[SPARK-12244][SPARK-12245][STREAMING] Rename trackStateByKey to mapWithState and change tracking function signature

SPARK-12244:

Based on feedback from early users and personal experience attempting to explain it, the name trackStateByKey had two problem.
"trackState" is a completely new term which really does not give any intuition on what the operation is
the resultant data stream of objects returned by the function is called in docs as the "emitted" data for the lack of a better.
"mapWithState" makes sense because the API is like a mapping function like (Key, Value) => T with State as an additional parameter. The resultant data stream is "mapped data". So both problems are solved.

SPARK-12245:

From initial experiences, not having the key in the function makes it hard to return mapped stuff, as the whole information of the records is not there. Basically the user is restricted to doing something like mapValue() instead of map(). So adding the key as a parameter.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #10224 from tdas/rename.

10 files changed, 367 insertions, 358 deletions

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/State.scala b/streaming/src/main/scala/org/apache/spark/streaming/State.scala
index 604e64fc61..b47bdda2c2 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/State.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/State.scala
@@ -23,14 +23,14 @@ import org.apache.spark.annotation.Experimental
 
 /**
  * :: Experimental ::
- * Abstract class for getting and updating the tracked state in the `trackStateByKey` operation of
- * a [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair DStream]] (Scala) or a
- * [[org.apache.spark.streaming.api.java.JavaPairDStream JavaPairDStream]] (Java).
+ * Abstract class for getting and updating the state in mapping function used in the `mapWithState`
+ * operation of a [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair DStream]] (Scala)
+ * or a [[org.apache.spark.streaming.api.java.JavaPairDStream JavaPairDStream]] (Java).
  *
  * Scala example of using `State`:
  * {{{
- *    // A tracking function that maintains an integer state and return a String
- *    def trackStateFunc(data: Option[Int], state: State[Int]): Option[String] = {
+ *    // A mapping function that maintains an integer state and returns a String
+ *    def mappingFunction(key: String, value: Option[Int], state: State[Int]): Option[String] = {
  *      // Check if state exists
  *      if (state.exists) {
  *        val existingState = state.get  // Get the existing state
@@ -52,12 +52,12 @@ import org.apache.spark.annotation.Experimental
  *
  * Java example of using `State`:
  * {{{
- *    // A tracking function that maintains an integer state and return a String
- *   Function2<Optional<Integer>, State<Integer>, Optional<String>> trackStateFunc =
- *       new Function2<Optional<Integer>, State<Integer>, Optional<String>>() {
+ *    // A mapping function that maintains an integer state and returns a String
+ *    Function3<String, Optional<Integer>, State<Integer>, String> mappingFunction =
+ *       new Function3<String, Optional<Integer>, State<Integer>, String>() {
  *
  *         @Override
- *         public Optional<String> call(Optional<Integer> one, State<Integer> state) {
+ *         public String call(String key, Optional<Integer> value, State<Integer> state) {
  *           if (state.exists()) {
  *             int existingState = state.get(); // Get the existing state
  *             boolean shouldRemove = ...; // Decide whether to remove the state
@@ -75,6 +75,8 @@ import org.apache.spark.annotation.Experimental
  *         }
  *       };
  * }}}
+ *
+ * @tparam S Class of the state
  */
 @Experimental
 sealed abstract class State[S] {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StateSpec.scala b/streaming/src/main/scala/org/apache/spark/streaming/StateSpec.scala
index bea5b9df20..9f6f95223f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StateSpec.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StateSpec.scala
@@ -20,7 +20,7 @@ package org.apache.spark.streaming
 import com.google.common.base.Optional
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.{JavaPairRDD, JavaUtils}
-import org.apache.spark.api.java.function.{Function2 => JFunction2, Function4 => JFunction4}
+import org.apache.spark.api.java.function.{Function3 => JFunction3, Function4 => JFunction4}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.ClosureCleaner
 import org.apache.spark.{HashPartitioner, Partitioner}
@@ -28,7 +28,7 @@ import org.apache.spark.{HashPartitioner, Partitioner}
 /**
  * :: Experimental ::
  * Abstract class representing all the specifications of the DStream transformation
- * `trackStateByKey` operation of a
+ * `mapWithState` operation of a
  * [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair DStream]] (Scala) or a
  * [[org.apache.spark.streaming.api.java.JavaPairDStream JavaPairDStream]] (Java).
  * Use the [[org.apache.spark.streaming.StateSpec StateSpec.apply()]] or
@@ -37,50 +37,63 @@ import org.apache.spark.{HashPartitioner, Partitioner}
  *
  * Example in Scala:
  * {{{
- *    def trackingFunction(data: Option[ValueType], wrappedState: State[StateType]): EmittedType = {
- *      ...
+ *    // A mapping function that maintains an integer state and return a String
+ *    def mappingFunction(key: String, value: Option[Int], state: State[Int]): Option[String] = {
+ *      // Use state.exists(), state.get(), state.update() and state.remove()
+ *      // to manage state, and return the necessary string
  *    }
  *
- *    val spec = StateSpec.function(trackingFunction).numPartitions(10)
+ *    val spec = StateSpec.function(mappingFunction).numPartitions(10)
  *
- *    val emittedRecordDStream = keyValueDStream.trackStateByKey[StateType, EmittedDataType](spec)
+ *    val mapWithStateDStream = keyValueDStream.mapWithState[StateType, MappedType](spec)
  * }}}
  *
  * Example in Java:
  * {{{
- *    StateSpec<KeyType, ValueType, StateType, EmittedDataType> spec =
- *      StateSpec.<KeyType, ValueType, StateType, EmittedDataType>function(trackingFunction)
- *                    .numPartition(10);
+ *   // A mapping function that maintains an integer state and return a string
+ *   Function3<String, Optional<Integer>, State<Integer>, String> mappingFunction =
+ *       new Function3<String, Optional<Integer>, State<Integer>, String>() {
+ *           @Override
+ *           public Optional<String> call(Optional<Integer> value, State<Integer> state) {
+ *               // Use state.exists(), state.get(), state.update() and state.remove()
+ *               // to manage state, and return the necessary string
+ *           }
+ *       };
  *
- *    JavaTrackStateDStream<KeyType, ValueType, StateType, EmittedType> emittedRecordDStream =
- *      javaPairDStream.<StateType, EmittedDataType>trackStateByKey(spec);
+ *    JavaMapWithStateDStream<String, Integer, Integer, String> mapWithStateDStream =
+ *        keyValueDStream.mapWithState(StateSpec.function(mappingFunc));
  * }}}
+ *
+ * @tparam KeyType    Class of the state key
+ * @tparam ValueType  Class of the state value
+ * @tparam StateType  Class of the state data
+ * @tparam MappedType Class of the mapped elements
  */
 @Experimental
-sealed abstract class StateSpec[KeyType, ValueType, StateType, EmittedType] extends Serializable {
+sealed abstract class StateSpec[KeyType, ValueType, StateType, MappedType] extends Serializable {
 
-  /** Set the RDD containing the initial states that will be used by `trackStateByKey` */
+  /** Set the RDD containing the initial states that will be used by `mapWithState` */
   def initialState(rdd: RDD[(KeyType, StateType)]): this.type
 
-  /** Set the RDD containing the initial states that will be used by `trackStateByKey` */
+  /** Set the RDD containing the initial states that will be used by `mapWithState` */
   def initialState(javaPairRDD: JavaPairRDD[KeyType, StateType]): this.type
 
   /**
-   * Set the number of partitions by which the state RDDs generated by `trackStateByKey`
+   * Set the number of partitions by which the state RDDs generated by `mapWithState`
    * will be partitioned. Hash partitioning will be used.
    */
   def numPartitions(numPartitions: Int): this.type
 
   /**
-   * Set the partitioner by which the state RDDs generated by `trackStateByKey` will be
+   * Set the partitioner by which the state RDDs generated by `mapWithState` will be
    * be partitioned.
    */
   def partitioner(partitioner: Partitioner): this.type
 
