scala/spark/CacheManager.scala b/core/src/main/scala/spark/CacheManager.scala
index 711435c333..c7b379a3fb 100644
--- a/core/src/main/scala/spark/CacheManager.scala
+++ b/core/src/main/scala/spark/CacheManager.scala
@@ -11,13 +11,13 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
   private val loading = new HashSet[String]
 
   /** Gets or computes an RDD split. Used by RDD.iterator() when an RDD is cached. */
-  def getOrCompute[T](rdd: RDD[T], split: Split, context: TaskContext, storageLevel: StorageLevel)
+  def getOrCompute[T](rdd: RDD[T], split: Partition, context: TaskContext, storageLevel: StorageLevel)
       : Iterator[T] = {
     val key = "rdd_%d_%d".format(rdd.id, split.index)
     logInfo("Cache key is " + key)
     blockManager.get(key) match {
       case Some(cachedValues) =>
-        // Split is in cache, so just return its values
+        // Partition is in cache, so just return its values
         logInfo("Found partition in cache!")
         return cachedValues.asInstanceOf[Iterator[T]]
 
diff --git a/core/src/main/scala/spark/DoubleRDDFunctions.scala b/core/src/main/scala/spark/DoubleRDDFunctions.scala
index b2a0e2b631..178d31a73b 100644
--- a/core/src/main/scala/spark/DoubleRDDFunctions.scala
+++ b/core/src/main/scala/spark/DoubleRDDFunctions.scala
@@ -42,14 +42,14 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
   /** (Experimental) Approximate operation to return the mean within a timeout. */
   def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {
     val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns)
-    val evaluator = new MeanEvaluator(self.splits.size, confidence)
+    val evaluator = new MeanEvaluator(self.partitions.size, confidence)
     self.context.runApproximateJob(self, processPartition, evaluator, timeout)
   }
 
   /** (Experimental) Approximate operation to return the sum within a timeout. */
   def sumApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {
     val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns)
-    val evaluator = new SumEvaluator(self.splits.size, confidence)
+    val evaluator = new SumEvaluator(self.partitions.size, confidence)
     self.context.runApproximateJob(self, processPartition, evaluator, timeout)
   }
 }
diff --git a/core/src/main/scala/spark/PairRDDFunctions.scala b/core/src/main/scala/spark/PairRDDFunctions.scala
index 019be11ea8..4319cbd892 100644
--- a/core/src/main/scala/spark/PairRDDFunctions.scala
+++ b/core/src/main/scala/spark/PairRDDFunctions.scala
@@ -83,8 +83,8 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
   def combineByKey[C](createCombiner: V => C,
       mergeValue: (C, V) => C,
       mergeCombiners: (C, C) => C,
-      numSplits: Int): RDD[(K, C)] = {
-    combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numSplits))
+      numPartitions: Int): RDD[(K, C)] = {
+    combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions))
   }
 
   /**
@@ -145,10 +145,10 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
   /**
    * Merge the values for each key using an associative reduce function. This will also perform
    * the merging locally on each mapper before sending results to a reducer, similarly to a
-   * "combiner" in MapReduce. Output will be hash-partitioned with numSplits splits.
+   * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
    */
-  def reduceByKey(func: (V, V) => V, numSplits: Int): RDD[(K, V)] = {
-    reduceByKey(new HashPartitioner(numSplits), func)
+  def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = {
+    reduceByKey(new HashPartitioner(numPartitions), func)
   }
 
   /**
@@ -166,10 +166,10 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
 
   /**
    * Group the values for each key in the RDD into a single sequence. Hash-partitions the
-   * resulting RDD with into `numSplits` partitions.
+   * resulting RDD with into `numPartitions` partitions.
    */
-  def groupByKey(numSplits: Int): RDD[(K, Seq[V])] = {
-    groupByKey(new HashPartitioner(numSplits))
+  def groupByKey(numPartitions: Int): RDD[(K, Seq[V])] = {
+    groupByKey(new HashPartitioner(numPartitions))
   }
 
   /**
@@ -287,8 +287,8 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
    * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
    * (k, v2) is in `other`. Performs a hash join across the cluster.
    */
-  def join[W](other: RDD[(K, W)], numSplits: Int): RDD[(K, (V, W))] = {
-    join(other, new HashPartitioner(numSplits))
+  def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))] = {
+    join(other, new HashPartitioner(numPartitions))
   }
 
   /**
@@ -305,10 +305,10 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
    * Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
    * resulting RDD will either contain all pairs (k, (v, Some(w))) for w in `other`, or the
    * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output
-   * into `numSplits` partitions.
+   * into `numPartitions` partitions.
    */
-  def leftOuterJoin[W](other: RDD[(K, W)], numSplits: Int): RDD[(K, (V, Option[W]))] = {
-    leftOuterJoin(other, new HashPartitioner(numSplits))
+  def leftOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, Option[W]))] = {
+    leftOuterJoin(other, new HashPartitioner(numPartitions))
   }
 
   /**
@@ -327,8 +327,8 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
    * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting
    * RDD into the given number of partitions.
    */
-  def rightOuterJoin[W](other: RDD[(K, W)], numSplits: Int): RDD[(K, (Option[V], W))] = {
-    rightOuterJoin(other, new HashPartitioner(numSplits))
+  def rightOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], W))] = {
+    rightOuterJoin(other, new HashPartitioner(numPartitions))
   }
 
   /**
@@ -414,17 +414,17 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
    * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
    * list of values for that key in `this` as well as `other`.
    */
-  def cogroup[W](other: RDD[(K, W)], numSplits: Int): RDD[(K, (Seq[V], Seq[W]))] = {
-    cogroup(other, new HashPartitioner(numSplits))
+  def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Seq[V], Seq[W]))] = {
+    cogroup(other, new HashPartitioner(numPartitions))
   }
 
   /**
    * For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
    * tuple with the list of values for that key in `this`, `other1` and `other2`.
    */
-  def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numSplits: Int)
+  def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numPartitions: Int)
       : RDD[(K, (Seq[V], Seq[W1], Seq[W2]))] = {
-    cogroup(other1, other2, new HashPartitioner(numSplits))
+    cogroup(other1, other2, new HashPartitioner(numPartitions))
   }
 
   /** Alias for cogroup. */
@@ -636,9 +636,9 @@ class OrderedRDDFunctions[K <% Ordered[K]: ClassManifest, V: ClassManifest](
    * (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
    * order of the keys).
    */
-  def sortByKey(ascending: Boolean = true, numSplits: Int = self.splits.size): RDD[(K,V)] = {
+  def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.size): RDD[(K,V)] = {
     val shuffled =
-      new ShuffledRDD[K, V](self, new RangePartitioner(numSplits, self, ascending))
+      new ShuffledRDD[K, V](self, new RangePartitioner(numPartitions, self, ascending))
     shuffled.mapPartitions(iter => {
       val buf = iter.toArray
       if (ascending) {
@@ -652,9 +652,9 @@ class OrderedRDDFunctions[K <% Ordered[K]: ClassManifest, V: ClassManifest](
 
 private[spark]
 class MappedValuesRDD[K, V, U](prev: RDD[(K, V)], f: V => U) extends RDD[(K, U)](prev) {
-  override def getSplits = firstParent[(K, V)].splits
+  override def getPartitions = firstParent[(K, V)].partitions
   override val partitioner = firstParent[(K, V)].partitioner
-  override def compute(split: Split, context: TaskContext) =
+  override def compute(split: Partition, context: TaskContext) =
     firstParent[(K, V)].iterator(split, context).map{ case (k, v) => (k, f(v)) }
 }
 
@@ -662,9 +662,9 @@ private[spark]
 class FlatMappedValuesRDD[K, V, U](prev: RDD[(K, V)], f: V => TraversableOnce[U])
   extends RDD[(K, U)](prev) {
 
-  override def getSplits = firstParent[(K, V)].splits
+  override def getPartitions = firstParent[(K, V)].partitions
   override val partitioner = firstParent[(K, V)].partitioner
-  override def compute(split: Split, context: TaskContext) = {
+  override def compute(split: Partition, context: TaskContext) = {
     firstParent[(K, V)].iterator(split, context).flatMap { case (k, v) => f(v).map(x => (k, x)) }
   }
 }
diff --git a/core/src/main/scala/spark/Split.scala b/core/src/main/scala/spark/Partition.scala
similarity index 84%
rename from core/src/main/scala/spark/Split.scala
rename to core/src/main/scala/spark/Partition.scala
index 90d4b47c55..e384308ef6 100644
--- a/core/src/main/scala/spark/Split.scala
+++ b/core/src/main/scala/spark/Partition.scala
@@ -3,7 +3,7 @@ package spark
 /**
  * A partition of an RDD.
  */
-trait Split extends Serializable {
+trait Partition extends Serializable {
   /**
    * Get the split's index within its parent RDD
    */
diff --git a/core/src/main/scala/spark/RDD.scala b/core/src/main/scala/spark/RDD.scala
index f6e927a989..da82dfd10f 100644
--- a/core/src/main/scala/spark/RDD.scala
+++ b/core/src/main/scala/spark/RDD.scala
@@ -27,7 +27,7 @@ import spark.rdd.FlatMappedRDD
 import spark.rdd.GlommedRDD
 import spark.rdd.MappedRDD
 import spark.rdd.MapPartitionsRDD
-import spark.rdd.MapPartitionsWithSplitRDD
+import spark.rdd.MapPartitionsWithIndexRDD
 import spark.rdd.PipedRDD
 import spark.rdd.SampledRDD
 import spark.rdd.UnionRDD
@@ -49,7 +49,7 @@ import SparkContext._
  *
  * Internally, each RDD is characterized by five main properties:
  *
- *  - A list of splits (partitions)
+ *  - A list of partitions
  *  - A function for computing each split
  *  - A list of dependencies on other RDDs
  *  - Optionally, a Partitioner for key-value RDDs (e.g. to say that the RDD is hash-partitioned)
@@ -76,13 +76,13 @@ abstract class RDD[T: ClassManifest](
   // =======================================================================
 
   /** Implemented by subclasses to compute a given partition. */
-  def compute(split: Split, context: TaskContext): Iterator[T]
+  def compute(split: Partition, context: TaskContext): Iterator[T]
 
   /**
    * Implemented by subclasses to return the set of partitions in this RDD. This method will only
    * be called once, so it is safe to implement a time-consuming computation in it.
    */
-  protected def getSplits: Array[Split]
+  protected def getPartitions: Array[Partition]
 
   /**
    * Implemented by subclasses to return how this RDD depends on parent RDDs. This method will only
@@ -91,7 +91,7 @@ abstract class RDD[T: ClassManifest](
   protected def getDependencies: Seq[Dependency[_]] = deps
 
   /** Optionally overridden by subclasses to specify placement preferences. */
-  protected def getPreferredLocations(split: Split): Seq[String] = Nil
+  protected def getPreferredLocations(split: Partition): Seq[String] = Nil
 
   /** Optionally overridden by subclasses to specify how they are partitioned. */
   val partitioner: Option[Partitioner] = None
@@ -137,10 +137,10 @@ abstract class RDD[T: ClassManifest](
   /** Get the RDD's current storage level, or StorageLevel.NONE if none is set. */
   def getStorageLevel = storageLevel
 
-  // Our dependencies and splits will be gotten by calling subclass's methods below, and will
+  // Our dependencies and partitions will be gotten by calling subclass's methods below, and will
   // be overwritten when we're checkpointed
   private var dependencies_ : Seq[Dependency[_]] = null
-  @transient private var splits_ : Array[Split] = null
+  @transient private var partitions_ : Array[Partition] = null
 
   /** An Option holding our checkpoint RDD, if we are checkpointed */
   private def checkpointRDD: Option[RDD[T]] = checkpointData.flatMap(_.checkpointRDD)
@@ -159,15 +159,15 @@ abstract class RDD[T: ClassManifest](
   }
 
   /**
-   * Get the array of splits of this RDD, taking into account whether the
+   * Get the array of partitions of this RDD, taking into account whether the
    * RDD is checkpointed or not.
    */
-  final def splits: Array[Split] = {
-    checkpointRDD.map(_.splits).getOrElse {
-      if (splits_ == null) {
-        splits_ = getSplits
+  final def partitions: Array[Partition] = {
+    checkpointRDD.map(_.partitions).getOrElse {
+      if (partitions_ == null) {
+        partitions_ = getPartitions
       }
-      splits_
+      partitions_
     }
   }
 
@@ -175,7 +175,7 @@ abstract class RDD[T: ClassManifest](
    * Get the preferred location of a split, taking into account whether the
    * RDD is checkpointed or not.
    */
-  final def preferredLocations(split: Split): Seq[String] = {
+  final def preferredLocations(split: Partition): Seq[String] = {
     checkpointRDD.map(_.getPreferredLocations(split)).getOrElse {
       getPreferredLocations(split)
     }
@@ -186,7 +186,7 @@ abstract class RDD[T: ClassManifest](
    * This should ''not'' be called by users directly, but is available for implementors of custom
    * subclasses of RDD.
    */
-  final def iterator(split: Split, context: TaskContext): Iterator[T] = {
+  final def iterator(split: Partition, context: TaskContext): Iterator[T] = {
     if (storageLevel != StorageLevel.NONE) {
       SparkEnv.get.cacheManager.getOrCompute(this, split, context, storageLevel)
     } else {
@@ -197,7 +197,7 @@ abstract class RDD[T: ClassManifest](
   /**
    * Compute an RDD partition or read it from a checkpoint if the RDD is checkpointing.
    */
-  private[spark] def computeOrReadCheckpoint(split: Split, context: TaskContext): Iterator[T] = {
+  private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] = {
     if (isCheckpointed) {
       firstParent[T].iterator(split, context)
     } else {
@@ -227,15 +227,15 @@ abstract class RDD[T: ClassManifest](
   /**
    * Return a new RDD containing the distinct elements in this RDD.
    */
-  def distinct(numSplits: Int): RDD[T] =
-    map(x => (x, null)).reduceByKey((x, y) => x, numSplits).map(_._1)
+  def distinct(numPartitions: Int): RDD[T] =
+    map(x => (x, null)).reduceByKey((x, y) => x, numPartitions).map(_._1)
 
