Changes after building against Shark.

core/src/main/scala/org/apache/spark/SerializableWritable.scala

@@ -23,7 +23,8 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io.ObjectWritable
 import org.apache.hadoop.io.Writable
 
-private[spark] class SerializableWritable[T <: Writable](@transient var t: T) extends Serializable {
+/** <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span> */
+class SerializableWritable[T <: Writable](@transient var t: T) extends Serializable {
   def value = t
   override def toString = t.toString
 

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

@@ -53,18 +53,28 @@ import org.apache.spark.util.{ClosureCleaner, MetadataCleaner, MetadataCleanerTy
  *
  * @param config a Spark Config object describing the application configuration. Any settings in
  *   this config overrides the default configs as well as system properties.
- * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on. Can
- *   be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
- *   from a list of input files or InputFormats for the application.
  */
-class SparkContext(
-    config: SparkConf,
-    // This is used only by YARN for now, but should be relevant to other cluster types (Mesos,
-    // etc) too. This is typically generated from InputFormatInfo.computePreferredLocations. It
-    // contains a map from hostname to a list of input format splits on the host.
-    val preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map())
+class SparkContext(config: SparkConf)
   extends Logging {
 
+  // This is used only by YARN for now, but should be relevant to other cluster types (Mesos,
+  // etc) too. This is typically generated from InputFormatInfo.computePreferredLocations. It
+  // contains a map from hostname to a list of input format splits on the host.
+  private[spark] var preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map()
+
+  /**
+   * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+   * Alternative constructor for setting preferred locations where Spark will create executors.
+   *
+   * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on. Can
+   * be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
+   * from a list of input files or InputFormats for the application.
+   */
+  def this(config: SparkConf, preferredNodeLocationData: Map[String, Set[SplitInfo]]) = {
+    this(config)
+    this.preferredNodeLocationData = preferredNodeLocationData
+  }
+
   /**
    * Alternative constructor that allows setting common Spark properties directly
    *
@@ -606,6 +616,9 @@ class SparkContext(
   def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] =
     new UnionRDD(this, Seq(first) ++ rest)
 
+  /** Get an RDD that has no partitions or elements. */
+  def emptyRDD[T: ClassTag] = new EmptyRDD[T](this)
+
   // Methods for creating shared variables
 
   /**

core/src/main/scala/org/apache/spark/SparkEnv.scala

@@ -35,13 +35,14 @@ import org.apache.spark.storage._
 import org.apache.spark.util.{AkkaUtils, Utils}
 
 /**
+ * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
  * Holds all the runtime environment objects for a running Spark instance (either master or worker),
  * including the serializer, Akka actor system, block manager, map output tracker, etc. Currently
  * Spark code finds the SparkEnv through a thread-local variable, so each thread that accesses these
  * objects needs to have the right SparkEnv set. You can get the current environment with
  * SparkEnv.get (e.g. after creating a SparkContext) and set it with SparkEnv.set.
  */
-private[spark] class SparkEnv (
+class SparkEnv (
     val executorId: String,
     val actorSystem: ActorSystem,
     val serializer: Serializer,

core/src/main/scala/org/apache/spark/rdd/EmptyRDD.scala

@@ -22,9 +22,9 @@ import scala.reflect.ClassTag
 import org.apache.spark.{Partition, SparkContext, TaskContext}
 
 /**
- * An RDD that has no partitions and no elements..
+ * An RDD that has no partitions and no elements.
  */
-private[spark] class EmptyRDD[T: ClassTag](sc: SparkContext) extends RDD[T](sc, Nil) {
+class EmptyRDD[T: ClassTag](sc: SparkContext) extends RDD[T](sc, Nil) {
 
   override def getPartitions: Array[Partition] = Array.empty
 

core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala

@@ -43,7 +43,7 @@ private[spark] class UnionPartition[T: ClassTag](idx: Int, rdd: RDD[T], splitInd
   }
 }
 
-private[spark] class UnionRDD[T: ClassTag](
+class UnionRDD[T: ClassTag](
     sc: SparkContext,
     @transient var rdds: Seq[RDD[T]])
   extends RDD[T](sc, Nil) {  // Nil since we implement getDependencies

core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala

@@ -30,6 +30,7 @@ import org.apache.spark.Logging
 import org.apache.spark.deploy.SparkHadoopUtil
 
 /**
+ * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
  * Parses and holds information about inputFormat (and files) specified as a parameter.
  */
 class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Class[_],

core/src/main/scala/org/apache/spark/scheduler/SplitInfo.scala

@@ -21,7 +21,7 @@ import collection.mutable.ArrayBuffer
 
 // information about a specific split instance : handles both split instances.
 // So that we do not need to worry about the differences.
-private[spark]
+/** <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span> */
 class SplitInfo(val inputFormatClazz: Class[_], val hostLocation: String, val path: String,
                 val length: Long, val underlyingSplit: Any) {
   override def toString(): String = {

core/src/main/scala/org/apache/spark/util/Vector.scala

@@ -21,7 +21,7 @@ import scala.util.Random
 
 import org.apache.spark.util.random.XORShiftRandom
 
-@deprecated("Use Vector from Spark's mllib.linalg package instead.", "1.0.0")
+@deprecated("Use Vectors.dense from Spark's mllib.linalg package instead.", "1.0.0")
 class Vector(val elements: Array[Double]) extends Serializable {
   def length = elements.length