   /**
    * Set the duration after which the state of an idle key will be removed. A key and its state is
-   * considered idle if it has not received any data for at least the given duration. The state
-   * tracking function will be called one final time on the idle states that are going to be
+   * considered idle if it has not received any data for at least the given duration. The
+   * mapping function will be called one final time on the idle states that are going to be
    * removed; [[org.apache.spark.streaming.State State.isTimingOut()]] set
    * to `true` in that call.
    */
@@ -91,115 +104,124 @@ sealed abstract class StateSpec[KeyType, ValueType, StateType, EmittedType] exte
 /**
  * :: Experimental ::
  * Builder object for creating instances of [[org.apache.spark.streaming.StateSpec StateSpec]]
- * that is used for specifying the parameters of the DStream transformation `trackStateByKey`
+ * that is used for specifying the parameters of the DStream transformation `mapWithState`
  * that is used for specifying the parameters of the DStream transformation
- * `trackStateByKey` operation of a
+ * `mapWithState` operation of a
  * [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair DStream]] (Scala) or a
  * [[org.apache.spark.streaming.api.java.JavaPairDStream JavaPairDStream]] (Java).
  *
  * Example in Scala:
  * {{{
- *    def trackingFunction(data: Option[ValueType], wrappedState: State[StateType]): EmittedType = {
- *      ...
+ *    // A mapping function that maintains an integer state and return a String
+ *    def mappingFunction(key: String, value: Option[Int], state: State[Int]): Option[String] = {
+ *      // Use state.exists(), state.get(), state.update() and state.remove()
+ *      // to manage state, and return the necessary string
  *    }
  *
- *    val emittedRecordDStream = keyValueDStream.trackStateByKey[StateType, EmittedDataType](
- *        StateSpec.function(trackingFunction).numPartitions(10))
+ *    val spec = StateSpec.function(mappingFunction).numPartitions(10)
+ *
+ *    val mapWithStateDStream = keyValueDStream.mapWithState[StateType, MappedType](spec)
  * }}}
  *
  * Example in Java:
  * {{{
- *    StateSpec<KeyType, ValueType, StateType, EmittedDataType> spec =
- *      StateSpec.<KeyType, ValueType, StateType, EmittedDataType>function(trackingFunction)
- *                    .numPartition(10);
+ *   // A mapping function that maintains an integer state and return a string
+ *   Function3<String, Optional<Integer>, State<Integer>, String> mappingFunction =
+ *       new Function3<String, Optional<Integer>, State<Integer>, String>() {
+ *           @Override
+ *           public Optional<String> call(Optional<Integer> value, State<Integer> state) {
+ *               // Use state.exists(), state.get(), state.update() and state.remove()
+ *               // to manage state, and return the necessary string
+ *           }
+ *       };
  *
- *    JavaTrackStateDStream<KeyType, ValueType, StateType, EmittedType> emittedRecordDStream =
- *      javaPairDStream.<StateType, EmittedDataType>trackStateByKey(spec);
- * }}}
+ *    JavaMapWithStateDStream<String, Integer, Integer, String> mapWithStateDStream =
+ *        keyValueDStream.mapWithState(StateSpec.function(mappingFunc));
+ *}}}
  */
 @Experimental
 object StateSpec {
   /**
    * Create a [[org.apache.spark.streaming.StateSpec StateSpec]] for setting all the specifications
-   * of the `trackStateByKey` operation on a
+   * of the `mapWithState` operation on a
    * [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair DStream]].
    *
-   * @param trackingFunction The function applied on every data item to manage the associated state
-   *                         and generate the emitted data
+   * @param mappingFunction The function applied on every data item to manage the associated state
+   *                         and generate the mapped data
    * @tparam KeyType      Class of the keys
    * @tparam ValueType    Class of the values
    * @tparam StateType    Class of the states data
-   * @tparam EmittedType  Class of the emitted data
+   * @tparam MappedType   Class of the mapped data
    */
-  def function[KeyType, ValueType, StateType, EmittedType](
-      trackingFunction: (Time, KeyType, Option[ValueType], State[StateType]) => Option[EmittedType]
-    ): StateSpec[KeyType, ValueType, StateType, EmittedType] = {
-    ClosureCleaner.clean(trackingFunction, checkSerializable = true)
-    new StateSpecImpl(trackingFunction)
+  def function[KeyType, ValueType, StateType, MappedType](
+      mappingFunction: (Time, KeyType, Option[ValueType], State[StateType]) => Option[MappedType]
+    ): StateSpec[KeyType, ValueType, StateType, MappedType] = {
+    ClosureCleaner.clean(mappingFunction, checkSerializable = true)
+    new StateSpecImpl(mappingFunction)
   }
 
   /**
    * Create a [[org.apache.spark.streaming.StateSpec StateSpec]] for setting all the specifications
-   * of the `trackStateByKey` operation on a
+   * of the `mapWithState` operation on a
    * [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair DStream]].
    *
-   * @param trackingFunction The function applied on every data item to manage the associated state
-   *                         and generate the emitted data
+   * @param mappingFunction The function applied on every data item to manage the associated state
+   *                         and generate the mapped data
    * @tparam ValueType    Class of the values
    * @tparam StateType    Class of the states data
-   * @tparam EmittedType  Class of the emitted data
+   * @tparam MappedType   Class of the mapped data
    */
-  def function[KeyType, ValueType, StateType, EmittedType](
-      trackingFunction: (Option[ValueType], State[StateType]) => EmittedType
-    ): StateSpec[KeyType, ValueType, StateType, EmittedType] = {
-    ClosureCleaner.clean(trackingFunction, checkSerializable = true)
+  def function[KeyType, ValueType, StateType, MappedType](
+      mappingFunction: (KeyType, Option[ValueType], State[StateType]) => MappedType
+    ): StateSpec[KeyType, ValueType, StateType, MappedType] = {
+    ClosureCleaner.clean(mappingFunction, checkSerializable = true)
     val wrappedFunction =
-      (time: Time, key: Any, value: Option[ValueType], state: State[StateType]) => {
-        Some(trackingFunction(value, state))
+      (time: Time, key: KeyType, value: Option[ValueType], state: State[StateType]) => {
+        Some(mappingFunction(key, value, state))
       }
     new StateSpecImpl(wrappedFunction)
   }
 
   /**
    * Create a [[org.apache.spark.streaming.StateSpec StateSpec]] for setting all
-   * the specifications of the `trackStateByKey` operation on a
+   * the specifications of the `mapWithState` operation on a
    * [[org.apache.spark.streaming.api.java.JavaPairDStream JavaPairDStream]].
    *
-   * @param javaTrackingFunction The function applied on every data item to manage the associated
-   *                             state and generate the emitted data
+   * @param mappingFunction The function applied on every data item to manage the associated
+   *                        state and generate the mapped data
    * @tparam KeyType      Class of the keys
    * @tparam ValueType    Class of the values
    * @tparam StateType    Class of the states data
-   * @tparam EmittedType  Class of the emitted data
+   * @tparam MappedType   Class of the mapped data
    */
-  def function[KeyType, ValueType, StateType, EmittedType](javaTrackingFunction:
-      JFunction4[Time, KeyType, Optional[ValueType], State[StateType], Optional[EmittedType]]):
-    StateSpec[KeyType, ValueType, StateType, EmittedType] = {
-    val trackingFunc = (time: Time, k: KeyType, v: Option[ValueType], s: State[StateType]) => {
-      val t = javaTrackingFunction.call(time, k, JavaUtils.optionToOptional(v), s)
+  def function[KeyType, ValueType, StateType, MappedType](mappingFunction:
+      JFunction4[Time, KeyType, Optional[ValueType], State[StateType], Optional[MappedType]]):
+    StateSpec[KeyType, ValueType, StateType, MappedType] = {
+    val wrappedFunc = (time: Time, k: KeyType, v: Option[ValueType], s: State[StateType]) => {
+      val t = mappingFunction.call(time, k, JavaUtils.optionToOptional(v), s)
       Option(t.orNull)
     }
-    StateSpec.function(trackingFunc)
+    StateSpec.function(wrappedFunc)
   }
 
   /**
    * Create a [[org.apache.spark.streaming.StateSpec StateSpec]] for setting all the specifications
-   * of the `trackStateByKey` operation on a
+   * of the `mapWithState` operation on a
    * [[org.apache.spark.streaming.api.java.JavaPairDStream JavaPairDStream]].
    *
-   * @param javaTrackingFunction The function applied on every data item to manage the associated
-   *                             state and generate the emitted data
+   * @param mappingFunction The function applied on every data item to manage the associated
+   *                        state and generate the mapped data
    * @tparam ValueType    Class of the values
    * @tparam StateType    Class of the states data
-   * @tparam EmittedType  Class of the emitted data
+   * @tparam MappedType   Class of the mapped data
    */
-  def function[KeyType, ValueType, StateType, EmittedType](
-      javaTrackingFunction: JFunction2[Optional[ValueType], State[StateType], EmittedType]):
-    StateSpec[KeyType, ValueType, StateType, EmittedType] = {
-    val trackingFunc = (v: Option[ValueType], s: State[StateType]) => {
-      javaTrackingFunction.call(Optional.fromNullable(v.get), s)
+  def function[KeyType, ValueType, StateType, MappedType](
+      mappingFunction: JFunction3[KeyType, Optional[ValueType], State[StateType], MappedType]):
+    StateSpec[KeyType, ValueType, StateType, MappedType] = {
+    val wrappedFunc = (k: KeyType, v: Option[ValueType], s: State[StateType]) => {
+      mappingFunction.call(k, Optional.fromNullable(v.get), s)
     }
-    StateSpec.function(trackingFunc)
+    StateSpec.function(wrappedFunc)
   }
 }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaTrackStateDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaMapWithStateDStream.scala
index f459930d06..16c0d6fff8 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaTrackStateDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaMapWithStateDStream.scala
@@ -19,23 +19,23 @@ package org.apache.spark.streaming.api.java
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaSparkContext
-import org.apache.spark.streaming.dstream.TrackStateDStream
+import org.apache.spark.streaming.dstream.MapWithStateDStream
 
 /**
  * :: Experimental ::
- * [[JavaDStream]] representing the stream of records emitted by the tracking function in the
- * `trackStateByKey` operation on a [[JavaPairDStream]]. Additionally, it also gives access to the
+ * DStream representing the stream of data generated by `mapWithState` operation on a
+ * [[JavaPairDStream]]. Additionally, it also gives access to the
  * stream of state snapshots, that is, the state data of all keys after a batch has updated them.
  *
- * @tparam KeyType Class of the state key
- * @tparam ValueType Class of the state value
- * @tparam StateType Class of the state
- * @tparam EmittedType Class of the emitted records
+ * @tparam KeyType Class of the keys
+ * @tparam ValueType Class of the values
+ * @tparam StateType Class of the state data
+ * @tparam MappedType Class of the mapped data
  */
 @Experimental
-class JavaTrackStateDStream[KeyType, ValueType, StateType, EmittedType](
-    dstream: TrackStateDStream[KeyType, ValueType, StateType, EmittedType])
-  extends JavaDStream[EmittedType](dstream)(JavaSparkContext.fakeClassTag) {
+class JavaMapWithStateDStream[KeyType, ValueType, StateType, MappedType] private[streaming](
+    dstream: MapWithStateDStream[KeyType, ValueType, StateType, MappedType])
+  extends JavaDStream[MappedType](dstream)(JavaSparkContext.fakeClassTag) {
 
   def stateSnapshots(): JavaPairDStream[KeyType, StateType] =
     new JavaPairDStream(dstream.stateSnapshots())(
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
index 70e32b383e..42ddd63f0f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
@@ -430,42 +430,36 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
 