-  def distinct(): RDD[T] = distinct(splits.size)
+  def distinct(): RDD[T] = distinct(partitions.size)
 
   /**
-   * Return a new RDD that is reduced into `numSplits` partitions.
+   * Return a new RDD that is reduced into `numPartitions` partitions.
    */
-  def coalesce(numSplits: Int): RDD[T] = new CoalescedRDD(this, numSplits)
+  def coalesce(numPartitions: Int): RDD[T] = new CoalescedRDD(this, numPartitions)
 
   /**
    * Return a sampled subset of this RDD.
@@ -303,9 +303,9 @@ abstract class RDD[T: ClassManifest](
    * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements
    * mapping to that key.
    */
-  def groupBy[K: ClassManifest](f: T => K, numSplits: Int): RDD[(K, Seq[T])] = {
+  def groupBy[K: ClassManifest](f: T => K, numPartitions: Int): RDD[(K, Seq[T])] = {
     val cleanF = sc.clean(f)
-    this.map(t => (cleanF(t), t)).groupByKey(numSplits)
+    this.map(t => (cleanF(t), t)).groupByKey(numPartitions)
   }
 
   /**
@@ -336,14 +336,24 @@ abstract class RDD[T: ClassManifest](
     preservesPartitioning: Boolean = false): RDD[U] =
     new MapPartitionsRDD(this, sc.clean(f), preservesPartitioning)
 
-   /**
+  /**
    * Return a new RDD by applying a function to each partition of this RDD, while tracking the index
    * of the original partition.
    */
+  def mapPartitionsWithIndex[U: ClassManifest](
+    f: (Int, Iterator[T]) => Iterator[U],
+    preservesPartitioning: Boolean = false): RDD[U] =
+    new MapPartitionsWithIndexRDD(this, sc.clean(f), preservesPartitioning)
+
+  /**
+   * Return a new RDD by applying a function to each partition of this RDD, while tracking the index
+   * of the original partition.
+   */
+  @deprecated("use mapPartitionsWithIndex")
   def mapPartitionsWithSplit[U: ClassManifest](
     f: (Int, Iterator[T]) => Iterator[U],
     preservesPartitioning: Boolean = false): RDD[U] =
-    new MapPartitionsWithSplitRDD(this, sc.clean(f), preservesPartitioning)
+    new MapPartitionsWithIndexRDD(this, sc.clean(f), preservesPartitioning)
 
   /**
    * Zips this RDD with another one, returning key-value pairs with the first element in each RDD,
@@ -471,7 +481,7 @@ abstract class RDD[T: ClassManifest](
       }
       result
     }
-    val evaluator = new CountEvaluator(splits.size, confidence)
+    val evaluator = new CountEvaluator(partitions.size, confidence)
     sc.runApproximateJob(this, countElements, evaluator, timeout)
   }
 
@@ -522,7 +532,7 @@ abstract class RDD[T: ClassManifest](
       }
       map
     }
-    val evaluator = new GroupedCountEvaluator[T](splits.size, confidence)
+    val evaluator = new GroupedCountEvaluator[T](partitions.size, confidence)
     sc.runApproximateJob(this, countPartition, evaluator, timeout)
   }
 
@@ -537,7 +547,7 @@ abstract class RDD[T: ClassManifest](
     }
     val buf = new ArrayBuffer[T]
     var p = 0
-    while (buf.size < num && p < splits.size) {
+    while (buf.size < num && p < partitions.size) {
       val left = num - buf.size
       val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, Array(p), true)
       buf ++= res(0)
@@ -657,11 +667,11 @@ abstract class RDD[T: ClassManifest](
 
   /**
    * Changes the dependencies of this RDD from its original parents to a new RDD (`newRDD`)
-   * created from the checkpoint file, and forget its old dependencies and splits.
+   * created from the checkpoint file, and forget its old dependencies and partitions.
    */
   private[spark] def markCheckpointed(checkpointRDD: RDD[_]) {
     clearDependencies()
-    splits_ = null
+    partitions_ = null
     deps = null    // Forget the constructor argument for dependencies too
   }
 
@@ -676,15 +686,15 @@ abstract class RDD[T: ClassManifest](
   }
 
   /** A description of this RDD and its recursive dependencies for debugging. */
-  def toDebugString(): String = {
+  def toDebugString: String = {
     def debugString(rdd: RDD[_], prefix: String = ""): Seq[String] = {
-      Seq(prefix + rdd + " (" + rdd.splits.size + " splits)") ++
+      Seq(prefix + rdd + " (" + rdd.partitions.size + " partitions)") ++
         rdd.dependencies.flatMap(d => debugString(d.rdd, prefix + "  "))
     }
     debugString(this).mkString("\n")
   }
 
-  override def toString(): String = "%s%s[%d] at %s".format(
+  override def toString: String = "%s%s[%d] at %s".format(
     Option(name).map(_ + " ").getOrElse(""),
     getClass.getSimpleName,
     id,
diff --git a/core/src/main/scala/spark/RDDCheckpointData.scala b/core/src/main/scala/spark/RDDCheckpointData.scala
index a4a4ebaf53..d00092e984 100644
--- a/core/src/main/scala/spark/RDDCheckpointData.scala
+++ b/core/src/main/scala/spark/RDDCheckpointData.scala
@@ -16,7 +16,7 @@ private[spark] object CheckpointState extends Enumeration {
 /**
  * This class contains all the information related to RDD checkpointing. Each instance of this class
  * is associated with a RDD. It manages process of checkpointing of the associated RDD, as well as,
- * manages the post-checkpoint state by providing the updated splits, iterator and preferred locations
+ * manages the post-checkpoint state by providing the updated partitions, iterator and preferred locations
  * of the checkpointed RDD.
  */
 private[spark] class RDDCheckpointData[T: ClassManifest](rdd: RDD[T])
@@ -67,11 +67,11 @@ private[spark] class RDDCheckpointData[T: ClassManifest](rdd: RDD[T])
     rdd.context.runJob(rdd, CheckpointRDD.writeToFile(path) _)
     val newRDD = new CheckpointRDD[T](rdd.context, path)
 
-    // Change the dependencies and splits of the RDD
+    // Change the dependencies and partitions of the RDD
     RDDCheckpointData.synchronized {
       cpFile = Some(path)
       cpRDD = Some(newRDD)
-      rdd.markCheckpointed(newRDD)   // Update the RDD's dependencies and splits
+      rdd.markCheckpointed(newRDD)   // Update the RDD's dependencies and partitions
       cpState = Checkpointed
       RDDCheckpointData.clearTaskCaches()
       logInfo("Done checkpointing RDD " + rdd.id + ", new parent is RDD " + newRDD.id)
@@ -79,15 +79,15 @@ private[spark] class RDDCheckpointData[T: ClassManifest](rdd: RDD[T])
   }
 
   // Get preferred location of a split after checkpointing
-  def getPreferredLocations(split: Split): Seq[String] = {
+  def getPreferredLocations(split: Partition): Seq[String] = {
     RDDCheckpointData.synchronized {
       cpRDD.get.preferredLocations(split)
     }
   }
 
-  def getSplits: Array[Split] = {
+  def getPartitions: Array[Partition] = {
     RDDCheckpointData.synchronized {
-      cpRDD.get.splits
+      cpRDD.get.partitions
     }
   }
 
diff --git a/core/src/main/scala/spark/SparkContext.scala b/core/src/main/scala/spark/SparkContext.scala
index 047b57dc1f..f299b7ea46 100644
--- a/core/src/main/scala/spark/SparkContext.scala
+++ b/core/src/main/scala/spark/SparkContext.scala
@@ -614,14 +614,14 @@ class SparkContext(
    * Run a job on all partitions in an RDD and return the results in an array.
    */
   def runJob[T, U: ClassManifest](rdd: RDD[T], func: (TaskContext, Iterator[T]) => U): Array[U] = {
-    runJob(rdd, func, 0 until rdd.splits.size, false)
+    runJob(rdd, func, 0 until rdd.partitions.size, false)
   }
 
   /**
    * Run a job on all partitions in an RDD and return the results in an array.
    */
   def runJob[T, U: ClassManifest](rdd: RDD[T], func: Iterator[T] => U): Array[U] = {
-    runJob(rdd, func, 0 until rdd.splits.size, false)
+    runJob(rdd, func, 0 until rdd.partitions.size, false)
   }
 
   /**
@@ -632,7 +632,7 @@ class SparkContext(
     processPartition: (TaskContext, Iterator[T]) => U,
     resultHandler: (Int, U) => Unit)
   {
-    runJob[T, U](rdd, processPartition, 0 until rdd.splits.size, false, resultHandler)
+    runJob[T, U](rdd, processPartition, 0 until rdd.partitions.size, false, resultHandler)
   }
 
   /**
@@ -644,7 +644,7 @@ class SparkContext(
       resultHandler: (Int, U) => Unit)
   {
     val processFunc = (context: TaskContext, iter: Iterator[T]) => processPartition(iter)
-    runJob[T, U](rdd, processFunc, 0 until rdd.splits.size, false, resultHandler)
+    runJob[T, U](rdd, processFunc, 0 until rdd.partitions.size, false, resultHandler)
   }
 
   /**
@@ -696,7 +696,7 @@ class SparkContext(
   /** Default level of parallelism to use when not given by user (e.g. for reduce tasks) */
   def defaultParallelism: Int = taskScheduler.defaultParallelism
 
-  /** Default min number of splits for Hadoop RDDs when not given by user */
+  /** Default min number of partitions for Hadoop RDDs when not given by user */
   def defaultMinSplits: Int = math.min(defaultParallelism, 2)
 
   private var nextShuffleId = new AtomicInteger(0)
diff --git a/core/src/main/scala/spark/api/java/JavaDoubleRDD.scala b/core/src/main/scala/spark/api/java/JavaDoubleRDD.scala
index 2810631b41..da3cb2cd31 100644
--- a/core/src/main/scala/spark/api/java/JavaDoubleRDD.scala
+++ b/core/src/main/scala/spark/api/java/JavaDoubleRDD.scala
@@ -44,7 +44,7 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav
   /**
    * Return a new RDD containing the distinct elements in this RDD.
    */
-  def distinct(numSplits: Int): JavaDoubleRDD = fromRDD(srdd.distinct(numSplits))
+  def distinct(numPartitions: Int): JavaDoubleRDD = fromRDD(srdd.distinct(numPartitions))
 
   /**
    * Return a new RDD containing only the elements that satisfy a predicate.
@@ -53,9 +53,9 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav
     fromRDD(srdd.filter(x => f(x).booleanValue()))
 
   /**
-   * Return a new RDD that is reduced into `numSplits` partitions.
+   * Return a new RDD that is reduced into `numPartitions` partitions.
    */
-  def coalesce(numSplits: Int): JavaDoubleRDD = fromRDD(srdd.coalesce(numSplits))
+  def coalesce(numPartitions: Int): JavaDoubleRDD = fromRDD(srdd.coalesce(numPartitions))
 
   /**
    * Return a sampled subset of this RDD.
diff --git a/core/src/main/scala/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/spark/api/java/JavaPairRDD.scala
index 55dc755358..df3af3817d 100644
--- a/core/src/main/scala/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/spark/api/java/JavaPairRDD.scala
@@ -54,7 +54,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
   /**
    * Return a new RDD containing the distinct elements in this RDD.
    */
-  def distinct(numSplits: Int): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.distinct(numSplits))
+  def distinct(numPartitions: Int): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.distinct(numPartitions))
 
   /**
    * Return a new RDD containing only the elements that satisfy a predicate.
@@ -63,9 +63,9 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
     new JavaPairRDD[K, V](rdd.filter(x => f(x).booleanValue()))
 
   /**
-   * Return a new RDD that is reduced into `numSplits` partitions.
+   * Return a new RDD that is reduced into `numPartitions` partitions.
    */
-  def coalesce(numSplits: Int): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.coalesce(numSplits))
+  def coalesce(numPartitions: Int): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.coalesce(numPartitions))
 
   /**
    * Return a sampled subset of this RDD.
@@ -122,8 +122,8 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
   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))
+    numPartitions: Int): JavaPairRDD[K, C] =
+    combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions))
 
   /**
    * Merge the values for each key using an associative reduce function. This will also perform
@@ -162,10 +162,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
   /**
    * Merge the values for each key using an associative reduce function. This will also perform
    * the merging locally on each mapper before sending results to a reducer, similarly to a
-   * "combiner" in MapReduce. Output will be hash-partitioned with numSplits splits.
+   * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
    */
-  def reduceByKey(func: JFunction2[V, V, V], numSplits: Int): JavaPairRDD[K, V] =
-    fromRDD(rdd.reduceByKey(func, numSplits))
+  def reduceByKey(func: JFunction2[V, V, V], numPartitions: Int): JavaPairRDD[K, V] =
+    fromRDD(rdd.reduceByKey(func, numPartitions))
 
   /**
    * Group the values for each key in the RDD into a single sequence. Allows controlling the
@@ -176,10 +176,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
 
   /**
    * Group the values for each key in the RDD into a single sequence. Hash-partitions the
-   * resulting RDD with into `numSplits` partitions.
+   * resulting RDD with into `numPartitions` partitions.
    */
-  def groupByKey(numSplits: Int): JavaPairRDD[K, JList[V]] =
-    fromRDD(groupByResultToJava(rdd.groupByKey(numSplits)))
+  def groupByKey(numPartitions: Int): JavaPairRDD[K, JList[V]] =
+    fromRDD(groupByResultToJava(rdd.groupByKey(numPartitions)))
 
   /**
    * Return a copy of the RDD partitioned using the specified partitioner. If `mapSideCombine`
@@ -261,8 +261,8 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
    * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
    * (k, v2) is in `other`. Performs a hash join across the cluster.
    */
-  def join[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (V, W)] =
-    fromRDD(rdd.join(other, numSplits))
+  def join[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, W)] =
+    fromRDD(rdd.join(other, numPartitions))
 
   /**
    * Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
@@ -277,10 +277,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
    * Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
    * resulting RDD will either contain all pairs (k, (v, Some(w))) for w in `other`, or the
    * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output
-   * into `numSplits` partitions.
+   * into `numPartitions` partitions.
    */
-  def leftOuterJoin[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (V, Option[W])] =
-    fromRDD(rdd.leftOuterJoin(other, numSplits))
+  def leftOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, Option[W])] =
+    fromRDD(rdd.leftOuterJoin(other, numPartitions))
 