   /**
    * :: Experimental ::
-   * Return a new [[JavaDStream]] of data generated by combining the key-value data in `this` stream
-   * with a continuously updated per-key state. The user-provided state tracking function is
-   * applied on each keyed data item along with its corresponding state. The function can choose to
-   * update/remove the state and return a transformed data, which forms the
-   * [[JavaTrackStateDStream]].
+   * Return a [[JavaMapWithStateDStream]] by applying a function to every key-value element of
+   * `this` stream, while maintaining some state data for each unique key. The mapping function
+   * and other specification (e.g. partitioners, timeouts, initial state data, etc.) of this
+   * transformation can be specified using [[StateSpec]] class. The state data is accessible in
+   * as a parameter of type [[State]] in the mapping function.
    *
-   * The specifications of this transformation is made through the
-   * [[org.apache.spark.streaming.StateSpec StateSpec]] class. Besides the tracking function, there
-   * are a number of optional parameters - initial state data, number of partitions, timeouts, etc.
-   * See the [[org.apache.spark.streaming.StateSpec StateSpec]] for more details.
-   *
-   * Example of using `trackStateByKey`:
+   * Example of using `mapWithState`:
    * {{{
-   *   // A tracking function that maintains an integer state and return a String
-   *   Function2<Optional<Integer>, State<Integer>, Optional<String>> trackStateFunc =
-   *       new Function2<Optional<Integer>, State<Integer>, Optional<String>>() {
-   *
-   *         @Override
-   *         public Optional<String> call(Optional<Integer> one, State<Integer> state) {
-   *           // Check if state exists, accordingly update/remove state and return transformed data
-   *         }
+   *   // A mapping function that maintains an integer state and return a string
+   *   Function3<String, Optional<Integer>, State<Integer>, String> mappingFunction =
+   *       new Function3<String, Optional<Integer>, State<Integer>, String>() {
+   *           @Override
+   *           public Optional<String> call(Optional<Integer> value, State<Integer> state) {
+   *               // Use state.exists(), state.get(), state.update() and state.remove()
+   *               // to manage state, and return the necessary string
+   *           }
    *       };
    *
-   *    JavaTrackStateDStream<Integer, Integer, Integer, String> trackStateDStream =
-   *        keyValueDStream.<Integer, String>trackStateByKey(
-   *                 StateSpec.function(trackStateFunc).numPartitions(10));
-   * }}}
+   *    JavaMapWithStateDStream<String, Integer, Integer, String> mapWithStateDStream =
+   *        keyValueDStream.mapWithState(StateSpec.function(mappingFunc));
+   *}}}
    *
    * @param spec          Specification of this transformation
-   * @tparam StateType    Class type of the state
-   * @tparam EmittedType  Class type of the tranformed data return by the tracking function
+   * @tparam StateType    Class type of the state data
+   * @tparam MappedType   Class type of the mapped data
    */
   @Experimental
-  def trackStateByKey[StateType, EmittedType](spec: StateSpec[K, V, StateType, EmittedType]):
-    JavaTrackStateDStream[K, V, StateType, EmittedType] = {
-    new JavaTrackStateDStream(dstream.trackStateByKey(spec)(
+  def mapWithState[StateType, MappedType](spec: StateSpec[K, V, StateType, MappedType]):
+    JavaMapWithStateDStream[K, V, StateType, MappedType] = {
+    new JavaMapWithStateDStream(dstream.mapWithState(spec)(
       JavaSparkContext.fakeClassTag,
       JavaSparkContext.fakeClassTag))
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TrackStateDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapWithStateDStream.scala
index ea6213420e..706465d4e2 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TrackStateDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapWithStateDStream.scala
@@ -24,53 +24,52 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.rdd.{EmptyRDD, RDD}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
-import org.apache.spark.streaming.rdd.{TrackStateRDD, TrackStateRDDRecord}
-import org.apache.spark.streaming.dstream.InternalTrackStateDStream._
+import org.apache.spark.streaming.rdd.{MapWithStateRDD, MapWithStateRDDRecord}
+import org.apache.spark.streaming.dstream.InternalMapWithStateDStream._
 
 /**
  * :: Experimental ::
- * DStream representing the stream of records emitted by the tracking function in the
- * `trackStateByKey` operation on a
+ * DStream representing the stream of data generated by `mapWithState` operation on a
  * [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair DStream]].
  * Additionally, it also gives access to the stream of state snapshots, that is, the state data of
  * all keys after a batch has updated them.
  *
- * @tparam KeyType Class of the state key
- * @tparam ValueType Class of the state value
+ * @tparam KeyType Class of the key
+ * @tparam ValueType Class of the value
  * @tparam StateType Class of the state data
- * @tparam EmittedType Class of the emitted records
+ * @tparam MappedType Class of the mapped data
  */
 @Experimental
-sealed abstract class TrackStateDStream[KeyType, ValueType, StateType, EmittedType: ClassTag](
-    ssc: StreamingContext) extends DStream[EmittedType](ssc) {
+sealed abstract class MapWithStateDStream[KeyType, ValueType, StateType, MappedType: ClassTag](
+    ssc: StreamingContext) extends DStream[MappedType](ssc) {
 
   /** Return a pair DStream where each RDD is the snapshot of the state of all the keys. */
   def stateSnapshots(): DStream[(KeyType, StateType)]
 }
 
-/** Internal implementation of the [[TrackStateDStream]] */
-private[streaming] class TrackStateDStreamImpl[
-    KeyType: ClassTag, ValueType: ClassTag, StateType: ClassTag, EmittedType: ClassTag](
+/** Internal implementation of the [[MapWithStateDStream]] */
+private[streaming] class MapWithStateDStreamImpl[
+    KeyType: ClassTag, ValueType: ClassTag, StateType: ClassTag, MappedType: ClassTag](
     dataStream: DStream[(KeyType, ValueType)],
-    spec: StateSpecImpl[KeyType, ValueType, StateType, EmittedType])
-  extends TrackStateDStream[KeyType, ValueType, StateType, EmittedType](dataStream.context) {
+    spec: StateSpecImpl[KeyType, ValueType, StateType, MappedType])
+  extends MapWithStateDStream[KeyType, ValueType, StateType, MappedType](dataStream.context) {
 
   private val internalStream =
-    new InternalTrackStateDStream[KeyType, ValueType, StateType, EmittedType](dataStream, spec)
+    new InternalMapWithStateDStream[KeyType, ValueType, StateType, MappedType](dataStream, spec)
 
   override def slideDuration: Duration = internalStream.slideDuration
 
   override def dependencies: List[DStream[_]] = List(internalStream)
 
-  override def compute(validTime: Time): Option[RDD[EmittedType]] = {
-    internalStream.getOrCompute(validTime).map { _.flatMap[EmittedType] { _.emittedRecords } }
+  override def compute(validTime: Time): Option[RDD[MappedType]] = {
+    internalStream.getOrCompute(validTime).map { _.flatMap[MappedType] { _.mappedData } }
   }
 
   /**
    * Forward the checkpoint interval to the internal DStream that computes the state maps. This
    * to make sure that this DStream does not get checkpointed, only the internal stream.
    */
-  override def checkpoint(checkpointInterval: Duration): DStream[EmittedType] = {
+  override def checkpoint(checkpointInterval: Duration): DStream[MappedType] = {
     internalStream.checkpoint(checkpointInterval)
     this
   }
@@ -87,32 +86,32 @@ private[streaming] class TrackStateDStreamImpl[
 
   def stateClass: Class[_] = implicitly[ClassTag[StateType]].runtimeClass
 
-  def emittedClass: Class[_] = implicitly[ClassTag[EmittedType]].runtimeClass
+  def mappedClass: Class[_] = implicitly[ClassTag[MappedType]].runtimeClass
 }
 
 /**
  * A DStream that allows per-key state to be maintains, and arbitrary records to be generated
- * based on updates to the state. This is the main DStream that implements the `trackStateByKey`
+ * based on updates to the state. This is the main DStream that implements the `mapWithState`
  * operation on DStreams.
  *
  * @param parent Parent (key, value) stream that is the source
- * @param spec Specifications of the trackStateByKey operation
+ * @param spec Specifications of the mapWithState operation
  * @tparam K   Key type
  * @tparam V   Value type
  * @tparam S   Type of the state maintained
- * @tparam E   Type of the emitted data
+ * @tparam E   Type of the mapped data
  */
 private[streaming]
-class InternalTrackStateDStream[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
+class InternalMapWithStateDStream[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
     parent: DStream[(K, V)], spec: StateSpecImpl[K, V, S, E])
-  extends DStream[TrackStateRDDRecord[K, S, E]](parent.context) {
+  extends DStream[MapWithStateRDDRecord[K, S, E]](parent.context) {
 
   persist(StorageLevel.MEMORY_ONLY)
 
   private val partitioner = spec.getPartitioner().getOrElse(
     new HashPartitioner(ssc.sc.defaultParallelism))
 
-  private val trackingFunction = spec.getFunction()
+  private val mappingFunction = spec.getFunction()
 
   override def slideDuration: Duration = parent.slideDuration
 
@@ -130,7 +129,7 @@ class InternalTrackStateDStream[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassT
   }
 
   /** Method that generates a RDD for the given time */
-  override def compute(validTime: Time): Option[RDD[TrackStateRDDRecord[K, S, E]]] = {
+  override def compute(validTime: Time): Option[RDD[MapWithStateRDDRecord[K, S, E]]] = {
     // Get the previous state or create a new empty state RDD
     val prevStateRDD = getOrCompute(validTime - slideDuration) match {
       case Some(rdd) =>
@@ -138,13 +137,13 @@ class InternalTrackStateDStream[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassT
           // If the RDD is not partitioned the right way, let us repartition it using the
           // partition index as the key. This is to ensure that state RDD is always partitioned
           // before creating another state RDD using it
-          TrackStateRDD.createFromRDD[K, V, S, E](
+          MapWithStateRDD.createFromRDD[K, V, S, E](
             rdd.flatMap { _.stateMap.getAll() }, partitioner, validTime)
         } else {
           rdd
         }
       case None =>
-        TrackStateRDD.createFromPairRDD[K, V, S, E](
+        MapWithStateRDD.createFromPairRDD[K, V, S, E](
           spec.getInitialStateRDD().getOrElse(new EmptyRDD[(K, S)](ssc.sparkContext)),
           partitioner,
           validTime
@@ -161,11 +160,11 @@ class InternalTrackStateDStream[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassT
     val timeoutThresholdTime = spec.getTimeoutInterval().map { interval =>
       (validTime - interval).milliseconds
     }
-    Some(new TrackStateRDD(
-      prevStateRDD, partitionedDataRDD, trackingFunction, validTime, timeoutThresholdTime))
+    Some(new MapWithStateRDD(
+      prevStateRDD, partitionedDataRDD, mappingFunction, validTime, timeoutThresholdTime))
   }
 }
 