   /**
    * Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
@@ -297,8 +297,8 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
    * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting
    * RDD into the given number of partitions.
    */
-  def rightOuterJoin[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (Option[V], W)] =
-    fromRDD(rdd.rightOuterJoin(other, numSplits))
+  def rightOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Option[V], W)] =
+    fromRDD(rdd.rightOuterJoin(other, numPartitions))
 
   /**
    * Return the key-value pairs in this RDD to the master as a Map.
@@ -362,16 +362,16 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
    * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
    * list of values for that key in `this` as well as `other`.
    */
-  def cogroup[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (JList[V], JList[W])]
-  = fromRDD(cogroupResultToJava(rdd.cogroup(other, numSplits)))
+  def cogroup[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (JList[V], JList[W])]
+  = fromRDD(cogroupResultToJava(rdd.cogroup(other, numPartitions)))
 
   /**
    * For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
    * tuple with the list of values for that key in `this`, `other1` and `other2`.
    */
-  def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], numSplits: Int)
+  def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], numPartitions: Int)
   : JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] =
-    fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, numSplits)))
+    fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, numPartitions)))
 
   /** Alias for cogroup. */
   def groupWith[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JList[V], JList[W])] =
diff --git a/core/src/main/scala/spark/api/java/JavaRDD.scala b/core/src/main/scala/spark/api/java/JavaRDD.scala
index 23e7ae2726..3ccd6f055e 100644
--- a/core/src/main/scala/spark/api/java/JavaRDD.scala
+++ b/core/src/main/scala/spark/api/java/JavaRDD.scala
@@ -30,7 +30,7 @@ JavaRDDLike[T, JavaRDD[T]] {
   /**
    * Return a new RDD containing the distinct elements in this RDD.
    */
-  def distinct(numSplits: Int): JavaRDD[T] = wrapRDD(rdd.distinct(numSplits))
+  def distinct(numPartitions: Int): JavaRDD[T] = wrapRDD(rdd.distinct(numPartitions))
   
   /**
    * Return a new RDD containing only the elements that satisfy a predicate.
@@ -39,9 +39,9 @@ JavaRDDLike[T, JavaRDD[T]] {
     wrapRDD(rdd.filter((x => f(x).booleanValue())))
 
   /**
-   * Return a new RDD that is reduced into `numSplits` partitions.
+   * Return a new RDD that is reduced into `numPartitions` partitions.
    */
-  def coalesce(numSplits: Int): JavaRDD[T] = rdd.coalesce(numSplits)
+  def coalesce(numPartitions: Int): JavaRDD[T] = rdd.coalesce(numPartitions)
 
   /**
    * Return a sampled subset of this RDD.
diff --git a/core/src/main/scala/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/spark/api/java/JavaRDDLike.scala
index d34d56d169..90b45cf875 100644
--- a/core/src/main/scala/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/spark/api/java/JavaRDDLike.scala
@@ -4,7 +4,7 @@ import java.util.{List => JList}
 import scala.Tuple2
 import scala.collection.JavaConversions._
 
-import spark.{SparkContext, Split, RDD, TaskContext}
+import spark.{SparkContext, Partition, RDD, TaskContext}
 import spark.api.java.JavaPairRDD._
 import spark.api.java.function.{Function2 => JFunction2, Function => JFunction, _}
 import spark.partial.{PartialResult, BoundedDouble}
@@ -20,7 +20,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends PairFlatMapWorkaround
   def rdd: RDD[T]
 
   /** Set of partitions in this RDD. */
-  def splits: JList[Split] = new java.util.ArrayList(rdd.splits.toSeq)
+  def splits: JList[Partition] = new java.util.ArrayList(rdd.partitions.toSeq)
 
   /** The [[spark.SparkContext]] that this RDD was created on. */
   def context: SparkContext = rdd.context
@@ -36,7 +36,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends PairFlatMapWorkaround
    * This should ''not'' be called by users directly, but is available for implementors of custom
    * subclasses of RDD.
    */
-  def iterator(split: Split, taskContext: TaskContext): java.util.Iterator[T] =
+  def iterator(split: Partition, taskContext: TaskContext): java.util.Iterator[T] =
     asJavaIterator(rdd.iterator(split, taskContext))
 
   // Transformations (return a new RDD)
@@ -146,12 +146,12 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends PairFlatMapWorkaround
    * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements
    * mapping to that key.
    */
-  def groupBy[K](f: JFunction[T, K], numSplits: Int): JavaPairRDD[K, JList[T]] = {
+  def groupBy[K](f: JFunction[T, K], numPartitions: 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)
+    JavaPairRDD.fromRDD(groupByResultToJava(rdd.groupBy(f, numPartitions)(f.returnType)))(kcm, vcm)
   }
 
   /**
@@ -333,6 +333,6 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends PairFlatMapWorkaround
 
   /** A description of this RDD and its recursive dependencies for debugging. */
   def toDebugString(): String = {
-    rdd.toDebugString()
+    rdd.toDebugString
   }
 }
diff --git a/core/src/main/scala/spark/api/python/PythonRDD.scala b/core/src/main/scala/spark/api/python/PythonRDD.scala
index ab8351e55e..8c73477384 100644
--- a/core/src/main/scala/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/spark/api/python/PythonRDD.scala
@@ -32,11 +32,11 @@ private[spark] class PythonRDD[T: ClassManifest](
     this(parent, PipedRDD.tokenize(command), envVars, preservePartitoning, pythonExec,
       broadcastVars, accumulator)
 
-  override def getSplits = parent.splits
+  override def getPartitions = parent.partitions
 
   override val partitioner = if (preservePartitoning) parent.partitioner else None
 
-  override def compute(split: Split, context: TaskContext): Iterator[Array[Byte]] = {
+  override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = {
     val SPARK_HOME = new ProcessBuilder().environment().get("SPARK_HOME")
 
     val pb = new ProcessBuilder(Seq(pythonExec, SPARK_HOME + "/python/pyspark/worker.py"))
@@ -65,7 +65,7 @@ private[spark] class PythonRDD[T: ClassManifest](
         SparkEnv.set(env)
         val out = new PrintWriter(proc.getOutputStream)
         val dOut = new DataOutputStream(proc.getOutputStream)
-        // Split index
+        // Partition index
         dOut.writeInt(split.index)
         // sparkFilesDir
         PythonRDD.writeAsPickle(SparkFiles.getRootDirectory, dOut)
@@ -155,8 +155,8 @@ private class PythonException(msg: String) extends Exception(msg)
  */
 private class PairwiseRDD(prev: RDD[Array[Byte]]) extends
   RDD[(Array[Byte], Array[Byte])](prev) {
-  override def getSplits = prev.splits
-  override def compute(split: Split, context: TaskContext) =
+  override def getPartitions = prev.partitions
+  override def compute(split: Partition, context: TaskContext) =
     prev.iterator(split, context).grouped(2).map {
       case Seq(a, b) => (a, b)
       case x          => throw new Exception("PairwiseRDD: unexpected value: " + x)
diff --git a/core/src/main/scala/spark/partial/ApproximateActionListener.scala b/core/src/main/scala/spark/partial/ApproximateActionListener.scala
index 24b4909380..de2dce161a 100644
--- a/core/src/main/scala/spark/partial/ApproximateActionListener.scala
+++ b/core/src/main/scala/spark/partial/ApproximateActionListener.scala
@@ -20,7 +20,7 @@ private[spark] class ApproximateActionListener[T, U, R](
   extends JobListener {
 
   val startTime = System.currentTimeMillis()
-  val totalTasks = rdd.splits.size
+  val totalTasks = rdd.partitions.size
   var finishedTasks = 0
   var failure: Option[Exception] = None             // Set if the job has failed (permanently)
   var resultObject: Option[PartialResult[R]] = None // Set if we've already returned a PartialResult
diff --git a/core/src/main/scala/spark/rdd/BlockRDD.scala b/core/src/main/scala/spark/rdd/BlockRDD.scala
index 17989c5ce5..7348c4f15b 100644
--- a/core/src/main/scala/spark/rdd/BlockRDD.scala
+++ b/core/src/main/scala/spark/rdd/BlockRDD.scala
@@ -1,9 +1,9 @@
 package spark.rdd
 
 import scala.collection.mutable.HashMap
-import spark.{RDD, SparkContext, SparkEnv, Split, TaskContext}
+import spark.{RDD, SparkContext, SparkEnv, Partition, TaskContext}
 
-private[spark] class BlockRDDSplit(val blockId: String, idx: Int) extends Split {
+private[spark] class BlockRDDPartition(val blockId: String, idx: Int) extends Partition {
   val index = idx
 }
 
@@ -18,14 +18,14 @@ class BlockRDD[T: ClassManifest](sc: SparkContext, @transient blockIds: Array[St
     HashMap(blockIds.zip(locations):_*)
   }
 
-  override def getSplits: Array[Split] = (0 until blockIds.size).map(i => {
-    new BlockRDDSplit(blockIds(i), i).asInstanceOf[Split]
+  override def getPartitions: Array[Partition] = (0 until blockIds.size).map(i => {
+    new BlockRDDPartition(blockIds(i), i).asInstanceOf[Partition]
   }).toArray
 
 
-  override def compute(split: Split, context: TaskContext): Iterator[T] = {
+  override def compute(split: Partition, context: TaskContext): Iterator[T] = {
     val blockManager = SparkEnv.get.blockManager
-    val blockId = split.asInstanceOf[BlockRDDSplit].blockId
+    val blockId = split.asInstanceOf[BlockRDDPartition].blockId
     blockManager.get(blockId) match {
       case Some(block) => block.asInstanceOf[Iterator[T]]
       case None =>
@@ -33,8 +33,8 @@ class BlockRDD[T: ClassManifest](sc: SparkContext, @transient blockIds: Array[St
     }
   }
 
-  override def getPreferredLocations(split: Split): Seq[String] =
-    locations_(split.asInstanceOf[BlockRDDSplit].blockId)
+  override def getPreferredLocations(split: Partition): Seq[String] =
+    locations_(split.asInstanceOf[BlockRDDPartition].blockId)
 
 }
 
diff --git a/core/src/main/scala/spark/rdd/CartesianRDD.scala b/core/src/main/scala/spark/rdd/CartesianRDD.scala
index 41cbbd0093..38600b8be4 100644
--- a/core/src/main/scala/spark/rdd/CartesianRDD.scala
+++ b/core/src/main/scala/spark/rdd/CartesianRDD.scala
@@ -5,22 +5,22 @@ import spark._
 
 
 private[spark]
-class CartesianSplit(
+class CartesianPartition(
     idx: Int,
     @transient rdd1: RDD[_],
     @transient rdd2: RDD[_],
     s1Index: Int,
     s2Index: Int
-  ) extends Split {
-  var s1 = rdd1.splits(s1Index)
-  var s2 = rdd2.splits(s2Index)
+  ) extends Partition {
+  var s1 = rdd1.partitions(s1Index)
+  var s2 = rdd2.partitions(s2Index)
   override val index: Int = idx
 
   @throws(classOf[IOException])
   private def writeObject(oos: ObjectOutputStream) {
     // Update the reference to parent split at the time of task serialization
-    s1 = rdd1.splits(s1Index)
-    s2 = rdd2.splits(s2Index)
+    s1 = rdd1.partitions(s1Index)
+    s2 = rdd2.partitions(s2Index)
     oos.defaultWriteObject()
   }
 }
@@ -33,35 +33,35 @@ class CartesianRDD[T: ClassManifest, U:ClassManifest](
   extends RDD[Pair[T, U]](sc, Nil)
   with Serializable {
 
-  val numSplitsInRdd2 = rdd2.splits.size
+  val numPartitionsInRdd2 = rdd2.partitions.size
 
-  override def getSplits: Array[Split] = {
+  override def getPartitions: Array[Partition] = {
     // create the cross product split
-    val array = new Array[Split](rdd1.splits.size * rdd2.splits.size)
-    for (s1 <- rdd1.splits; s2 <- rdd2.splits) {
-      val idx = s1.index * numSplitsInRdd2 + s2.index
-      array(idx) = new CartesianSplit(idx, rdd1, rdd2, s1.index, s2.index)
+    val array = new Array[Partition](rdd1.partitions.size * rdd2.partitions.size)
+    for (s1 <- rdd1.partitions; s2 <- rdd2.partitions) {
+      val idx = s1.index * numPartitionsInRdd2 + s2.index
+      array(idx) = new CartesianPartition(idx, rdd1, rdd2, s1.index, s2.index)
     }
     array
   }
 
-  override def getPreferredLocations(split: Split): Seq[String] = {
-    val currSplit = split.asInstanceOf[CartesianSplit]
+  override def getPreferredLocations(split: Partition): Seq[String] = {
+    val currSplit = split.asInstanceOf[CartesianPartition]
     rdd1.preferredLocations(currSplit.s1) ++ rdd2.preferredLocations(currSplit.s2)
   }
 
-  override def compute(split: Split, context: TaskContext) = {
-    val currSplit = split.asInstanceOf[CartesianSplit]
+  override def compute(split: Partition, context: TaskContext) = {
+    val currSplit = split.asInstanceOf[CartesianPartition]
     for (x <- rdd1.iterator(currSplit.s1, context);
       y <- rdd2.iterator(currSplit.s2, context)) yield (x, y)
   }
 
   override def getDependencies: Seq[Dependency[_]] = List(
     new NarrowDependency(rdd1) {
-      def getParents(id: Int): Seq[Int] = List(id / numSplitsInRdd2)
+      def getParents(id: Int): Seq[Int] = List(id / numPartitionsInRdd2)
     },
     new NarrowDependency(rdd2) {
-      def getParents(id: Int): Seq[Int] = List(id % numSplitsInRdd2)
+      def getParents(id: Int): Seq[Int] = List(id % numPartitionsInRdd2)
     }
   )
 
diff --git a/core/src/main/scala/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/spark/rdd/CheckpointRDD.scala
index 3558d4673f..36bfb0355e 100644
--- a/core/src/main/scala/spark/rdd/CheckpointRDD.scala
+++ b/core/src/main/scala/spark/rdd/CheckpointRDD.scala
@@ -9,7 +9,7 @@ import org.apache.hadoop.fs.Path
 import java.io.{File, IOException, EOFException}
 import java.text.NumberFormat
 
-private[spark] class CheckpointRDDSplit(val index: Int) extends Split {}
+private[spark] class CheckpointRDDPartition(val index: Int) extends Partition {}
 
 /**
  * This RDD represents a RDD checkpoint file (similar to HadoopRDD).
@@ -20,27 +20,27 @@ class CheckpointRDD[T: ClassManifest](sc: SparkContext, val checkpointPath: Stri
 
   @transient val fs = new Path(checkpointPath).getFileSystem(sc.hadoopConfiguration)
 
-  override def getSplits: Array[Split] = {
+  override def getPartitions: Array[Partition] = {
     val dirContents = fs.listStatus(new Path(checkpointPath))
     val splitFiles = dirContents.map(_.getPath.toString).filter(_.contains("part-")).sorted
-    val numSplits = splitFiles.size
+    val numPartitions = splitFiles.size
     if (!splitFiles(0).endsWith(CheckpointRDD.splitIdToFile(0)) ||
-        !splitFiles(numSplits-1).endsWith(CheckpointRDD.splitIdToFile(numSplits-1))) {
+        !splitFiles(numPartitions-1).endsWith(CheckpointRDD.splitIdToFile(numPartitions-1))) {
       throw new SparkException("Invalid checkpoint directory: " + checkpointPath)
     }
-    Array.tabulate(numSplits)(i => new CheckpointRDDSplit(i))
+    Array.tabulate(numPartitions)(i => new CheckpointRDDPartition(i))
   }
 
   checkpointData = Some(new RDDCheckpointData[T](this))
   checkpointData.get.cpFile = Some(checkpointPath)
 
-  override def getPreferredLocations(split: Split): Seq[String] = {
+  override def getPreferredLocations(split: Partition): Seq[String] = {
     val status = fs.getFileStatus(new Path(checkpointPath))
     val locations = fs.getFileBlockLocations(status, 0, status.getLen)
     locations.headOption.toList.flatMap(_.getHosts).filter(_ != "localhost")
   }
 
-  override def compute(split: Split, context: TaskContext): Iterator[T] = {
+  override def compute(split: Partition, context: TaskContext): Iterator[T] = {
     val file = new Path(checkpointPath, CheckpointRDD.splitIdToFile(split.index))
     CheckpointRDD.readFromFile(file, context)
   }
@@ -107,7 +107,7 @@ private[spark] object CheckpointRDD extends Logging {
     deserializeStream.asIterator.asInstanceOf[Iterator[T]]
   }
 
-  // Test whether CheckpointRDD generate expected number of splits despite
+  // Test whether CheckpointRDD generate expected number of partitions despite
   // each split file having multiple blocks. This needs to be run on a
   // cluster (mesos or standalone) using HDFS.
   def main(args: Array[String]) {
@@ -120,8 +120,8 @@ private[spark] object CheckpointRDD extends Logging {
     val fs = path.getFileSystem(new Configuration())
     sc.runJob(rdd, CheckpointRDD.writeToFile(path.toString, 1024) _)
     val cpRDD = new CheckpointRDD[Int](sc, path.toString)
-    assert(cpRDD.splits.length == rdd.splits.length, "Number of splits is not the same")
-    assert(cpRDD.collect.toList == rdd.collect.toList, "Data of splits not the same")
+    assert(cpRDD.partitions.length == rdd.partitions.length, "Number of partitions is not the same")
+    assert(cpRDD.collect.toList == rdd.collect.toList, "Data of partitions not the same")
     fs.delete(path)
   }
 }
diff --git a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
index 868ee5a39f..5200fb6b65 100644
--- a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
@@ -5,7 +5,7 @@ import java.util.{HashMap => JHashMap}
 import scala.collection.JavaConversions
 import scala.collection.mutable.ArrayBuffer
 
-import spark.{Aggregator, Logging, Partitioner, RDD, SparkEnv, Split, TaskContext}
+import spark.{Aggregator, Logging, Partitioner, RDD, SparkEnv, Partition, TaskContext}
 import spark.{Dependency, OneToOneDependency, ShuffleDependency}
 
 
@@ -14,13 +14,13 @@ private[spark] sealed trait CoGroupSplitDep extends Serializable
 private[spark] case class NarrowCoGroupSplitDep(
     rdd: RDD[_],
     splitIndex: Int,
-    var split: Split
+    var split: Partition
   ) extends CoGroupSplitDep {
 
   @throws(classOf[IOException])
   private def writeObject(oos: ObjectOutputStream) {
     // Update the reference to parent split at the time of task serialization
-    split = rdd.splits(splitIndex)
+    split = rdd.partitions(splitIndex)
     oos.defaultWriteObject()
   }
 }
@@ -28,7 +28,7 @@ private[spark] case class NarrowCoGroupSplitDep(
 private[spark] case class ShuffleCoGroupSplitDep(shuffleId: Int) extends CoGroupSplitDep
 
 private[spark]
-class CoGroupSplit(idx: Int, val deps: Seq[CoGroupSplitDep]) extends Split with Serializable {
+class CoGroupPartition(idx: Int, val deps: Seq[CoGroupSplitDep]) extends Partition with Serializable {
   override val index: Int = idx
   override def hashCode(): Int = idx
 }
@@ -58,17 +58,17 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[(K, _)]], part: Partitioner)
     }
   }
 
-  override def getSplits: Array[Split] = {
-    val array = new Array[Split](part.numPartitions)
+  override def getPartitions: Array[Partition] = {
+    val array = new Array[Partition](part.numPartitions)
     for (i <- 0 until array.size) {
-      // Each CoGroupSplit will have a dependency per contributing RDD
-      array(i) = new CoGroupSplit(i, rdds.zipWithIndex.map { case (rdd, j) =>
+      // Each CoGroupPartition will have a dependency per contributing RDD
+      array(i) = new CoGroupPartition(i, rdds.zipWithIndex.map { case (rdd, j) =>
         // Assume each RDD contributed a single dependency, and get it
         dependencies(j) match {
           case s: ShuffleDependency[_, _] =>
             new ShuffleCoGroupSplitDep(s.shuffleId)
           case _ =>
-            new NarrowCoGroupSplitDep(rdd, i, rdd.splits(i))
+            new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i))
         }
       }.toList)
     }
@@ -77,8 +77,8 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[(K, _)]], part: Partitioner)
 
   override val partitioner = Some(part)
 
-  override def compute(s: Split, context: TaskContext): Iterator[(K, Seq[Seq[_]])] = {
-    val split = s.asInstanceOf[CoGroupSplit]
+  override def compute(s: Partition, context: TaskContext): Iterator[(K, Seq[Seq[_]])] = {
+    val split = s.asInstanceOf[CoGroupPartition]
     val numRdds = split.deps.size
     // e.g. for `(k, a) cogroup (k, b)`, K -> Seq(ArrayBuffer as, ArrayBuffer bs)
     val map = new JHashMap[K, Seq[ArrayBuffer[Any]]]
diff --git a/core/src/main/scala/spark/rdd/CoalescedRDD.scala b/core/src/main/scala/spark/rdd/CoalescedRDD.scala
index fcd26da43a..0d16cf6e85 100644
--- a/core/src/main/scala/spark/rdd/CoalescedRDD.scala
+++ b/core/src/main/scala/spark/rdd/CoalescedRDD.scala
@@ -1,19 +1,19 @@
 package spark.rdd
 
-import spark.{Dependency, OneToOneDependency, NarrowDependency, RDD, Split, TaskContext}
+import spark.{Dependency, OneToOneDependency, NarrowDependency, RDD, Partition, TaskContext}
 import java.io.{ObjectOutputStream, IOException}
 
-private[spark] case class CoalescedRDDSplit(
+private[spark] case class CoalescedRDDPartition(
     index: Int,
     @transient rdd: RDD[_],
     parentsIndices: Array[Int]
-  ) extends Split {
-  var parents: Seq[Split] = parentsIndices.map(rdd.splits(_))
+  ) extends Partition {
+  var parents: Seq[Partition] = parentsIndices.map(rdd.partitions(_))
 
   @throws(classOf[IOException])
   private def writeObject(oos: ObjectOutputStream) {
     // Update the reference to parent split at the time of task serialization
-    parents = parentsIndices.map(rdd.splits(_))
+    parents = parentsIndices.map(rdd.partitions(_))
     oos.defaultWriteObject()
   }
 }
@@ -31,21 +31,21 @@ class CoalescedRDD[T: ClassManifest](
     maxPartitions: Int)
   extends RDD[T](prev.context, Nil) {  // Nil since we implement getDependencies
 
-  override def getSplits: Array[Split] = {
-    val prevSplits = prev.splits
+  override def getPartitions: Array[Partition] = {
+    val prevSplits = prev.partitions
     if (prevSplits.length < maxPartitions) {
-      prevSplits.map(_.index).map{idx => new CoalescedRDDSplit(idx, prev, Array(idx)) }
+      prevSplits.map(_.index).map{idx => new CoalescedRDDPartition(idx, prev, Array(idx)) }
     } else {
       (0 until maxPartitions).map { i =>
         val rangeStart = (i * prevSplits.length) / maxPartitions
         val rangeEnd = ((i + 1) * prevSplits.length) / maxPartitions
-        new CoalescedRDDSplit(i, prev, (rangeStart until rangeEnd).toArray)
+        new CoalescedRDDPartition(i, prev, (rangeStart until rangeEnd).toArray)
       }.toArray
     }
   }
 
-  override def compute(split: Split, context: TaskContext): Iterator[T] = {
-    split.asInstanceOf[CoalescedRDDSplit].parents.iterator.flatMap { parentSplit =>
+  override def compute(split: Partition, context: TaskContext): Iterator[T] = {
+    split.asInstanceOf[CoalescedRDDPartition].parents.iterator.flatMap { parentSplit =>
       firstParent[T].iterator(parentSplit, context)
     }
   }
@@ -53,7 +53,7 @@ class CoalescedRDD[T: ClassManifest](
   override def getDependencies: Seq[Dependency[_]] = {
     Seq(new NarrowDependency(prev) {
       def getParents(id: Int): Seq[Int] =
-        splits(id).asInstanceOf[CoalescedRDDSplit].parentsIndices
+        partitions(id).asInstanceOf[CoalescedRDDPartition].parentsIndices
     })
   }
 
diff --git a/core/src/main/scala/spark/rdd/FilteredRDD.scala b/core/src/main/scala/spark/rdd/FilteredRDD.scala
index 93e398ea2b..c84ec39d21 100644
--- a/core/src/main/scala/spark/rdd/FilteredRDD.scala
+++ b/core/src/main/scala/spark/rdd/FilteredRDD.scala
@@ -1,16 +1,16 @@
 package spark.rdd
 
-import spark.{OneToOneDependency, RDD, Split, TaskContext}
+import spark.{OneToOneDependency, RDD, Partition, TaskContext}
 
 private[spark] class FilteredRDD[T: ClassManifest](
     prev: RDD[T],
     f: T => Boolean)
   extends RDD[T](prev) {
 
-  override def getSplits: Array[Split] = firstParent[T].splits
+  override def getPartitions: Array[Partition] = firstParent[T].partitions
 
   override val partitioner = prev.partitioner    // Since filter cannot change a partition's keys
 
-  override def compute(split: Split, context: TaskContext) =
+  override def compute(split: Partition, context: TaskContext) =
     firstParent[T].iterator(split, context).filter(f)
 }
diff --git a/core/src/main/scala/spark/rdd/FlatMappedRDD.scala b/core/src/main/scala/spark/rdd/FlatMappedRDD.scala
index 8c2a610593..8ebc778925 100644
--- a/core/src/main/scala/spark/rdd/FlatMappedRDD.scala
+++ b/core/src/main/scala/spark/rdd/FlatMappedRDD.scala
@@ -1,6 +1,6 @@
 package spark.rdd
 
-import spark.{RDD, Split, TaskContext}
+import spark.{RDD, Partition, TaskContext}
 
 
 private[spark]
@@ -9,8 +9,8 @@ class FlatMappedRDD[U: ClassManifest, T: ClassManifest](
     f: T => TraversableOnce[U])
   extends RDD[U](prev) {
 
-  override def getSplits: Array[Split] = firstParent[T].splits
+  override def getPartitions: Array[Partition] = firstParent[T].partitions
 
-  override def compute(split: Split, context: TaskContext) =
+  override def compute(split: Partition, context: TaskContext) =
     firstParent[T].iterator(split, context).flatMap(f)
 }
diff --git a/core/src/main/scala/spark/rdd/GlommedRDD.scala b/core/src/main/scala/spark/rdd/GlommedRDD.scala
index 70b9b4e34e..e16c7ba881 100644
--- a/core/src/main/scala/spark/rdd/GlommedRDD.scala
+++ b/core/src/main/scala/spark/rdd/GlommedRDD.scala
@@ -1,12 +1,12 @@
 package spark.rdd
 
-import spark.{RDD, Split, TaskContext}
+import spark.{RDD, Partition, TaskContext}
 
 private[spark] class GlommedRDD[T: ClassManifest](prev: RDD[T])
   extends RDD[Array[T]](prev) {
 
-  override def getSplits: Array[Split] = firstParent[T].splits
+  override def getPartitions: Array[Partition] = firstParent[T].partitions
 
-  override def compute(split: Split, context: TaskContext) =
+  override def compute(split: Partition, context: TaskContext) =
     Array(firstParent[T].iterator(split, context).toArray).iterator
 }
diff --git a/core/src/main/scala/spark/rdd/HadoopRDD.scala b/core/src/main/scala/spark/rdd/HadoopRDD.scala
index 854993737b..8139a2a40c 100644
--- a/core/src/main/scala/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/spark/rdd/HadoopRDD.scala
@@ -15,14 +15,14 @@ import org.apache.hadoop.mapred.RecordReader
 import org.apache.hadoop.mapred.Reporter
 import org.apache.hadoop.util.ReflectionUtils
 