-private[streaming] object InternalTrackStateDStream {
+private[streaming] object InternalMapWithStateDStream {
   private val DEFAULT_CHECKPOINT_DURATION_MULTIPLIER = 10
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala
index 2762309134..a64a1fe93f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala
@@ -352,39 +352,36 @@ class PairDStreamFunctions[K, V](self: DStream[(K, V)])
 
   /**
    * :: Experimental ::
-   * Return a new DStream of data generated by combining the key-value data in `this` stream
-   * with a continuously updated per-key state. The user-provided state tracking function is
-   * applied on each keyed data item along with its corresponding state. The function can choose to
-   * update/remove the state and return a transformed data, which forms the
-   * [[org.apache.spark.streaming.dstream.TrackStateDStream]].
+   * Return a [[MapWithStateDStream]] by applying a function to every key-value element of
+   * `this` stream, while maintaining some state data for each unique key. The mapping function
+   * and other specification (e.g. partitioners, timeouts, initial state data, etc.) of this
+   * transformation can be specified using [[StateSpec]] class. The state data is accessible in
+   * as a parameter of type [[State]] in the mapping function.
    *
-   * The specifications of this transformation is made through the
-   * [[org.apache.spark.streaming.StateSpec StateSpec]] class. Besides the tracking function, there
-   * are a number of optional parameters - initial state data, number of partitions, timeouts, etc.
-   * See the [[org.apache.spark.streaming.StateSpec StateSpec spec docs]] for more details.
-   *
-   * Example of using `trackStateByKey`:
+   * Example of using `mapWithState`:
    * {{{
-   *    def trackingFunction(data: Option[Int], wrappedState: State[Int]): String = {
-   *      // Check if state exists, accordingly update/remove state and return transformed data
+   *    // A mapping function that maintains an integer state and return a String
+   *    def mappingFunction(key: String, value: Option[Int], state: State[Int]): Option[String] = {
+   *      // Use state.exists(), state.get(), state.update() and state.remove()
+   *      // to manage state, and return the necessary string
    *    }
    *
-   *    val spec = StateSpec.function(trackingFunction).numPartitions(10)
+   *    val spec = StateSpec.function(mappingFunction).numPartitions(10)
    *
-   *    val trackStateDStream = keyValueDStream.trackStateByKey[Int, String](spec)
+   *    val mapWithStateDStream = keyValueDStream.mapWithState[StateType, MappedType](spec)
    * }}}
    *
    * @param spec          Specification of this transformation
-   * @tparam StateType    Class type of the state
-   * @tparam EmittedType  Class type of the tranformed data return by the tracking function
+   * @tparam StateType    Class type of the state data
+   * @tparam MappedType   Class type of the mapped data
    */
   @Experimental
-  def trackStateByKey[StateType: ClassTag, EmittedType: ClassTag](
-      spec: StateSpec[K, V, StateType, EmittedType]
-    ): TrackStateDStream[K, V, StateType, EmittedType] = {
-    new TrackStateDStreamImpl[K, V, StateType, EmittedType](
+  def mapWithState[StateType: ClassTag, MappedType: ClassTag](
+      spec: StateSpec[K, V, StateType, MappedType]
+    ): MapWithStateDStream[K, V, StateType, MappedType] = {
+    new MapWithStateDStreamImpl[K, V, StateType, MappedType](
       self,
-      spec.asInstanceOf[StateSpecImpl[K, V, StateType, EmittedType]]
+      spec.asInstanceOf[StateSpecImpl[K, V, StateType, MappedType]]
     )
   }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/TrackStateRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/MapWithStateRDD.scala
index 30aafcf146..ed95171f73 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/TrackStateRDD.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/MapWithStateRDD.scala
@@ -29,60 +29,60 @@ import org.apache.spark.util.Utils
 import org.apache.spark._
 
 /**
- * Record storing the keyed-state [[TrackStateRDD]]. Each record contains a [[StateMap]] and a
- * sequence of records returned by the tracking function of `trackStateByKey`.
+ * Record storing the keyed-state [[MapWithStateRDD]]. Each record contains a [[StateMap]] and a
+ * sequence of records returned by the mapping function of `mapWithState`.
  */
-private[streaming] case class TrackStateRDDRecord[K, S, E](
-    var stateMap: StateMap[K, S], var emittedRecords: Seq[E])
+private[streaming] case class MapWithStateRDDRecord[K, S, E](
+    var stateMap: StateMap[K, S], var mappedData: Seq[E])
 
-private[streaming] object TrackStateRDDRecord {
+private[streaming] object MapWithStateRDDRecord {
   def updateRecordWithData[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
-    prevRecord: Option[TrackStateRDDRecord[K, S, E]],
+    prevRecord: Option[MapWithStateRDDRecord[K, S, E]],
     dataIterator: Iterator[(K, V)],
-    updateFunction: (Time, K, Option[V], State[S]) => Option[E],
+    mappingFunction: (Time, K, Option[V], State[S]) => Option[E],
     batchTime: Time,
     timeoutThresholdTime: Option[Long],
     removeTimedoutData: Boolean
-  ): TrackStateRDDRecord[K, S, E] = {
+  ): MapWithStateRDDRecord[K, S, E] = {
     // Create a new state map by cloning the previous one (if it exists) or by creating an empty one
     val newStateMap = prevRecord.map { _.stateMap.copy() }. getOrElse { new EmptyStateMap[K, S]() }
 
-    val emittedRecords = new ArrayBuffer[E]
+    val mappedData = new ArrayBuffer[E]
     val wrappedState = new StateImpl[S]()
 
-    // Call the tracking function on each record in the data iterator, and accordingly
-    // update the states touched, and collect the data returned by the tracking function
+    // Call the mapping function on each record in the data iterator, and accordingly
+    // update the states touched, and collect the data returned by the mapping function
     dataIterator.foreach { case (key, value) =>
       wrappedState.wrap(newStateMap.get(key))
-      val emittedRecord = updateFunction(batchTime, key, Some(value), wrappedState)
+      val returned = mappingFunction(batchTime, key, Some(value), wrappedState)
       if (wrappedState.isRemoved) {
         newStateMap.remove(key)
       } else if (wrappedState.isUpdated || timeoutThresholdTime.isDefined) {
         newStateMap.put(key, wrappedState.get(), batchTime.milliseconds)
       }
-      emittedRecords ++= emittedRecord
+      mappedData ++= returned
     }
 
-    // Get the timed out state records, call the tracking function on each and collect the
+    // Get the timed out state records, call the mapping function on each and collect the
     // data returned
     if (removeTimedoutData && timeoutThresholdTime.isDefined) {
       newStateMap.getByTime(timeoutThresholdTime.get).foreach { case (key, state, _) =>
         wrappedState.wrapTiminoutState(state)
-        val emittedRecord = updateFunction(batchTime, key, None, wrappedState)
-        emittedRecords ++= emittedRecord
+        val returned = mappingFunction(batchTime, key, None, wrappedState)
+        mappedData ++= returned
         newStateMap.remove(key)
       }
     }
 
-    TrackStateRDDRecord(newStateMap, emittedRecords)
+    MapWithStateRDDRecord(newStateMap, mappedData)
   }
 }
 
 /**
- * Partition of the [[TrackStateRDD]], which depends on corresponding partitions of prev state
+ * Partition of the [[MapWithStateRDD]], which depends on corresponding partitions of prev state
  * RDD, and a partitioned keyed-data RDD
  */
-private[streaming] class TrackStateRDDPartition(
+private[streaming] class MapWithStateRDDPartition(
     idx: Int,
     @transient private var prevStateRDD: RDD[_],
     @transient private var partitionedDataRDD: RDD[_]) extends Partition {
@@ -104,27 +104,28 @@ private[streaming] class TrackStateRDDPartition(
 
 
 /**
- * RDD storing the keyed-state of `trackStateByKey` and corresponding emitted records.
- * Each partition of this RDD has a single record of type [[TrackStateRDDRecord]]. This contains a
- * [[StateMap]] (containing the keyed-states) and the sequence of records returned by the tracking
- * function of  `trackStateByKey`.
- * @param prevStateRDD The previous TrackStateRDD on whose StateMap data `this` RDD will be created
+ * RDD storing the keyed states of `mapWithState` operation and corresponding mapped data.
+ * Each partition of this RDD has a single record of type [[MapWithStateRDDRecord]]. This contains a
+ * [[StateMap]] (containing the keyed-states) and the sequence of records returned by the mapping
+ * function of  `mapWithState`.
+ * @param prevStateRDD The previous MapWithStateRDD on whose StateMap data `this` RDD
+  *                    will be created
  * @param partitionedDataRDD The partitioned data RDD which is used update the previous StateMaps
  *                           in the `prevStateRDD` to create `this` RDD
- * @param trackingFunction The function that will be used to update state and return new data
+ * @param mappingFunction  The function that will be used to update state and return new data
  * @param batchTime        The time of the batch to which this RDD belongs to. Use to update
  * @param timeoutThresholdTime The time to indicate which keys are timeout
  */
-private[streaming] class TrackStateRDD[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
-    private var prevStateRDD: RDD[TrackStateRDDRecord[K, S, E]],
+private[streaming] class MapWithStateRDD[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
+    private var prevStateRDD: RDD[MapWithStateRDDRecord[K, S, E]],
     private var partitionedDataRDD: RDD[(K, V)],
-    trackingFunction: (Time, K, Option[V], State[S]) => Option[E],
+    mappingFunction: (Time, K, Option[V], State[S]) => Option[E],
     batchTime: Time,
     timeoutThresholdTime: Option[Long]
-  ) extends RDD[TrackStateRDDRecord[K, S, E]](
+  ) extends RDD[MapWithStateRDDRecord[K, S, E]](
     partitionedDataRDD.sparkContext,
     List(
-      new OneToOneDependency[TrackStateRDDRecord[K, S, E]](prevStateRDD),
+      new OneToOneDependency[MapWithStateRDDRecord[K, S, E]](prevStateRDD),
       new OneToOneDependency(partitionedDataRDD))
   ) {
 