-import spark.{Dependency, RDD, SerializableWritable, SparkContext, Split, TaskContext}
+import spark.{Dependency, RDD, SerializableWritable, SparkContext, Partition, TaskContext}
 
 
 /**
  * A Spark split class that wraps around a Hadoop InputSplit.
  */
-private[spark] class HadoopSplit(rddId: Int, idx: Int, @transient s: InputSplit)
-  extends Split {
+private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSplit)
+  extends Partition {
   
   val inputSplit = new SerializableWritable[InputSplit](s)
 
@@ -47,12 +47,12 @@ class HadoopRDD[K, V](
   // A Hadoop JobConf can be about 10 KB, which is pretty big, so broadcast it
   private val confBroadcast = sc.broadcast(new SerializableWritable(conf))
 
-  override def getSplits: Array[Split] = {
+  override def getPartitions: Array[Partition] = {
     val inputFormat = createInputFormat(conf)
     val inputSplits = inputFormat.getSplits(conf, minSplits)
-    val array = new Array[Split](inputSplits.size)
+    val array = new Array[Partition](inputSplits.size)
     for (i <- 0 until inputSplits.size) {
-      array(i) = new HadoopSplit(id, i, inputSplits(i))
+      array(i) = new HadoopPartition(id, i, inputSplits(i))
     }
     array
   }
@@ -62,8 +62,8 @@ class HadoopRDD[K, V](
       .asInstanceOf[InputFormat[K, V]]
   }
 
-  override def compute(theSplit: Split, context: TaskContext) = new Iterator[(K, V)] {
-    val split = theSplit.asInstanceOf[HadoopSplit]
+  override def compute(theSplit: Partition, context: TaskContext) = new Iterator[(K, V)] {
+    val split = theSplit.asInstanceOf[HadoopPartition]
     var reader: RecordReader[K, V] = null
 
     val conf = confBroadcast.value.value
@@ -106,9 +106,9 @@ class HadoopRDD[K, V](
     }
   }
 
-  override def getPreferredLocations(split: Split): Seq[String] = {
+  override def getPreferredLocations(split: Partition): Seq[String] = {
     // TODO: Filtering out "localhost" in case of file:// URLs
-    val hadoopSplit = split.asInstanceOf[HadoopSplit]
+    val hadoopSplit = split.asInstanceOf[HadoopPartition]
     hadoopSplit.inputSplit.value.getLocations.filter(_ != "localhost")
   }
 
diff --git a/core/src/main/scala/spark/rdd/MapPartitionsRDD.scala b/core/src/main/scala/spark/rdd/MapPartitionsRDD.scala
index 7b0b4525c7..d283c5b2bb 100644
--- a/core/src/main/scala/spark/rdd/MapPartitionsRDD.scala
+++ b/core/src/main/scala/spark/rdd/MapPartitionsRDD.scala
@@ -1,6 +1,6 @@
 package spark.rdd
 
-import spark.{RDD, Split, TaskContext}
+import spark.{RDD, Partition, TaskContext}
 
 
 private[spark]
@@ -13,8 +13,8 @@ class MapPartitionsRDD[U: ClassManifest, T: ClassManifest](
   override val partitioner =
     if (preservesPartitioning) firstParent[T].partitioner else None
 
-  override def getSplits: Array[Split] = firstParent[T].splits
+  override def getPartitions: Array[Partition] = firstParent[T].partitions
 
-  override def compute(split: Split, context: TaskContext) =
+  override def compute(split: Partition, context: TaskContext) =
     f(firstParent[T].iterator(split, context))
-}
\ No newline at end of file
+}
diff --git a/core/src/main/scala/spark/rdd/MapPartitionsWithSplitRDD.scala b/core/src/main/scala/spark/rdd/MapPartitionsWithIndexRDD.scala
similarity index 57%
rename from core/src/main/scala/spark/rdd/MapPartitionsWithSplitRDD.scala
rename to core/src/main/scala/spark/rdd/MapPartitionsWithIndexRDD.scala
index c6dc1080a9..afb7504ba1 100644
--- a/core/src/main/scala/spark/rdd/MapPartitionsWithSplitRDD.scala
+++ b/core/src/main/scala/spark/rdd/MapPartitionsWithIndexRDD.scala
@@ -1,24 +1,24 @@
 package spark.rdd
 
-import spark.{RDD, Split, TaskContext}
+import spark.{RDD, Partition, TaskContext}
 
 
 /**
- * A variant of the MapPartitionsRDD that passes the split index into the
+ * A variant of the MapPartitionsRDD that passes the partition index into the
  * closure. This can be used to generate or collect partition specific
  * information such as the number of tuples in a partition.
  */
 private[spark]
-class MapPartitionsWithSplitRDD[U: ClassManifest, T: ClassManifest](
+class MapPartitionsWithIndexRDD[U: ClassManifest, T: ClassManifest](
     prev: RDD[T],
     f: (Int, Iterator[T]) => Iterator[U],
     preservesPartitioning: Boolean
   ) extends RDD[U](prev) {
 
-  override def getSplits: Array[Split] = firstParent[T].splits
+  override def getPartitions: Array[Partition] = firstParent[T].partitions
 
   override val partitioner = if (preservesPartitioning) prev.partitioner else None
 
-  override def compute(split: Split, context: TaskContext) =
+  override def compute(split: Partition, context: TaskContext) =
     f(split.index, firstParent[T].iterator(split, context))
-}
\ No newline at end of file
+}
diff --git a/core/src/main/scala/spark/rdd/MappedRDD.scala b/core/src/main/scala/spark/rdd/MappedRDD.scala
index 6074f411e3..af07311b6d 100644
--- a/core/src/main/scala/spark/rdd/MappedRDD.scala
+++ b/core/src/main/scala/spark/rdd/MappedRDD.scala
@@ -1,13 +1,13 @@
 package spark.rdd
 
-import spark.{RDD, Split, TaskContext}
+import spark.{RDD, Partition, TaskContext}
 
 private[spark]
 class MappedRDD[U: ClassManifest, T: ClassManifest](prev: RDD[T], f: T => U)
   extends RDD[U](prev) {
 
-  override def getSplits: Array[Split] = firstParent[T].splits
+  override def getPartitions: Array[Partition] = firstParent[T].partitions
 
-  override def compute(split: Split, context: TaskContext) =
+  override def compute(split: Partition, context: TaskContext) =
     firstParent[T].iterator(split, context).map(f)
 }
diff --git a/core/src/main/scala/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/spark/rdd/NewHadoopRDD.scala
index 345ae79d74..ebd4c3f0e2 100644
--- a/core/src/main/scala/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/spark/rdd/NewHadoopRDD.scala
@@ -7,12 +7,12 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapreduce._
 
-import spark.{Dependency, RDD, SerializableWritable, SparkContext, Split, TaskContext}
+import spark.{Dependency, RDD, SerializableWritable, SparkContext, Partition, TaskContext}
 
 
 private[spark]
-class NewHadoopSplit(rddId: Int, val index: Int, @transient rawSplit: InputSplit with Writable)
-  extends Split {
+class NewHadoopPartition(rddId: Int, val index: Int, @transient rawSplit: InputSplit with Writable)
+  extends Partition {
 
   val serializableHadoopSplit = new SerializableWritable(rawSplit)
 
@@ -39,19 +39,19 @@ class NewHadoopRDD[K, V](
 
   @transient private val jobId = new JobID(jobtrackerId, id)
 
-  override def getSplits: Array[Split] = {
+  override def getPartitions: Array[Partition] = {
     val inputFormat = inputFormatClass.newInstance
     val jobContext = newJobContext(conf, jobId)
     val rawSplits = inputFormat.getSplits(jobContext).toArray
-    val result = new Array[Split](rawSplits.size)
+    val result = new Array[Partition](rawSplits.size)
     for (i <- 0 until rawSplits.size) {
-      result(i) = new NewHadoopSplit(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
+      result(i) = new NewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
     }
     result
   }
 
-  override def compute(theSplit: Split, context: TaskContext) = new Iterator[(K, V)] {
-    val split = theSplit.asInstanceOf[NewHadoopSplit]
+  override def compute(theSplit: Partition, context: TaskContext) = new Iterator[(K, V)] {
+    val split = theSplit.asInstanceOf[NewHadoopPartition]
     val conf = confBroadcast.value.value
     val attemptId = new TaskAttemptID(jobtrackerId, id, true, split.index, 0)
     val hadoopAttemptContext = newTaskAttemptContext(conf, attemptId)
@@ -83,8 +83,8 @@ class NewHadoopRDD[K, V](
     }
   }
 
-  override def getPreferredLocations(split: Split): Seq[String] = {
-    val theSplit = split.asInstanceOf[NewHadoopSplit]
+  override def getPreferredLocations(split: Partition): Seq[String] = {
+    val theSplit = split.asInstanceOf[NewHadoopPartition]
     theSplit.serializableHadoopSplit.value.getLocations.filter(_ != "localhost")
   }
 }
diff --git a/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala
index e703794787..07585a88ce 100644
--- a/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala
+++ b/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala
@@ -3,20 +3,20 @@ package spark.rdd
 import scala.collection.immutable.NumericRange
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.Map
-import spark.{RDD, TaskContext, SparkContext, Split}
+import spark.{RDD, TaskContext, SparkContext, Partition}
 
-private[spark] class ParallelCollectionSplit[T: ClassManifest](
+private[spark] class ParallelCollectionPartition[T: ClassManifest](
     val rddId: Long,
     val slice: Int,
     values: Seq[T])
-  extends Split with Serializable {
+  extends Partition with Serializable {
 
   def iterator: Iterator[T] = values.iterator
 
   override def hashCode(): Int = (41 * (41 + rddId) + slice).toInt
 
   override def equals(other: Any): Boolean = other match {
-    case that: ParallelCollectionSplit[_] => (this.rddId == that.rddId && this.slice == that.slice)
+    case that: ParallelCollectionPartition[_] => (this.rddId == that.rddId && this.slice == that.slice)
     case _ => false
   }
 
@@ -34,15 +34,15 @@ private[spark] class ParallelCollectionRDD[T: ClassManifest](
   // instead.
   // UPDATE: A parallel collection can be checkpointed to HDFS, which achieves this goal.
 
-  override def getSplits: Array[Split] = {
+  override def getPartitions: Array[Partition] = {
     val slices = ParallelCollectionRDD.slice(data, numSlices).toArray
-    slices.indices.map(i => new ParallelCollectionSplit(id, i, slices(i))).toArray
+    slices.indices.map(i => new ParallelCollectionPartition(id, i, slices(i))).toArray
   }
 
-  override def compute(s: Split, context: TaskContext) =
-    s.asInstanceOf[ParallelCollectionSplit[T]].iterator
+  override def compute(s: Partition, context: TaskContext) =
+    s.asInstanceOf[ParallelCollectionPartition[T]].iterator
 
-  override def getPreferredLocations(s: Split): Seq[String] = {
+  override def getPreferredLocations(s: Partition): Seq[String] = {
     locationPrefs.getOrElse(s.index, Nil)
   }
 }
diff --git a/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala b/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala
index d1553181c1..f2f4fd56d1 100644
--- a/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala
+++ b/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala
@@ -1,9 +1,9 @@
 package spark.rdd
 
-import spark.{NarrowDependency, RDD, SparkEnv, Split, TaskContext}
+import spark.{NarrowDependency, RDD, SparkEnv, Partition, TaskContext}
 
 
-class PartitionPruningRDDSplit(idx: Int, val parentSplit: Split) extends Split {
+class PartitionPruningRDDPartition(idx: Int, val parentSplit: Partition) extends Partition {
   override val index = idx
 }
 
@@ -16,15 +16,15 @@ class PruneDependency[T](rdd: RDD[T], @transient partitionFilterFunc: Int => Boo
   extends NarrowDependency[T](rdd) {
 
   @transient
-  val partitions: Array[Split] = rdd.splits.filter(s => partitionFilterFunc(s.index))
-    .zipWithIndex.map { case(split, idx) => new PartitionPruningRDDSplit(idx, split) : Split }
+  val partitions: Array[Partition] = rdd.partitions.filter(s => partitionFilterFunc(s.index))
+    .zipWithIndex.map { case(split, idx) => new PartitionPruningRDDPartition(idx, split) : Partition }
 
   override def getParents(partitionId: Int) = List(partitions(partitionId).index)
 }
 
 
 /**
- * A RDD used to prune RDD partitions/splits so we can avoid launching tasks on
+ * A RDD used to prune RDD partitions/partitions so we can avoid launching tasks on
  * all partitions. An example use case: If we know the RDD is partitioned by range,
  * and the execution DAG has a filter on the key, we can avoid launching tasks
  * on partitions that don't have the range covering the key.
@@ -34,9 +34,9 @@ class PartitionPruningRDD[T: ClassManifest](
     @transient partitionFilterFunc: Int => Boolean)
   extends RDD[T](prev.context, List(new PruneDependency(prev, partitionFilterFunc))) {
 
-  override def compute(split: Split, context: TaskContext) = firstParent[T].iterator(
-    split.asInstanceOf[PartitionPruningRDDSplit].parentSplit, context)
+  override def compute(split: Partition, context: TaskContext) = firstParent[T].iterator(
+    split.asInstanceOf[PartitionPruningRDDPartition].parentSplit, context)
 
-  override protected def getSplits: Array[Split] =
+  override protected def getPartitions: Array[Partition] =
     getDependencies.head.asInstanceOf[PruneDependency[T]].partitions
 }
diff --git a/core/src/main/scala/spark/rdd/PipedRDD.scala b/core/src/main/scala/spark/rdd/PipedRDD.scala
index 56032a8659..962a1b21ad 100644
--- a/core/src/main/scala/spark/rdd/PipedRDD.scala
+++ b/core/src/main/scala/spark/rdd/PipedRDD.scala
@@ -8,7 +8,7 @@ import scala.collection.JavaConversions._
 import scala.collection.mutable.ArrayBuffer
 import scala.io.Source
 
-import spark.{RDD, SparkEnv, Split, TaskContext}
+import spark.{RDD, SparkEnv, Partition, TaskContext}
 
 
 /**
@@ -27,9 +27,9 @@ class PipedRDD[T: ClassManifest](
   // using a standard StringTokenizer (i.e. by spaces)
   def this(prev: RDD[T], command: String) = this(prev, PipedRDD.tokenize(command))
 
-  override def getSplits: Array[Split] = firstParent[T].splits
+  override def getPartitions: Array[Partition] = firstParent[T].partitions
 
-  override def compute(split: Split, context: TaskContext): Iterator[String] = {
+  override def compute(split: Partition, context: TaskContext): Iterator[String] = {
     val pb = new ProcessBuilder(command)
     // Add the environmental variables to the process.
     val currentEnvVars = pb.environment()
diff --git a/core/src/main/scala/spark/rdd/SampledRDD.scala b/core/src/main/scala/spark/rdd/SampledRDD.scala
index f2a144e2e0..243673f151 100644
--- a/core/src/main/scala/spark/rdd/SampledRDD.scala
+++ b/core/src/main/scala/spark/rdd/SampledRDD.scala
@@ -5,10 +5,10 @@ import java.util.Random
 import cern.jet.random.Poisson
 import cern.jet.random.engine.DRand
 
-import spark.{RDD, Split, TaskContext}
+import spark.{RDD, Partition, TaskContext}
 
 private[spark]
-class SampledRDDSplit(val prev: Split, val seed: Int) extends Split with Serializable {
+class SampledRDDPartition(val prev: Partition, val seed: Int) extends Partition with Serializable {
   override val index: Int = prev.index
 }
 
@@ -19,16 +19,16 @@ class SampledRDD[T: ClassManifest](
     seed: Int)
   extends RDD[T](prev) {
 
-  override def getSplits: Array[Split] = {
+  override def getPartitions: Array[Partition] = {
     val rg = new Random(seed)
-    firstParent[T].splits.map(x => new SampledRDDSplit(x, rg.nextInt))
+    firstParent[T].partitions.map(x => new SampledRDDPartition(x, rg.nextInt))
   }
 
-  override def getPreferredLocations(split: Split): Seq[String] =
-    firstParent[T].preferredLocations(split.asInstanceOf[SampledRDDSplit].prev)
+  override def getPreferredLocations(split: Partition): Seq[String] =
+    firstParent[T].preferredLocations(split.asInstanceOf[SampledRDDPartition].prev)
 
-  override def compute(splitIn: Split, context: TaskContext): Iterator[T] = {
-    val split = splitIn.asInstanceOf[SampledRDDSplit]
+  override def compute(splitIn: Partition, context: TaskContext): Iterator[T] = {
+    val split = splitIn.asInstanceOf[SampledRDDPartition]
     if (withReplacement) {
       // For large datasets, the expected number of occurrences of each element in a sample with
       // replacement is Poisson(frac). We use that to get a count for each element.
diff --git a/core/src/main/scala/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/spark/rdd/ShuffledRDD.scala
index bf69b5150b..c2f118305f 100644
--- a/core/src/main/scala/spark/rdd/ShuffledRDD.scala
+++ b/core/src/main/scala/spark/rdd/ShuffledRDD.scala
@@ -1,9 +1,9 @@
 package spark.rdd
 
-import spark.{Partitioner, RDD, SparkEnv, ShuffleDependency, Split, TaskContext}
+import spark.{Partitioner, RDD, SparkEnv, ShuffleDependency, Partition, TaskContext}
 import spark.SparkContext._
 
-private[spark] class ShuffledRDDSplit(val idx: Int) extends Split {
+private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition {
   override val index = idx
   override def hashCode(): Int = idx
 }
@@ -22,11 +22,11 @@ class ShuffledRDD[K, V](
 
   override val partitioner = Some(part)
 
-  override def getSplits: Array[Split] = {
-    Array.tabulate[Split](part.numPartitions)(i => new ShuffledRDDSplit(i)) 
+  override def getPartitions: Array[Partition] = {
+    Array.tabulate[Partition](part.numPartitions)(i => new ShuffledRDDPartition(i))
   }
 
-  override def compute(split: Split, context: TaskContext): Iterator[(K, V)] = {
+  override def compute(split: Partition, context: TaskContext): Iterator[(K, V)] = {
     val shuffledId = dependencies.head.asInstanceOf[ShuffleDependency[K, V]].shuffleId
     SparkEnv.get.shuffleFetcher.fetch[K, V](shuffledId, split.index)
   }
diff --git a/core/src/main/scala/spark/rdd/UnionRDD.scala b/core/src/main/scala/spark/rdd/UnionRDD.scala
index ebc0068228..2c52a67e22 100644
--- a/core/src/main/scala/spark/rdd/UnionRDD.scala
+++ b/core/src/main/scala/spark/rdd/UnionRDD.scala
@@ -1,13 +1,13 @@
 package spark.rdd
 
 import scala.collection.mutable.ArrayBuffer
-import spark.{Dependency, RangeDependency, RDD, SparkContext, Split, TaskContext}
+import spark.{Dependency, RangeDependency, RDD, SparkContext, Partition, TaskContext}
 import java.io.{ObjectOutputStream, IOException}
 
-private[spark] class UnionSplit[T: ClassManifest](idx: Int, rdd: RDD[T], splitIndex: Int)
-  extends Split {
+private[spark] class UnionPartition[T: ClassManifest](idx: Int, rdd: RDD[T], splitIndex: Int)
+  extends Partition {
 
-  var split: Split = rdd.splits(splitIndex)
+  var split: Partition = rdd.partitions(splitIndex)
 
   def iterator(context: TaskContext) = rdd.iterator(split, context)
 
@@ -18,7 +18,7 @@ private[spark] class UnionSplit[T: ClassManifest](idx: Int, rdd: RDD[T], splitIn
   @throws(classOf[IOException])
   private def writeObject(oos: ObjectOutputStream) {
     // Update the reference to parent split at the time of task serialization
-    split = rdd.splits(splitIndex)
+    split = rdd.partitions(splitIndex)
     oos.defaultWriteObject()
   }
 }
@@ -28,11 +28,11 @@ class UnionRDD[T: ClassManifest](
     @transient var rdds: Seq[RDD[T]])
   extends RDD[T](sc, Nil) {  // Nil since we implement getDependencies
 
-  override def getSplits: Array[Split] = {
-    val array = new Array[Split](rdds.map(_.splits.size).sum)
+  override def getPartitions: Array[Partition] = {
+    val array = new Array[Partition](rdds.map(_.partitions.size).sum)
     var pos = 0
-    for (rdd <- rdds; split <- rdd.splits) {
-      array(pos) = new UnionSplit(pos, rdd, split.index)
+    for (rdd <- rdds; split <- rdd.partitions) {
+      array(pos) = new UnionPartition(pos, rdd, split.index)
       pos += 1
     }
     array
@@ -42,15 +42,15 @@ class UnionRDD[T: ClassManifest](
     val deps = new ArrayBuffer[Dependency[_]]
     var pos = 0
     for (rdd <- rdds) {
-      deps += new RangeDependency(rdd, 0, pos, rdd.splits.size)
-      pos += rdd.splits.size
+      deps += new RangeDependency(rdd, 0, pos, rdd.partitions.size)
+      pos += rdd.partitions.size
     }
     deps
   }
 
-  override def compute(s: Split, context: TaskContext): Iterator[T] =
-    s.asInstanceOf[UnionSplit[T]].iterator(context)
+  override def compute(s: Partition, context: TaskContext): Iterator[T] =
+    s.asInstanceOf[UnionPartition[T]].iterator(context)
 
-  override def getPreferredLocations(s: Split): Seq[String] =
-    s.asInstanceOf[UnionSplit[T]].preferredLocations()
+  override def getPreferredLocations(s: Partition): Seq[String] =
+    s.asInstanceOf[UnionPartition[T]].preferredLocations()
 }
diff --git a/core/src/main/scala/spark/rdd/ZippedRDD.scala b/core/src/main/scala/spark/rdd/ZippedRDD.scala
index 1ce70268bb..e80ec17aa5 100644
--- a/core/src/main/scala/spark/rdd/ZippedRDD.scala
+++ b/core/src/main/scala/spark/rdd/ZippedRDD.scala
@@ -1,17 +1,17 @@
 package spark.rdd
 
-import spark.{OneToOneDependency, RDD, SparkContext, Split, TaskContext}
+import spark.{OneToOneDependency, RDD, SparkContext, Partition, TaskContext}
 import java.io.{ObjectOutputStream, IOException}
 
 
-private[spark] class ZippedSplit[T: ClassManifest, U: ClassManifest](
+private[spark] class ZippedPartition[T: ClassManifest, U: ClassManifest](
     idx: Int,
     @transient rdd1: RDD[T],
     @transient rdd2: RDD[U]
-  ) extends Split {
+  ) extends Partition {
 
-  var split1 = rdd1.splits(idx)
-  var split2 = rdd1.splits(idx)
+  var split1 = rdd1.partitions(idx)
+  var split2 = rdd1.partitions(idx)
   override val index: Int = idx
 
   def splits = (split1, split2)
@@ -19,8 +19,8 @@ private[spark] class ZippedSplit[T: ClassManifest, U: ClassManifest](
   @throws(classOf[IOException])
   private def writeObject(oos: ObjectOutputStream) {
     // Update the reference to parent split at the time of task serialization
-    split1 = rdd1.splits(idx)
-    split2 = rdd2.splits(idx)
+    split1 = rdd1.partitions(idx)
+    split2 = rdd2.partitions(idx)
     oos.defaultWriteObject()
   }
 }
@@ -31,24 +31,24 @@ class ZippedRDD[T: ClassManifest, U: ClassManifest](
     var rdd2: RDD[U])
   extends RDD[(T, U)](sc, List(new OneToOneDependency(rdd1), new OneToOneDependency(rdd2))) {
 
-  override def getSplits: Array[Split] = {
-    if (rdd1.splits.size != rdd2.splits.size) {
+  override def getPartitions: Array[Partition] = {
+    if (rdd1.partitions.size != rdd2.partitions.size) {
       throw new IllegalArgumentException("Can't zip RDDs with unequal numbers of partitions")
     }
-    val array = new Array[Split](rdd1.splits.size)
-    for (i <- 0 until rdd1.splits.size) {
-      array(i) = new ZippedSplit(i, rdd1, rdd2)
+    val array = new Array[Partition](rdd1.partitions.size)
+    for (i <- 0 until rdd1.partitions.size) {
+      array(i) = new ZippedPartition(i, rdd1, rdd2)
     }
     array
   }
 
-  override def compute(s: Split, context: TaskContext): Iterator[(T, U)] = {
-    val (split1, split2) = s.asInstanceOf[ZippedSplit[T, U]].splits
+  override def compute(s: Partition, context: TaskContext): Iterator[(T, U)] = {
+    val (split1, split2) = s.asInstanceOf[ZippedPartition[T, U]].splits
     rdd1.iterator(split1, context).zip(rdd2.iterator(split2, context))
   }
 
-  override def getPreferredLocations(s: Split): Seq[String] = {
-    val (split1, split2) = s.asInstanceOf[ZippedSplit[T, U]].splits
+  override def getPreferredLocations(s: Partition): Seq[String] = {
+    val (split1, split2) = s.asInstanceOf[ZippedPartition[T, U]].splits
     rdd1.preferredLocations(split1).intersect(rdd2.preferredLocations(split2))
   }
 
diff --git a/core/src/main/scala/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/spark/scheduler/DAGScheduler.scala
index 319eef6978..bf0837c066 100644
--- a/core/src/main/scala/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/spark/scheduler/DAGScheduler.scala
@@ -106,7 +106,7 @@ class DAGScheduler(
 
   private def getCacheLocs(rdd: RDD[_]): Array[List[String]] = {
     if (!cacheLocs.contains(rdd.id)) {
-      val blockIds = rdd.splits.indices.map(index=> "rdd_%d_%d".format(rdd.id, index)).toArray
+      val blockIds = rdd.partitions.indices.map(index=> "rdd_%d_%d".format(rdd.id, index)).toArray
       cacheLocs(rdd.id) = blockManagerMaster.getLocations(blockIds).map {
         locations => locations.map(_.ip).toList
       }.toArray
@@ -141,9 +141,9 @@ class DAGScheduler(
   private def newStage(rdd: RDD[_], shuffleDep: Option[ShuffleDependency[_,_]], priority: Int): Stage = {
     if (shuffleDep != None) {
       // Kind of ugly: need to register RDDs with the cache and map output tracker here
-      // since we can't do it in the RDD constructor because # of splits is unknown
+      // since we can't do it in the RDD constructor because # of partitions is unknown
       logInfo("Registering RDD " + rdd.id + " (" + rdd.origin + ")")
-      mapOutputTracker.registerShuffle(shuffleDep.get.shuffleId, rdd.splits.size)
+      mapOutputTracker.registerShuffle(shuffleDep.get.shuffleId, rdd.partitions.size)
     }
     val id = nextStageId.getAndIncrement()
     val stage = new Stage(id, rdd, shuffleDep, getParentStages(rdd, priority), priority)
@@ -162,7 +162,7 @@ class DAGScheduler(
       if (!visited(r)) {
         visited += r
         // Kind of ugly: need to register RDDs with the cache here since
-        // we can't do it in its constructor because # of splits is unknown
+        // we can't do it in its constructor because # of partitions is unknown
         for (dep <- r.dependencies) {
           dep match {
             case shufDep: ShuffleDependency[_,_] =>
@@ -257,7 +257,7 @@ class DAGScheduler(
   {
     val listener = new ApproximateActionListener(rdd, func, evaluator, timeout)
     val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
-    val partitions = (0 until rdd.splits.size).toArray
+    val partitions = (0 until rdd.partitions.size).toArray
     eventQueue.put(JobSubmitted(rdd, func2, partitions, false, callSite, listener))
     return listener.awaitResult()    // Will throw an exception if the job fails
   }
@@ -386,7 +386,7 @@ class DAGScheduler(
         try {
           SparkEnv.set(env)
           val rdd = job.finalStage.rdd
-          val split = rdd.splits(job.partitions(0))
+          val split = rdd.partitions(job.partitions(0))
           val taskContext = new TaskContext(job.finalStage.id, job.partitions(0), 0)
           try {
             val result = job.func(taskContext, rdd.iterator(split, taskContext))
@@ -672,7 +672,7 @@ class DAGScheduler(
       return cached
     }
     // If the RDD has some placement preferences (as is the case for input RDDs), get those
-    val rddPrefs = rdd.preferredLocations(rdd.splits(partition)).toList
+    val rddPrefs = rdd.preferredLocations(rdd.partitions(partition)).toList
     if (rddPrefs != Nil) {
       return rddPrefs
     }
diff --git a/core/src/main/scala/spark/scheduler/ResultTask.scala b/core/src/main/scala/spark/scheduler/ResultTask.scala
index 8cd4c661eb..1721f78f48 100644
--- a/core/src/main/scala/spark/scheduler/ResultTask.scala
+++ b/core/src/main/scala/spark/scheduler/ResultTask.scala
@@ -67,7 +67,7 @@ private[spark] class ResultTask[T, U](
   var split = if (rdd == null) {
     null
   } else {
-    rdd.splits(partition)
+    rdd.partitions(partition)
   }
 