@@ -141,19 +142,19 @@ private[streaming] class TrackStateRDD[K: ClassTag, V: ClassTag, S: ClassTag, E:
   }
 
   override def compute(
-      partition: Partition, context: TaskContext): Iterator[TrackStateRDDRecord[K, S, E]] = {
+      partition: Partition, context: TaskContext): Iterator[MapWithStateRDDRecord[K, S, E]] = {
 
-    val stateRDDPartition = partition.asInstanceOf[TrackStateRDDPartition]
+    val stateRDDPartition = partition.asInstanceOf[MapWithStateRDDPartition]
     val prevStateRDDIterator = prevStateRDD.iterator(
       stateRDDPartition.previousSessionRDDPartition, context)
     val dataIterator = partitionedDataRDD.iterator(
       stateRDDPartition.partitionedDataRDDPartition, context)
 
     val prevRecord = if (prevStateRDDIterator.hasNext) Some(prevStateRDDIterator.next()) else None
-    val newRecord = TrackStateRDDRecord.updateRecordWithData(
+    val newRecord = MapWithStateRDDRecord.updateRecordWithData(
       prevRecord,
       dataIterator,
-      trackingFunction,
+      mappingFunction,
       batchTime,
       timeoutThresholdTime,
       removeTimedoutData = doFullScan // remove timedout data only when full scan is enabled
@@ -163,7 +164,7 @@ private[streaming] class TrackStateRDD[K: ClassTag, V: ClassTag, S: ClassTag, E:
 
   override protected def getPartitions: Array[Partition] = {
     Array.tabulate(prevStateRDD.partitions.length) { i =>
-      new TrackStateRDDPartition(i, prevStateRDD, partitionedDataRDD)}
+      new MapWithStateRDDPartition(i, prevStateRDD, partitionedDataRDD)}
   }
 
   override def clearDependencies(): Unit = {
@@ -177,52 +178,46 @@ private[streaming] class TrackStateRDD[K: ClassTag, V: ClassTag, S: ClassTag, E:
   }
 }
 
-private[streaming] object TrackStateRDD {
+private[streaming] object MapWithStateRDD {
 
   def createFromPairRDD[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
       pairRDD: RDD[(K, S)],
       partitioner: Partitioner,
-      updateTime: Time): TrackStateRDD[K, V, S, E] = {
+      updateTime: Time): MapWithStateRDD[K, V, S, E] = {
 
-    val rddOfTrackStateRecords = pairRDD.partitionBy(partitioner).mapPartitions ({ iterator =>
+    val stateRDD = pairRDD.partitionBy(partitioner).mapPartitions ({ iterator =>
       val stateMap = StateMap.create[K, S](SparkEnv.get.conf)
       iterator.foreach { case (key, state) => stateMap.put(key, state, updateTime.milliseconds) }
-      Iterator(TrackStateRDDRecord(stateMap, Seq.empty[E]))
+      Iterator(MapWithStateRDDRecord(stateMap, Seq.empty[E]))
     }, preservesPartitioning = true)
 
     val emptyDataRDD = pairRDD.sparkContext.emptyRDD[(K, V)].partitionBy(partitioner)
 
     val noOpFunc = (time: Time, key: K, value: Option[V], state: State[S]) => None
 
-    new TrackStateRDD[K, V, S, E](rddOfTrackStateRecords, emptyDataRDD, noOpFunc, updateTime, None)
+    new MapWithStateRDD[K, V, S, E](
+      stateRDD, emptyDataRDD, noOpFunc, updateTime, None)
   }
 
   def createFromRDD[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
       rdd: RDD[(K, S, Long)],
       partitioner: Partitioner,
-      updateTime: Time): TrackStateRDD[K, V, S, E] = {
+      updateTime: Time): MapWithStateRDD[K, V, S, E] = {
 
     val pairRDD = rdd.map { x => (x._1, (x._2, x._3)) }
-    val rddOfTrackStateRecords = pairRDD.partitionBy(partitioner).mapPartitions({ iterator =>
+    val stateRDD = pairRDD.partitionBy(partitioner).mapPartitions({ iterator =>
       val stateMap = StateMap.create[K, S](SparkEnv.get.conf)
       iterator.foreach { case (key, (state, updateTime)) =>
         stateMap.put(key, state, updateTime)
       }
-      Iterator(TrackStateRDDRecord(stateMap, Seq.empty[E]))
+      Iterator(MapWithStateRDDRecord(stateMap, Seq.empty[E]))
     }, preservesPartitioning = true)
 
     val emptyDataRDD = pairRDD.sparkContext.emptyRDD[(K, V)].partitionBy(partitioner)
 
     val noOpFunc = (time: Time, key: K, value: Option[V], state: State[S]) => None
 
-    new TrackStateRDD[K, V, S, E](rddOfTrackStateRecords, emptyDataRDD, noOpFunc, updateTime, None)
-  }
-}
-
-private[streaming] class EmittedRecordsRDD[K: ClassTag, V: ClassTag, S: ClassTag, T: ClassTag](
-    parent: TrackStateRDD[K, V, S, T]) extends RDD[T](parent) {
-  override protected def getPartitions: Array[Partition] = parent.partitions
-  override def compute(partition: Partition, context: TaskContext): Iterator[T] = {
-    parent.compute(partition, context).flatMap { _.emittedRecords }
+    new MapWithStateRDD[K, V, S, E](
+      stateRDD, emptyDataRDD, noOpFunc, updateTime, None)
   }
 }
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaTrackStateByKeySuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java
index 89d0bb7b61..bc4bc2eb42 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaTrackStateByKeySuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java
@@ -37,12 +37,12 @@ import org.junit.Test;
 
 import org.apache.spark.HashPartitioner;
 import org.apache.spark.api.java.JavaPairRDD;
-import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.api.java.function.Function3;
 import org.apache.spark.api.java.function.Function4;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
-import org.apache.spark.streaming.api.java.JavaTrackStateDStream;
+import org.apache.spark.streaming.api.java.JavaMapWithStateDStream;
 
-public class JavaTrackStateByKeySuite extends LocalJavaStreamingContext implements Serializable {
+public class JavaMapWithStateSuite extends LocalJavaStreamingContext implements Serializable {
 
   /**
    * This test is only for testing the APIs. It's not necessary to run it.
@@ -52,7 +52,7 @@ public class JavaTrackStateByKeySuite extends LocalJavaStreamingContext implemen
     JavaPairDStream<String, Integer> wordsDstream = null;
 
     final Function4<Time, String, Optional<Integer>, State<Boolean>, Optional<Double>>
-        trackStateFunc =
+        mappingFunc =
         new Function4<Time, String, Optional<Integer>, State<Boolean>, Optional<Double>>() {
 
           @Override
@@ -68,21 +68,21 @@ public class JavaTrackStateByKeySuite extends LocalJavaStreamingContext implemen
           }
         };
 
-    JavaTrackStateDStream<String, Integer, Boolean, Double> stateDstream =
-        wordsDstream.trackStateByKey(
-            StateSpec.function(trackStateFunc)
+    JavaMapWithStateDStream<String, Integer, Boolean, Double> stateDstream =
+        wordsDstream.mapWithState(
+            StateSpec.function(mappingFunc)
                 .initialState(initialRDD)
                 .numPartitions(10)
                 .partitioner(new HashPartitioner(10))
                 .timeout(Durations.seconds(10)));
 
-    JavaPairDStream<String, Boolean> emittedRecords = stateDstream.stateSnapshots();
+    JavaPairDStream<String, Boolean> stateSnapshots = stateDstream.stateSnapshots();
 
-    final Function2<Optional<Integer>, State<Boolean>, Double> trackStateFunc2 =
-        new Function2<Optional<Integer>, State<Boolean>, Double>() {
+    final Function3<String, Optional<Integer>, State<Boolean>, Double> mappingFunc2 =
+        new Function3<String, Optional<Integer>, State<Boolean>, Double>() {
 
           @Override
-          public Double call(Optional<Integer> one, State<Boolean> state) {
+          public Double call(String key, Optional<Integer> one, State<Boolean> state) {
             // Use all State's methods here
             state.exists();
             state.get();
@@ -93,15 +93,15 @@ public class JavaTrackStateByKeySuite extends LocalJavaStreamingContext implemen
           }
         };
 
-    JavaTrackStateDStream<String, Integer, Boolean, Double> stateDstream2 =
-        wordsDstream.trackStateByKey(
-            StateSpec.<String, Integer, Boolean, Double>function(trackStateFunc2)
+    JavaMapWithStateDStream<String, Integer, Boolean, Double> stateDstream2 =
+        wordsDstream.mapWithState(
+            StateSpec.<String, Integer, Boolean, Double>function(mappingFunc2)
                 .initialState(initialRDD)
                 .numPartitions(10)
                 .partitioner(new HashPartitioner(10))
                 .timeout(Durations.seconds(10)));
 
-    JavaPairDStream<String, Boolean> emittedRecords2 = stateDstream2.stateSnapshots();
+    JavaPairDStream<String, Boolean> stateSnapshots2 = stateDstream2.stateSnapshots();
   }
 
   @Test
@@ -148,11 +148,11 @@ public class JavaTrackStateByKeySuite extends LocalJavaStreamingContext implemen
             new Tuple2<String, Integer>("c", 1))
     );
 
-    Function2<Optional<Integer>, State<Integer>, Integer> trackStateFunc =
-        new Function2<Optional<Integer>, State<Integer>, Integer>() {
+    Function3<String, Optional<Integer>, State<Integer>, Integer> mappingFunc =
+        new Function3<String, Optional<Integer>, State<Integer>, Integer>() {
 