   override def run(attemptId: Long): U = {
@@ -85,7 +85,7 @@ private[spark] class ResultTask[T, U](
 
   override def writeExternal(out: ObjectOutput) {
     RDDCheckpointData.synchronized {
-      split = rdd.splits(partition)
+      split = rdd.partitions(partition)
       out.writeInt(stageId)
       val bytes = ResultTask.serializeInfo(
         stageId, rdd, func.asInstanceOf[(TaskContext, Iterator[_]) => _])
@@ -107,6 +107,6 @@ private[spark] class ResultTask[T, U](
     func = func_.asInstanceOf[(TaskContext, Iterator[T]) => U]
     partition = in.readInt()
     val outputId = in.readInt()
-    split = in.readObject().asInstanceOf[Split]
+    split = in.readObject().asInstanceOf[Partition]
   }
 }
diff --git a/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala
index bed9f1864f..59ee3c0a09 100644
--- a/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala
@@ -86,12 +86,12 @@ private[spark] class ShuffleMapTask(
   var split = if (rdd == null) {
     null
   } else {
-    rdd.splits(partition)
+    rdd.partitions(partition)
   }
 
   override def writeExternal(out: ObjectOutput) {
     RDDCheckpointData.synchronized {
-      split = rdd.splits(partition)
+      split = rdd.partitions(partition)
       out.writeInt(stageId)
       val bytes = ShuffleMapTask.serializeInfo(stageId, rdd, dep)
       out.writeInt(bytes.length)
@@ -112,7 +112,7 @@ private[spark] class ShuffleMapTask(
     dep = dep_
     partition = in.readInt()
     generation = in.readLong()
-    split = in.readObject().asInstanceOf[Split]
+    split = in.readObject().asInstanceOf[Partition]
   }
 
   override def run(attemptId: Long): MapStatus = {
diff --git a/core/src/main/scala/spark/scheduler/Stage.scala b/core/src/main/scala/spark/scheduler/Stage.scala
index 374114d870..552061e46b 100644
--- a/core/src/main/scala/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/spark/scheduler/Stage.scala
@@ -28,7 +28,7 @@ private[spark] class Stage(
   extends Logging {
   
   val isShuffleMap = shuffleDep != None
-  val numPartitions = rdd.splits.size
+  val numPartitions = rdd.partitions.size
   val outputLocs = Array.fill[List[MapStatus]](numPartitions)(Nil)
   var numAvailableOutputs = 0
 
diff --git a/core/src/main/scala/spark/storage/BlockManager.scala b/core/src/main/scala/spark/storage/BlockManager.scala
index 2e7db60841..2462721fb8 100644
--- a/core/src/main/scala/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/spark/storage/BlockManager.scala
@@ -513,7 +513,7 @@ class BlockManager(
       }
     }
 
-    // Split local and remote blocks. Remote blocks are further split into FetchRequests of size
+    // Partition local and remote blocks. Remote blocks are further split into FetchRequests of size
     // at most maxBytesInFlight in order to limit the amount of data in flight.
     val remoteRequests = new ArrayBuffer[FetchRequest]
     for ((address, blockInfos) <- blocksByAddress) {
diff --git a/core/src/main/scala/spark/storage/StorageUtils.scala b/core/src/main/scala/spark/storage/StorageUtils.scala
index 5f72b67b2c..dec47a9d41 100644
--- a/core/src/main/scala/spark/storage/StorageUtils.scala
+++ b/core/src/main/scala/spark/storage/StorageUtils.scala
@@ -63,7 +63,7 @@ object StorageUtils {
       val rddName = Option(rdd.name).getOrElse(rddKey)
       val rddStorageLevel = rdd.getStorageLevel
 
-      RDDInfo(rddId, rddName, rddStorageLevel, rddBlocks.length, rdd.splits.size, memSize, diskSize)
+      RDDInfo(rddId, rddName, rddStorageLevel, rddBlocks.length, rdd.partitions.size, memSize, diskSize)
     }.toArray
   }
 
diff --git a/core/src/test/scala/spark/CheckpointSuite.scala b/core/src/test/scala/spark/CheckpointSuite.scala
index 51ff966ae4..3e5ffa81d6 100644
--- a/core/src/test/scala/spark/CheckpointSuite.scala
+++ b/core/src/test/scala/spark/CheckpointSuite.scala
@@ -34,7 +34,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     testCheckpointing(_.sample(false, 0.5, 0))
     testCheckpointing(_.glom())
     testCheckpointing(_.mapPartitions(_.map(_.toString)))
-    testCheckpointing(r => new MapPartitionsWithSplitRDD(r,
+    testCheckpointing(r => new MapPartitionsWithIndexRDD(r,
       (i: Int, iter: Iterator[Int]) => iter.map(_.toString), false ))
     testCheckpointing(_.map(x => (x % 2, 1)).reduceByKey(_ + _).mapValues(_.toString))
     testCheckpointing(_.map(x => (x % 2, 1)).reduceByKey(_ + _).flatMapValues(x => 1 to x))
@@ -43,14 +43,14 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
 
   test("ParallelCollection") {
     val parCollection = sc.makeRDD(1 to 4, 2)
-    val numSplits = parCollection.splits.size
+    val numPartitions = parCollection.partitions.size
     parCollection.checkpoint()
     assert(parCollection.dependencies === Nil)
     val result = parCollection.collect()
     assert(sc.checkpointFile[Int](parCollection.getCheckpointFile.get).collect() === result)
     assert(parCollection.dependencies != Nil)
-    assert(parCollection.splits.length === numSplits)
-    assert(parCollection.splits.toList === parCollection.checkpointData.get.getSplits.toList)
+    assert(parCollection.partitions.length === numPartitions)
+    assert(parCollection.partitions.toList === parCollection.checkpointData.get.getPartitions.toList)
     assert(parCollection.collect() === result)
   }
 
@@ -59,13 +59,13 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     val blockManager = SparkEnv.get.blockManager
     blockManager.putSingle(blockId, "test", StorageLevel.MEMORY_ONLY)
     val blockRDD = new BlockRDD[String](sc, Array(blockId))
-    val numSplits = blockRDD.splits.size
+    val numPartitions = blockRDD.partitions.size
     blockRDD.checkpoint()
     val result = blockRDD.collect()
     assert(sc.checkpointFile[String](blockRDD.getCheckpointFile.get).collect() === result)
     assert(blockRDD.dependencies != Nil)
-    assert(blockRDD.splits.length === numSplits)
-    assert(blockRDD.splits.toList === blockRDD.checkpointData.get.getSplits.toList)
+    assert(blockRDD.partitions.length === numPartitions)
+    assert(blockRDD.partitions.toList === blockRDD.checkpointData.get.getPartitions.toList)
     assert(blockRDD.collect() === result)
   }
 
@@ -79,9 +79,9 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
   test("UnionRDD") {
     def otherRDD = sc.makeRDD(1 to 10, 1)
 
-    // Test whether the size of UnionRDDSplits reduce in size after parent RDD is checkpointed.
+    // Test whether the size of UnionRDDPartitions reduce in size after parent RDD is checkpointed.
     // Current implementation of UnionRDD has transient reference to parent RDDs,
-    // so only the splits will reduce in serialized size, not the RDD.
+    // so only the partitions will reduce in serialized size, not the RDD.
     testCheckpointing(_.union(otherRDD), false, true)
     testParentCheckpointing(_.union(otherRDD), false, true)
   }
@@ -91,21 +91,21 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     testCheckpointing(new CartesianRDD(sc, _, otherRDD))
 
     // Test whether size of CoalescedRDD reduce in size after parent RDD is checkpointed
-    // Current implementation of CoalescedRDDSplit has transient reference to parent RDD,
-    // so only the RDD will reduce in serialized size, not the splits.
+    // Current implementation of CoalescedRDDPartition has transient reference to parent RDD,
+    // so only the RDD will reduce in serialized size, not the partitions.
     testParentCheckpointing(new CartesianRDD(sc, _, otherRDD), true, false)
 
-    // Test that the CartesianRDD updates parent splits (CartesianRDD.s1/s2) after
-    // the parent RDD has been checkpointed and parent splits have been changed to HadoopSplits.
+    // Test that the CartesianRDD updates parent partitions (CartesianRDD.s1/s2) after
+    // the parent RDD has been checkpointed and parent partitions have been changed to HadoopPartitions.
     // Note that this test is very specific to the current implementation of CartesianRDD.
     val ones = sc.makeRDD(1 to 100, 10).map(x => x)
     ones.checkpoint() // checkpoint that MappedRDD
     val cartesian = new CartesianRDD(sc, ones, ones)
     val splitBeforeCheckpoint =
-      serializeDeserialize(cartesian.splits.head.asInstanceOf[CartesianSplit])
+      serializeDeserialize(cartesian.partitions.head.asInstanceOf[CartesianPartition])
     cartesian.count() // do the checkpointing
     val splitAfterCheckpoint =
-      serializeDeserialize(cartesian.splits.head.asInstanceOf[CartesianSplit])
+      serializeDeserialize(cartesian.partitions.head.asInstanceOf[CartesianPartition])
     assert(
       (splitAfterCheckpoint.s1 != splitBeforeCheckpoint.s1) &&
         (splitAfterCheckpoint.s2 != splitBeforeCheckpoint.s2),
@@ -117,24 +117,24 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     testCheckpointing(_.coalesce(2))
 
     // Test whether size of CoalescedRDD reduce in size after parent RDD is checkpointed
-    // Current implementation of CoalescedRDDSplit has transient reference to parent RDD,
-    // so only the RDD will reduce in serialized size, not the splits.
+    // Current implementation of CoalescedRDDPartition has transient reference to parent RDD,
+    // so only the RDD will reduce in serialized size, not the partitions.
     testParentCheckpointing(_.coalesce(2), true, false)
 
-    // Test that the CoalescedRDDSplit updates parent splits (CoalescedRDDSplit.parents) after
-    // the parent RDD has been checkpointed and parent splits have been changed to HadoopSplits.
-    // Note that this test is very specific to the current implementation of CoalescedRDDSplits
+    // Test that the CoalescedRDDPartition updates parent partitions (CoalescedRDDPartition.parents) after
+    // the parent RDD has been checkpointed and parent partitions have been changed to HadoopPartitions.
+    // Note that this test is very specific to the current implementation of CoalescedRDDPartitions
     val ones = sc.makeRDD(1 to 100, 10).map(x => x)
     ones.checkpoint() // checkpoint that MappedRDD
     val coalesced = new CoalescedRDD(ones, 2)
     val splitBeforeCheckpoint =
-      serializeDeserialize(coalesced.splits.head.asInstanceOf[CoalescedRDDSplit])
+      serializeDeserialize(coalesced.partitions.head.asInstanceOf[CoalescedRDDPartition])
     coalesced.count() // do the checkpointing
     val splitAfterCheckpoint =
-      serializeDeserialize(coalesced.splits.head.asInstanceOf[CoalescedRDDSplit])
+      serializeDeserialize(coalesced.partitions.head.asInstanceOf[CoalescedRDDPartition])
     assert(
       splitAfterCheckpoint.parents.head != splitBeforeCheckpoint.parents.head,
-      "CoalescedRDDSplit.parents not updated after parent RDD checkpointed"
+      "CoalescedRDDPartition.parents not updated after parent RDD checkpointed"
     )
   }
 
@@ -156,8 +156,8 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
       rdd => new ZippedRDD(sc, rdd, rdd.map(x => x)), true, false)
 
     // Test whether size of ZippedRDD reduce in size after parent RDD is checkpointed
-    // Current implementation of ZippedRDDSplit has transient references to parent RDDs,
-    // so only the RDD will reduce in serialized size, not the splits.
+    // Current implementation of ZippedRDDPartitions has transient references to parent RDDs,
+    // so only the RDD will reduce in serialized size, not the partitions.
     testParentCheckpointing(
       rdd => new ZippedRDD(sc, rdd, rdd.map(x => x)), true, false)
   }
@@ -165,21 +165,21 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
   /**
    * Test checkpointing of the final RDD generated by the given operation. By default,
    * this method tests whether the size of serialized RDD has reduced after checkpointing or not.
-   * It can also test whether the size of serialized RDD splits has reduced after checkpointing or
-   * not, but this is not done by default as usually the splits do not refer to any RDD and
+   * It can also test whether the size of serialized RDD partitions has reduced after checkpointing or
+   * not, but this is not done by default as usually the partitions do not refer to any RDD and
    * therefore never store the lineage.
    */
   def testCheckpointing[U: ClassManifest](
       op: (RDD[Int]) => RDD[U],
       testRDDSize: Boolean = true,
-      testRDDSplitSize: Boolean = false
+      testRDDPartitionSize: Boolean = false
     ) {
     // Generate the final RDD using given RDD operation
     val baseRDD = generateLongLineageRDD()
     val operatedRDD = op(baseRDD)
     val parentRDD = operatedRDD.dependencies.headOption.orNull
     val rddType = operatedRDD.getClass.getSimpleName
-    val numSplits = operatedRDD.splits.length
+    val numPartitions = operatedRDD.partitions.length
 
     // Find serialized sizes before and after the checkpoint
     val (rddSizeBeforeCheckpoint, splitSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD)
@@ -193,11 +193,11 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     // Test whether dependencies have been changed from its earlier parent RDD
     assert(operatedRDD.dependencies.head.rdd != parentRDD)
 
-    // Test whether the splits have been changed to the new Hadoop splits
-    assert(operatedRDD.splits.toList === operatedRDD.checkpointData.get.getSplits.toList)
+    // Test whether the partitions have been changed to the new Hadoop partitions
+    assert(operatedRDD.partitions.toList === operatedRDD.checkpointData.get.getPartitions.toList)
 
-    // Test whether the number of splits is same as before
-    assert(operatedRDD.splits.length === numSplits)
+    // Test whether the number of partitions is same as before
+    assert(operatedRDD.partitions.length === numPartitions)
 
     // Test whether the data in the checkpointed RDD is same as original
     assert(operatedRDD.collect() === result)
@@ -215,18 +215,18 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
       )
     }
 
-    // Test whether serialized size of the splits has reduced. If the splits
-    // do not have any non-transient reference to another RDD or another RDD's splits, it
+    // Test whether serialized size of the partitions has reduced. If the partitions
+    // do not have any non-transient reference to another RDD or another RDD's partitions, it
     // does not refer to a lineage and therefore may not reduce in size after checkpointing.
-    // However, if the original splits before checkpointing do refer to a parent RDD, the splits
+    // However, if the original partitions before checkpointing do refer to a parent RDD, the partitions
     // must be forgotten after checkpointing (to remove all reference to parent RDDs) and
-    // replaced with the HadoopSplits of the checkpointed RDD.
-    if (testRDDSplitSize) {
-      logInfo("Size of " + rddType + " splits "
+    // replaced with the HadooPartitions of the checkpointed RDD.
+    if (testRDDPartitionSize) {
+      logInfo("Size of " + rddType + " partitions "
         + "[" + splitSizeBeforeCheckpoint + " --> " + splitSizeAfterCheckpoint + "]")
       assert(
         splitSizeAfterCheckpoint < splitSizeBeforeCheckpoint,
-        "Size of " + rddType + " splits did not reduce after checkpointing " +
+        "Size of " + rddType + " partitions did not reduce after checkpointing " +
           "[" + splitSizeBeforeCheckpoint + " --> " + splitSizeAfterCheckpoint + "]"
       )
     }
@@ -235,13 +235,13 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
   /**
    * Test whether checkpointing of the parent of the generated RDD also
    * truncates the lineage or not. Some RDDs like CoGroupedRDD hold on to its parent
-   * RDDs splits. So even if the parent RDD is checkpointed and its splits changed,
-   * this RDD will remember the splits and therefore potentially the whole lineage.
+   * RDDs partitions. So even if the parent RDD is checkpointed and its partitions changed,
+   * this RDD will remember the partitions and therefore potentially the whole lineage.
    */
   def testParentCheckpointing[U: ClassManifest](
       op: (RDD[Int]) => RDD[U],
       testRDDSize: Boolean,
-      testRDDSplitSize: Boolean
+      testRDDPartitionSize: Boolean
     ) {
     // Generate the final RDD using given RDD operation
     val baseRDD = generateLongLineageRDD()
@@ -250,9 +250,9 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     val rddType = operatedRDD.getClass.getSimpleName
     val parentRDDType = parentRDD.getClass.getSimpleName
 
-    // Get the splits and dependencies of the parent in case they're lazily computed
+    // Get the partitions and dependencies of the parent in case they're lazily computed
     parentRDD.dependencies
-    parentRDD.splits
+    parentRDD.partitions
 
     // Find serialized sizes before and after the checkpoint
     val (rddSizeBeforeCheckpoint, splitSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD)
@@ -275,16 +275,16 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
       )
     }
 
-    // Test whether serialized size of the splits has reduced because of its parent being
-    // checkpointed. If the splits do not have any non-transient reference to another RDD
-    // or another RDD's splits, it does not refer to a lineage and therefore may not reduce
-    // in size after checkpointing. However, if the splits do refer to the *splits* of a parent
-    // RDD, then these splits must update reference to the parent RDD splits as the parent RDD's
-    // splits must have changed after checkpointing.
-    if (testRDDSplitSize) {
+    // Test whether serialized size of the partitions has reduced because of its parent being
+    // checkpointed. If the partitions do not have any non-transient reference to another RDD
+    // or another RDD's partitions, it does not refer to a lineage and therefore may not reduce
+    // in size after checkpointing. However, if the partitions do refer to the *partitions* of a parent
+    // RDD, then these partitions must update reference to the parent RDD partitions as the parent RDD's
+    // partitions must have changed after checkpointing.
+    if (testRDDPartitionSize) {
       assert(
         splitSizeAfterCheckpoint < splitSizeBeforeCheckpoint,
-        "Size of " + rddType + " splits did not reduce after checkpointing parent " + parentRDDType +
+        "Size of " + rddType + " partitions did not reduce after checkpointing parent " + parentRDDType +
           "[" + splitSizeBeforeCheckpoint + " --> " + splitSizeAfterCheckpoint + "]"
       )
     }
@@ -321,12 +321,12 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
   }
 
   /**
-   * Get serialized sizes of the RDD and its splits, in order to test whether the size shrinks
+   * Get serialized sizes of the RDD and its partitions, in order to test whether the size shrinks
    * upon checkpointing. Ignores the checkpointData field, which may grow when we checkpoint.
    */
   def getSerializedSizes(rdd: RDD[_]): (Int, Int) = {
     (Utils.serialize(rdd).length - Utils.serialize(rdd.checkpointData).length,
-     Utils.serialize(rdd.splits).length)
+     Utils.serialize(rdd.partitions).length)
   }
 
   /**
diff --git a/core/src/test/scala/spark/RDDSuite.scala b/core/src/test/scala/spark/RDDSuite.scala
index ffa866de75..9739ba869b 100644
--- a/core/src/test/scala/spark/RDDSuite.scala
+++ b/core/src/test/scala/spark/RDDSuite.scala
@@ -33,6 +33,11 @@ class RDDSuite extends FunSuite with LocalSparkContext {
     }
     assert(partitionSumsWithSplit.collect().toList === List((0, 3), (1, 7)))
 
+    val partitionSumsWithIndex = nums.mapPartitionsWithIndex {
+      case(split, iter) => Iterator((split, iter.reduceLeft(_ + _)))
+    }
+    assert(partitionSumsWithIndex.collect().toList === List((0, 3), (1, 7)))
+
     intercept[UnsupportedOperationException] {
       nums.filter(_ > 5).reduce(_ + _)
     }
@@ -97,12 +102,12 @@ class RDDSuite extends FunSuite with LocalSparkContext {
 
   test("caching with failures") {
     sc = new SparkContext("local", "test")
-    val onlySplit = new Split { override def index: Int = 0 }
+    val onlySplit = new Partition { override def index: Int = 0 }
     var shouldFail = true
     val rdd = new RDD[Int](sc, Nil) {
-      override def getSplits: Array[Split] = Array(onlySplit)
+      override def getPartitions: Array[Partition] = Array(onlySplit)
       override val getDependencies = List[Dependency[_]]()
-      override def compute(split: Split, context: TaskContext): Iterator[Int] = {
+      override def compute(split: Partition, context: TaskContext): Iterator[Int] = {
         if (shouldFail) {
           throw new Exception("injected failure")
         } else {
@@ -168,7 +173,7 @@ class RDDSuite extends FunSuite with LocalSparkContext {
     val data = sc.parallelize(1 to 10, 10)
     // Note that split number starts from 0, so > 8 means only 10th partition left.
     val prunedRdd = new PartitionPruningRDD(data, splitNum => splitNum > 8)
-    assert(prunedRdd.splits.size === 1)
+    assert(prunedRdd.partitions.size === 1)
     val prunedData = prunedRdd.collect()
     assert(prunedData.size === 1)
     assert(prunedData(0) === 10)
diff --git a/core/src/test/scala/spark/ShuffleSuite.scala b/core/src/test/scala/spark/ShuffleSuite.scala
index 50f2b294bf..92c3f67416 100644
--- a/core/src/test/scala/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/spark/ShuffleSuite.scala
@@ -222,7 +222,7 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
     sc = new SparkContext("local", "test")
     val emptyDir = Files.createTempDir()
     val file = sc.textFile(emptyDir.getAbsolutePath)
-    assert(file.splits.size == 0)
+    assert(file.partitions.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)
diff --git a/core/src/test/scala/spark/SortingSuite.scala b/core/src/test/scala/spark/SortingSuite.scala
index edb8c839fc..495f957e53 100644
--- a/core/src/test/scala/spark/SortingSuite.scala
+++ b/core/src/test/scala/spark/SortingSuite.scala
@@ -19,7 +19,7 @@ class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers w
     val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) }
     val pairs = sc.parallelize(pairArr, 2)
     val sorted = pairs.sortByKey()
-    assert(sorted.splits.size === 2)
+    assert(sorted.partitions.size === 2)
     assert(sorted.collect() === pairArr.sortBy(_._1))
   }
 
@@ -29,17 +29,17 @@ class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers w
     val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) }
     val pairs = sc.parallelize(pairArr, 2)
     val sorted = pairs.sortByKey(true, 1)
-    assert(sorted.splits.size === 1)
+    assert(sorted.partitions.size === 1)
     assert(sorted.collect() === pairArr.sortBy(_._1))
   }
   
-  test("large array with many splits") {
+  test("large array with many partitions") {
     sc = new SparkContext("local", "test")
     val rand = new scala.util.Random()
     val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) }
     val pairs = sc.parallelize(pairArr, 2)
     val sorted = pairs.sortByKey(true, 20)
-    assert(sorted.splits.size === 20)
+    assert(sorted.partitions.size === 20)
     assert(sorted.collect() === pairArr.sortBy(_._1))
   }
   
@@ -59,7 +59,7 @@ class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers w
     assert(pairs.sortByKey(false, 1).collect() === pairArr.sortWith((x, y) => x._1 > y._1))
   }
   
-  test("sort descending with many splits") {
+  test("sort descending with many partitions") {
     sc = new SparkContext("local", "test")
     val rand = new scala.util.Random()
     val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) }
diff --git a/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala
index 83663ac702..8de490eb86 100644
--- a/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala
@@ -24,7 +24,7 @@ import spark.MapOutputTracker
 import spark.RDD
 import spark.SparkContext
 import spark.SparkException
-import spark.Split
+import spark.Partition
 import spark.TaskContext
 import spark.TaskEndReason
 
@@ -144,18 +144,18 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
    * so we can test that DAGScheduler does not try to execute RDDs locally.
    */
   def makeRdd(
-        numSplits: Int,
+        numPartitions: Int,
         dependencies: List[Dependency[_]],
         locations: Seq[Seq[String]] = Nil
       ): MyRDD = {
-    val maxSplit = numSplits - 1
+    val maxPartition = numPartitions - 1
     return new MyRDD(sc, dependencies) {
-      override def compute(split: Split, context: TaskContext): Iterator[(Int, Int)] =
+      override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] =
         throw new RuntimeException("should not be reached")
-      override def getSplits() = (0 to maxSplit).map(i => new Split {
+      override def getPartitions = (0 to maxPartition).map(i => new Partition {
         override def index = i
       }).toArray
-      override def getPreferredLocations(split: Split): Seq[String] =
+      override def getPreferredLocations(split: Partition): Seq[String] =
         if (locations.isDefinedAt(split.index))
           locations(split.index)
         else
@@ -295,11 +295,11 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
    * collect the result of the job via callbacks from DAGScheduler.
    */
   def submitRdd(rdd: MyRDD, allowLocal: Boolean = false): (JobWaiter[Int], Array[Int]) = {
-    val resultArray = new Array[Int](rdd.splits.size)
+    val resultArray = new Array[Int](rdd.partitions.size)
     val (toSubmit, waiter) = scheduler.prepareJob[(Int, Int), Int](
         rdd,
         jobComputeFunc,
-        (0 to (rdd.splits.size - 1)),
+        (0 to (rdd.partitions.size - 1)),
         "test-site",
         allowLocal,
         (i: Int, value: Int) => resultArray(i) = value
@@ -355,10 +355,10 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
 
   test("local job") {
     val rdd = new MyRDD(sc, Nil) {
-      override def compute(split: Split, context: TaskContext): Iterator[(Int, Int)] =
+      override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] =
         Array(42 -> 0).iterator
-      override def getSplits() = Array( new Split { override def index = 0 } )
-      override def getPreferredLocations(split: Split) = Nil
+      override def getPartitions = Array( new Partition { override def index = 0 } )
+      override def getPreferredLocations(split: Partition) = Nil
       override def toString = "DAGSchedulerSuite Local RDD"
     }
     submitRdd(rdd, true)
diff --git a/core/src/test/scala/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/spark/scheduler/TaskContextSuite.scala
index a5db7103f5..647bcaf860 100644
--- a/core/src/test/scala/spark/scheduler/TaskContextSuite.scala
+++ b/core/src/test/scala/spark/scheduler/TaskContextSuite.scala
@@ -5,7 +5,7 @@ import org.scalatest.BeforeAndAfter
 import spark.TaskContext
 import spark.RDD
 import spark.SparkContext
-import spark.Split
+import spark.Partition
 import spark.LocalSparkContext
 
 class TaskContextSuite extends FunSuite with BeforeAndAfter with LocalSparkContext {
@@ -14,8 +14,8 @@ class TaskContextSuite extends FunSuite with BeforeAndAfter with LocalSparkConte
     var completed = false
     sc = new SparkContext("local", "test")
     val rdd = new RDD[String](sc, List()) {
-      override def getSplits = Array[Split](StubSplit(0))
-      override def compute(split: Split, context: TaskContext) = {
+      override def getPartitions = Array[Partition](StubPartition(0))
+      override def compute(split: Partition, context: TaskContext) = {
         context.addOnCompleteCallback(() => completed = true)
         sys.error("failed")
       }
@@ -28,5 +28,5 @@ class TaskContextSuite extends FunSuite with BeforeAndAfter with LocalSparkConte
     assert(completed === true)
   }
 
-  case class StubSplit(val index: Int) extends Split
-}
\ No newline at end of file
+  case class StubPartition(val index: Int) extends Partition
+}