           @Override
-          public Integer call(Optional<Integer> value, State<Integer> state) throws Exception {
+          public Integer call(String key, Optional<Integer> value, State<Integer> state) throws Exception {
             int sum = value.or(0) + (state.exists() ? state.get() : 0);
             state.update(sum);
             return sum;
@@ -160,29 +160,29 @@ public class JavaTrackStateByKeySuite extends LocalJavaStreamingContext implemen
         };
     testOperation(
         inputData,
-        StateSpec.<String, Integer, Integer, Integer>function(trackStateFunc),
+        StateSpec.<String, Integer, Integer, Integer>function(mappingFunc),
         outputData,
         stateData);
   }
 
   private <K, S, T> void testOperation(
       List<List<K>> input,
-      StateSpec<K, Integer, S, T> trackStateSpec,
+      StateSpec<K, Integer, S, T> mapWithStateSpec,
       List<Set<T>> expectedOutputs,
       List<Set<Tuple2<K, S>>> expectedStateSnapshots) {
     int numBatches = expectedOutputs.size();
     JavaDStream<K> inputStream = JavaTestUtils.attachTestInputStream(ssc, input, 2);
-    JavaTrackStateDStream<K, Integer, S, T> trackeStateStream =
+    JavaMapWithStateDStream<K, Integer, S, T> mapWithStateDStream =
         JavaPairDStream.fromJavaDStream(inputStream.map(new Function<K, Tuple2<K, Integer>>() {
           @Override
           public Tuple2<K, Integer> call(K x) throws Exception {
             return new Tuple2<K, Integer>(x, 1);
           }
-        })).trackStateByKey(trackStateSpec);
+        })).mapWithState(mapWithStateSpec);
 
     final List<Set<T>> collectedOutputs =
         Collections.synchronizedList(Lists.<Set<T>>newArrayList());
-    trackeStateStream.foreachRDD(new Function<JavaRDD<T>, Void>() {
+    mapWithStateDStream.foreachRDD(new Function<JavaRDD<T>, Void>() {
       @Override
       public Void call(JavaRDD<T> rdd) throws Exception {
         collectedOutputs.add(Sets.newHashSet(rdd.collect()));
@@ -191,7 +191,7 @@ public class JavaTrackStateByKeySuite extends LocalJavaStreamingContext implemen
     });
     final List<Set<Tuple2<K, S>>> collectedStateSnapshots =
         Collections.synchronizedList(Lists.<Set<Tuple2<K, S>>>newArrayList());
-    trackeStateStream.stateSnapshots().foreachRDD(new Function<JavaPairRDD<K, S>, Void>() {
+    mapWithStateDStream.stateSnapshots().foreachRDD(new Function<JavaPairRDD<K, S>, Void>() {
       @Override
       public Void call(JavaPairRDD<K, S> rdd) throws Exception {
         collectedStateSnapshots.add(Sets.newHashSet(rdd.collect()));
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TrackStateByKeySuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/MapWithStateSuite.scala
index 1fc320d31b..4b08085e09 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/TrackStateByKeySuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/MapWithStateSuite.scala
@@ -25,11 +25,11 @@ import scala.reflect.ClassTag
 import org.scalatest.PrivateMethodTester._
 import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
 
-import org.apache.spark.streaming.dstream.{DStream, InternalTrackStateDStream, TrackStateDStream, TrackStateDStreamImpl}
+import org.apache.spark.streaming.dstream.{DStream, InternalMapWithStateDStream, MapWithStateDStream, MapWithStateDStreamImpl}
 import org.apache.spark.util.{ManualClock, Utils}
 import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
 
-class TrackStateByKeySuite extends SparkFunSuite
+class MapWithStateSuite extends SparkFunSuite
   with DStreamCheckpointTester with BeforeAndAfterAll with BeforeAndAfter {
 
   private var sc: SparkContext = null
@@ -49,7 +49,7 @@ class TrackStateByKeySuite extends SparkFunSuite
   }
 
   override def beforeAll(): Unit = {
-    val conf = new SparkConf().setMaster("local").setAppName("TrackStateByKeySuite")
+    val conf = new SparkConf().setMaster("local").setAppName("MapWithStateSuite")
     conf.set("spark.streaming.clock", classOf[ManualClock].getName())
     sc = new SparkContext(conf)
   }
@@ -129,7 +129,7 @@ class TrackStateByKeySuite extends SparkFunSuite
     testState(Some(3), shouldBeTimingOut = true)
   }
 
-  test("trackStateByKey - basic operations with simple API") {
+  test("mapWithState - basic operations with simple API") {
     val inputData =
       Seq(
         Seq(),
@@ -164,17 +164,17 @@ class TrackStateByKeySuite extends SparkFunSuite
       )
 
     // state maintains running count, and updated count is returned
-    val trackStateFunc = (value: Option[Int], state: State[Int]) => {
+    val mappingFunc = (key: String, value: Option[Int], state: State[Int]) => {
       val sum = value.getOrElse(0) + state.getOption.getOrElse(0)
       state.update(sum)
       sum
     }
 
     testOperation[String, Int, Int](
-      inputData, StateSpec.function(trackStateFunc), outputData, stateData)
+      inputData, StateSpec.function(mappingFunc), outputData, stateData)
   }
 
-  test("trackStateByKey - basic operations with advanced API") {
+  test("mapWithState - basic operations with advanced API") {
     val inputData =
       Seq(
         Seq(),
@@ -209,65 +209,65 @@ class TrackStateByKeySuite extends SparkFunSuite
       )
 
     // state maintains running count, key string doubled and returned
-    val trackStateFunc = (batchTime: Time, key: String, value: Option[Int], state: State[Int]) => {
+    val mappingFunc = (batchTime: Time, key: String, value: Option[Int], state: State[Int]) => {
       val sum = value.getOrElse(0) + state.getOption.getOrElse(0)
       state.update(sum)
       Some(key * 2)
     }
 
-    testOperation(inputData, StateSpec.function(trackStateFunc), outputData, stateData)
+    testOperation(inputData, StateSpec.function(mappingFunc), outputData, stateData)
   }
 
-  test("trackStateByKey - type inferencing and class tags") {
+  test("mapWithState - type inferencing and class tags") {
 
-    // Simple track state function with value as Int, state as Double and emitted type as Double
-    val simpleFunc = (value: Option[Int], state: State[Double]) => {
+    // Simple track state function with value as Int, state as Double and mapped type as Double
+    val simpleFunc = (key: String, value: Option[Int], state: State[Double]) => {
       0L
     }
 
     // Advanced track state function with key as String, value as Int, state as Double and
-    // emitted type as Double
+    // mapped type as Double
     val advancedFunc = (time: Time, key: String, value: Option[Int], state: State[Double]) => {
       Some(0L)
     }
 
-    def testTypes(dstream: TrackStateDStream[_, _, _, _]): Unit = {
-      val dstreamImpl = dstream.asInstanceOf[TrackStateDStreamImpl[_, _, _, _]]
+    def testTypes(dstream: MapWithStateDStream[_, _, _, _]): Unit = {
+      val dstreamImpl = dstream.asInstanceOf[MapWithStateDStreamImpl[_, _, _, _]]
       assert(dstreamImpl.keyClass === classOf[String])
       assert(dstreamImpl.valueClass === classOf[Int])
       assert(dstreamImpl.stateClass === classOf[Double])
-      assert(dstreamImpl.emittedClass === classOf[Long])
+      assert(dstreamImpl.mappedClass === classOf[Long])
     }
     val ssc = new StreamingContext(sc, batchDuration)
     val inputStream = new TestInputStream[(String, Int)](ssc, Seq.empty, numPartitions = 2)
 
-    // Defining StateSpec inline with trackStateByKey and simple function implicitly gets the types
-    val simpleFunctionStateStream1 = inputStream.trackStateByKey(
+    // Defining StateSpec inline with mapWithState and simple function implicitly gets the types
+    val simpleFunctionStateStream1 = inputStream.mapWithState(
       StateSpec.function(simpleFunc).numPartitions(1))
     testTypes(simpleFunctionStateStream1)
 
     // Separately defining StateSpec with simple function requires explicitly specifying types
     val simpleFuncSpec = StateSpec.function[String, Int, Double, Long](simpleFunc)
-    val simpleFunctionStateStream2 = inputStream.trackStateByKey(simpleFuncSpec)
+    val simpleFunctionStateStream2 = inputStream.mapWithState(simpleFuncSpec)
     testTypes(simpleFunctionStateStream2)
 
     // Separately defining StateSpec with advanced function implicitly gets the types
     val advFuncSpec1 = StateSpec.function(advancedFunc)
-    val advFunctionStateStream1 = inputStream.trackStateByKey(advFuncSpec1)
+    val advFunctionStateStream1 = inputStream.mapWithState(advFuncSpec1)
     testTypes(advFunctionStateStream1)
 
-    // Defining StateSpec inline with trackStateByKey and advanced func implicitly gets the types
-    val advFunctionStateStream2 = inputStream.trackStateByKey(
+    // Defining StateSpec inline with mapWithState and advanced func implicitly gets the types
+    val advFunctionStateStream2 = inputStream.mapWithState(
       StateSpec.function(simpleFunc).numPartitions(1))
     testTypes(advFunctionStateStream2)
 
-    // Defining StateSpec inline with trackStateByKey and advanced func implicitly gets the types
+    // Defining StateSpec inline with mapWithState and advanced func implicitly gets the types
     val advFuncSpec2 = StateSpec.function[String, Int, Double, Long](advancedFunc)
-    val advFunctionStateStream3 = inputStream.trackStateByKey[Double, Long](advFuncSpec2)
+    val advFunctionStateStream3 = inputStream.mapWithState[Double, Long](advFuncSpec2)
     testTypes(advFunctionStateStream3)
   }
 
-  test("trackStateByKey - states as emitted records") {
+  test("mapWithState - states as mapped data") {
     val inputData =
       Seq(
         Seq(),
@@ -301,17 +301,17 @@ class TrackStateByKeySuite extends SparkFunSuite
         Seq(("a", 5), ("b", 3), ("c", 1))
       )
 
-    val trackStateFunc = (time: Time, key: String, value: Option[Int], state: State[Int]) => {
+    val mappingFunc = (time: Time, key: String, value: Option[Int], state: State[Int]) => {
       val sum = value.getOrElse(0) + state.getOption.getOrElse(0)
       val output = (key, sum)
       state.update(sum)
       Some(output)
     }
 
-    testOperation(inputData, StateSpec.function(trackStateFunc), outputData, stateData)
+    testOperation(inputData, StateSpec.function(mappingFunc), outputData, stateData)
   }
 
-  test("trackStateByKey - initial states, with nothing emitted") {
+  test("mapWithState - initial states, with nothing returned as from mapping function") {
 
     val initialState = Seq(("a", 5), ("b", 10), ("c", -20), ("d", 0))
 
@@ -339,18 +339,18 @@ class TrackStateByKeySuite extends SparkFunSuite
         Seq(("a", 10), ("b", 13), ("c", -19), ("d", 0))
       )
 
-    val trackStateFunc = (time: Time, key: String, value: Option[Int], state: State[Int]) => {
+    val mappingFunc = (time: Time, key: String, value: Option[Int], state: State[Int]) => {
       val sum = value.getOrElse(0) + state.getOption.getOrElse(0)
       val output = (key, sum)
       state.update(sum)
       None.asInstanceOf[Option[Int]]
     }
 
-    val trackStateSpec = StateSpec.function(trackStateFunc).initialState(sc.makeRDD(initialState))
-    testOperation(inputData, trackStateSpec, outputData, stateData)
+    val mapWithStateSpec = StateSpec.function(mappingFunc).initialState(sc.makeRDD(initialState))
+    testOperation(inputData, mapWithStateSpec, outputData, stateData)
   }
 
-  test("trackStateByKey - state removing") {
+  test("mapWithState - state removing") {
     val inputData =
       Seq(
         Seq(),
@@ -388,7 +388,7 @@ class TrackStateByKeySuite extends SparkFunSuite
         Seq()
       )
 
-    val trackStateFunc = (time: Time, key: String, value: Option[Int], state: State[Int]) => {
+    val mappingFunc = (time: Time, key: String, value: Option[Int], state: State[Int]) => {
       if (state.exists) {
         state.remove()
         Some(key)
@@ -399,10 +399,10 @@ class TrackStateByKeySuite extends SparkFunSuite
     }
 
     testOperation(
-      inputData, StateSpec.function(trackStateFunc).numPartitions(1), outputData, stateData)
+      inputData, StateSpec.function(mappingFunc).numPartitions(1), outputData, stateData)
   }
 
-  test("trackStateByKey - state timing out") {
+  test("mapWithState - state timing out") {
     val inputData =
       Seq(
         Seq("a", "b", "c"),
@@ -413,7 +413,7 @@ class TrackStateByKeySuite extends SparkFunSuite
         Seq("a") // a will not time out
       ) ++ Seq.fill(20)(Seq("a")) // a will continue to stay active
 
-    val trackStateFunc = (time: Time, key: String, value: Option[Int], state: State[Int]) => {
+    val mappingFunc = (time: Time, key: String, value: Option[Int], state: State[Int]) => {
       if (value.isDefined) {
         state.update(1)
       }
@@ -425,9 +425,9 @@ class TrackStateByKeySuite extends SparkFunSuite
     }
 
     val (collectedOutputs, collectedStateSnapshots) = getOperationOutput(
-      inputData, StateSpec.function(trackStateFunc).timeout(Seconds(3)), 20)
+      inputData, StateSpec.function(mappingFunc).timeout(Seconds(3)), 20)
 
-    // b and c should be emitted once each, when they were marked as expired
+    // b and c should be returned once each, when they were marked as expired
     assert(collectedOutputs.flatten.sorted === Seq("b", "c"))
 
     // States for a, b, c should be defined at one point of time
@@ -439,8 +439,8 @@ class TrackStateByKeySuite extends SparkFunSuite
     assert(collectedStateSnapshots.last.toSet === Set(("a", 1)))
   }
 
-  test("trackStateByKey - checkpoint durations") {
-    val privateMethod = PrivateMethod[InternalTrackStateDStream[_, _, _, _]]('internalStream)
+  test("mapWithState - checkpoint durations") {
+    val privateMethod = PrivateMethod[InternalMapWithStateDStream[_, _, _, _]]('internalStream)
 
     def testCheckpointDuration(
         batchDuration: Duration,
@@ -451,18 +451,18 @@ class TrackStateByKeySuite extends SparkFunSuite
 
       try {
         val inputStream = new TestInputStream(ssc, Seq.empty[Seq[Int]], 2).map(_ -> 1)
-        val dummyFunc = (value: Option[Int], state: State[Int]) => 0
-        val trackStateStream = inputStream.trackStateByKey(StateSpec.function(dummyFunc))
-        val internalTrackStateStream = trackStateStream invokePrivate privateMethod()
+        val dummyFunc = (key: Int, value: Option[Int], state: State[Int]) => 0
+        val mapWithStateStream = inputStream.mapWithState(StateSpec.function(dummyFunc))
+        val internalmapWithStateStream = mapWithStateStream invokePrivate privateMethod()
 
         explicitCheckpointDuration.foreach { d =>
-          trackStateStream.checkpoint(d)
+          mapWithStateStream.checkpoint(d)
         }
-        trackStateStream.register()
+        mapWithStateStream.register()
         ssc.checkpoint(checkpointDir.toString)
         ssc.start()  // should initialize all the checkpoint durations
-        assert(trackStateStream.checkpointDuration === null)
-        assert(internalTrackStateStream.checkpointDuration === expectedCheckpointDuration)
+        assert(mapWithStateStream.checkpointDuration === null)
+        assert(internalmapWithStateStream.checkpointDuration === expectedCheckpointDuration)
       } finally {
         ssc.stop(stopSparkContext = false)
       }
@@ -478,7 +478,7 @@ class TrackStateByKeySuite extends SparkFunSuite
   }
 
 
-  test("trackStateByKey - driver failure recovery") {
+  test("mapWithState - driver failure recovery") {
     val inputData =
       Seq(
         Seq(),
@@ -505,16 +505,16 @@ class TrackStateByKeySuite extends SparkFunSuite
 
       val checkpointDuration = batchDuration * (stateData.size / 2)
 
-      val runningCount = (value: Option[Int], state: State[Int]) => {
+      val runningCount = (key: String, value: Option[Int], state: State[Int]) => {
         state.update(state.getOption().getOrElse(0) + value.getOrElse(0))
         state.get()
       }
 
-      val trackStateStream = dstream.map { _ -> 1 }.trackStateByKey(
+      val mapWithStateStream = dstream.map { _ -> 1 }.mapWithState(
         StateSpec.function(runningCount))
       // Set internval make sure there is one RDD checkpointing
-      trackStateStream.checkpoint(checkpointDuration)
-      trackStateStream.stateSnapshots()
+      mapWithStateStream.checkpoint(checkpointDuration)
+      mapWithStateStream.stateSnapshots()
     }
 
     testCheckpointedOperation(inputData, operation, stateData, inputData.size / 2,
@@ -523,28 +523,28 @@ class TrackStateByKeySuite extends SparkFunSuite
 
   private def testOperation[K: ClassTag, S: ClassTag, T: ClassTag](
       input: Seq[Seq[K]],
-      trackStateSpec: StateSpec[K, Int, S, T],
+      mapWithStateSpec: StateSpec[K, Int, S, T],
       expectedOutputs: Seq[Seq[T]],
       expectedStateSnapshots: Seq[Seq[(K, S)]]
     ): Unit = {
     require(expectedOutputs.size == expectedStateSnapshots.size)
 
     val (collectedOutputs, collectedStateSnapshots) =
-      getOperationOutput(input, trackStateSpec, expectedOutputs.size)
+      getOperationOutput(input, mapWithStateSpec, expectedOutputs.size)
     assert(expectedOutputs, collectedOutputs, "outputs")
     assert(expectedStateSnapshots, collectedStateSnapshots, "state snapshots")
   }
 
   private def getOperationOutput[K: ClassTag, S: ClassTag, T: ClassTag](
       input: Seq[Seq[K]],
-      trackStateSpec: StateSpec[K, Int, S, T],
+      mapWithStateSpec: StateSpec[K, Int, S, T],
       numBatches: Int
     ): (Seq[Seq[T]], Seq[Seq[(K, S)]]) = {
 
     // Setup the stream computation
     val ssc = new StreamingContext(sc, Seconds(1))
     val inputStream = new TestInputStream(ssc, input, numPartitions = 2)
-    val trackeStateStream = inputStream.map(x => (x, 1)).trackStateByKey(trackStateSpec)
+    val trackeStateStream = inputStream.map(x => (x, 1)).mapWithState(mapWithStateSpec)
     val collectedOutputs = new ArrayBuffer[Seq[T]] with SynchronizedBuffer[Seq[T]]
     val outputStream = new TestOutputStream(trackeStateStream, collectedOutputs)
     val collectedStateSnapshots = new ArrayBuffer[Seq[(K, S)]] with SynchronizedBuffer[Seq[(K, S)]]
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/rdd/TrackStateRDDSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/rdd/MapWithStateRDDSuite.scala
index 3b2d43f2ce..aa95bd33dd 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/rdd/TrackStateRDDSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/rdd/MapWithStateRDDSuite.scala
@@ -30,14 +30,14 @@ import org.apache.spark.streaming.util.OpenHashMapBasedStateMap
 import org.apache.spark.streaming.{State, Time}
 import org.apache.spark.util.Utils
 
-class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with BeforeAndAfterAll {
+class MapWithStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with BeforeAndAfterAll {
 
   private var sc: SparkContext = null
   private var checkpointDir: File = _
 
   override def beforeAll(): Unit = {
     sc = new SparkContext(
-      new SparkConf().setMaster("local").setAppName("TrackStateRDDSuite"))
+      new SparkConf().setMaster("local").setAppName("MapWithStateRDDSuite"))
     checkpointDir = Utils.createTempDir()
     sc.setCheckpointDir(checkpointDir.toString)
   }
@@ -54,7 +54,7 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
   test("creation from pair RDD") {
     val data = Seq((1, "1"), (2, "2"), (3, "3"))
     val partitioner = new HashPartitioner(10)
-    val rdd = TrackStateRDD.createFromPairRDD[Int, Int, String, Int](
+    val rdd = MapWithStateRDD.createFromPairRDD[Int, Int, String, Int](
       sc.parallelize(data), partitioner, Time(123))
     assertRDD[Int, Int, String, Int](rdd, data.map { x => (x._1, x._2, 123)}.toSet, Set.empty)
     assert(rdd.partitions.size === partitioner.numPartitions)
@@ -62,7 +62,7 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
     assert(rdd.partitioner === Some(partitioner))
   }
 
-  test("updating state and generating emitted data in TrackStateRecord") {
+  test("updating state and generating mapped data in MapWithStateRDDRecord") {
 
     val initialTime = 1000L
     val updatedTime = 2000L
@@ -71,7 +71,7 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
 
     /**
      * Assert that applying given data on a prior record generates correct updated record, with
-     * correct state map and emitted data
+     * correct state map and mapped data
      */
     def assertRecordUpdate(
         initStates: Iterable[Int],
@@ -86,18 +86,18 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
       val initialStateMap = new OpenHashMapBasedStateMap[String, Int]()
       initStates.foreach { s => initialStateMap.put("key", s, initialTime) }
       functionCalled = false
-      val record = TrackStateRDDRecord[String, Int, Int](initialStateMap, Seq.empty)
+      val record = MapWithStateRDDRecord[String, Int, Int](initialStateMap, Seq.empty)
       val dataIterator = data.map { v => ("key", v) }.iterator
       val removedStates = new ArrayBuffer[Int]
       val timingOutStates = new ArrayBuffer[Int]
       /**
-       * Tracking function that updates/removes state based on instructions in the data, and
+       * Mapping function that updates/removes state based on instructions in the data, and
        * return state (when instructed or when state is timing out).
        */
       def testFunc(t: Time, key: String, data: Option[String], state: State[Int]): Option[Int] = {
         functionCalled = true
 
-        assert(t.milliseconds === updatedTime, "tracking func called with wrong time")
+        assert(t.milliseconds === updatedTime, "mapping func called with wrong time")
 
         data match {
           case Some("noop") =>
@@ -120,22 +120,22 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
         }
       }
 
-      val updatedRecord = TrackStateRDDRecord.updateRecordWithData[String, String, Int, Int](
+      val updatedRecord = MapWithStateRDDRecord.updateRecordWithData[String, String, Int, Int](
         Some(record), dataIterator, testFunc,
         Time(updatedTime), timeoutThreshold, removeTimedoutData)
 
       val updatedStateData = updatedRecord.stateMap.getAll().map { x => (x._2, x._3) }
       assert(updatedStateData.toSet === expectedStates.toSet,
-        "states do not match after updating the TrackStateRecord")
+        "states do not match after updating the MapWithStateRDDRecord")
 
-      assert(updatedRecord.emittedRecords.toSet === expectedOutput.toSet,
-        "emitted data do not match after updating the TrackStateRecord")
+      assert(updatedRecord.mappedData.toSet === expectedOutput.toSet,
+        "mapped data do not match after updating the MapWithStateRDDRecord")
 
       assert(timingOutStates.toSet === expectedTimingOutStates.toSet, "timing out states do not " +
-        "match those that were expected to do so while updating the TrackStateRecord")
+        "match those that were expected to do so while updating the MapWithStateRDDRecord")
 
       assert(removedStates.toSet === expectedRemovedStates.toSet, "removed states do not " +
-        "match those that were expected to do so while updating the TrackStateRecord")
+        "match those that were expected to do so while updating the MapWithStateRDDRecord")
 
     }
 
@@ -187,12 +187,12 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
 
   }
 
-  test("states generated by TrackStateRDD") {
+  test("states generated by MapWithStateRDD") {
     val initStates = Seq(("k1", 0), ("k2", 0))
     val initTime = 123
     val initStateWthTime = initStates.map { x => (x._1, x._2, initTime) }.toSet
     val partitioner = new HashPartitioner(2)
-    val initStateRDD = TrackStateRDD.createFromPairRDD[String, Int, Int, Int](
+    val initStateRDD = MapWithStateRDD.createFromPairRDD[String, Int, Int, Int](
       sc.parallelize(initStates), partitioner, Time(initTime)).persist()
     assertRDD(initStateRDD, initStateWthTime, Set.empty)
 
@@ -203,21 +203,21 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
      * creates a new state RDD with expected states
      */
     def testStateUpdates(
-        testStateRDD: TrackStateRDD[String, Int, Int, Int],
+        testStateRDD: MapWithStateRDD[String, Int, Int, Int],
         testData: Seq[(String, Int)],
-        expectedStates: Set[(String, Int, Int)]): TrackStateRDD[String, Int, Int, Int] = {
+        expectedStates: Set[(String, Int, Int)]): MapWithStateRDD[String, Int, Int, Int] = {
 
-      // Persist the test TrackStateRDD so that its not recomputed while doing the next operation.
-      // This is to make sure that we only track which state keys are being touched in the next op.
+      // Persist the test MapWithStateRDD so that its not recomputed while doing the next operation.
+      // This is to make sure that we only touch which state keys are being touched in the next op.
       testStateRDD.persist().count()
 
       // To track which keys are being touched
-      TrackStateRDDSuite.touchedStateKeys.clear()
+      MapWithStateRDDSuite.touchedStateKeys.clear()
 
-      val trackingFunc = (time: Time, key: String, data: Option[Int], state: State[Int]) => {
+      val mappingFunction = (time: Time, key: String, data: Option[Int], state: State[Int]) => {
 
         // Track the key that has been touched
-        TrackStateRDDSuite.touchedStateKeys += key
+        MapWithStateRDDSuite.touchedStateKeys += key
 
         // If the data is 0, do not do anything with the state
         // else if the data is 1, increment the state if it exists, or set new state to 0
@@ -236,12 +236,12 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
 
       // Assert that the new state RDD has expected state data
       val newStateRDD = assertOperation(
-        testStateRDD, newDataRDD, trackingFunc, updateTime, expectedStates, Set.empty)
+        testStateRDD, newDataRDD, mappingFunction, updateTime, expectedStates, Set.empty)
 
       // Assert that the function was called only for the keys present in the data
-      assert(TrackStateRDDSuite.touchedStateKeys.size === testData.size,
+      assert(MapWithStateRDDSuite.touchedStateKeys.size === testData.size,
         "More number of keys are being touched than that is expected")
-      assert(TrackStateRDDSuite.touchedStateKeys.toSet === testData.toMap.keys,
+      assert(MapWithStateRDDSuite.touchedStateKeys.toSet === testData.toMap.keys,
         "Keys not in the data are being touched unexpectedly")
 
       // Assert that the test RDD's data has not changed
@@ -289,19 +289,19 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
 
   test("checkpointing") {
     /**
-     * This tests whether the TrackStateRDD correctly truncates any references to its parent RDDs -
-     * the data RDD and the parent TrackStateRDD.
+     * This tests whether the MapWithStateRDD correctly truncates any references to its parent RDDs
+     * - the data RDD and the parent MapWithStateRDD.
      */
-    def rddCollectFunc(rdd: RDD[TrackStateRDDRecord[Int, Int, Int]])
+    def rddCollectFunc(rdd: RDD[MapWithStateRDDRecord[Int, Int, Int]])
       : Set[(List[(Int, Int, Long)], List[Int])] = {
-      rdd.map { record => (record.stateMap.getAll().toList, record.emittedRecords.toList) }
+      rdd.map { record => (record.stateMap.getAll().toList, record.mappedData.toList) }
          .collect.toSet
     }
 
-    /** Generate TrackStateRDD with data RDD having a long lineage */
+    /** Generate MapWithStateRDD with data RDD having a long lineage */
     def makeStateRDDWithLongLineageDataRDD(longLineageRDD: RDD[Int])
-      : TrackStateRDD[Int, Int, Int, Int] = {
-      TrackStateRDD.createFromPairRDD(longLineageRDD.map { _ -> 1}, partitioner, Time(0))
+      : MapWithStateRDD[Int, Int, Int, Int] = {
+      MapWithStateRDD.createFromPairRDD(longLineageRDD.map { _ -> 1}, partitioner, Time(0))
     }
 
     testRDD(
@@ -309,15 +309,15 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
     testRDDPartitions(
       makeStateRDDWithLongLineageDataRDD, reliableCheckpoint = true, rddCollectFunc _)
 
-    /** Generate TrackStateRDD with parent state RDD having a long lineage */
+    /** Generate MapWithStateRDD with parent state RDD having a long lineage */
     def makeStateRDDWithLongLineageParenttateRDD(
-        longLineageRDD: RDD[Int]): TrackStateRDD[Int, Int, Int, Int] = {
+        longLineageRDD: RDD[Int]): MapWithStateRDD[Int, Int, Int, Int] = {
 
-      // Create a TrackStateRDD that has a long lineage using the data RDD with a long lineage
+      // Create a MapWithStateRDD that has a long lineage using the data RDD with a long lineage
       val stateRDDWithLongLineage = makeStateRDDWithLongLineageDataRDD(longLineageRDD)
 
-      // Create a new TrackStateRDD, with the lineage lineage TrackStateRDD as the parent
-      new TrackStateRDD[Int, Int, Int, Int](
+      // Create a new MapWithStateRDD, with the lineage lineage MapWithStateRDD as the parent
+      new MapWithStateRDD[Int, Int, Int, Int](
         stateRDDWithLongLineage,
         stateRDDWithLongLineage.sparkContext.emptyRDD[(Int, Int)].partitionBy(partitioner),
         (time: Time, key: Int, value: Option[Int], state: State[Int]) => None,
@@ -333,25 +333,25 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
   }
 
   test("checkpointing empty state RDD") {
-    val emptyStateRDD = TrackStateRDD.createFromPairRDD[Int, Int, Int, Int](
+    val emptyStateRDD = MapWithStateRDD.createFromPairRDD[Int, Int, Int, Int](
       sc.emptyRDD[(Int, Int)], new HashPartitioner(10), Time(0))
     emptyStateRDD.checkpoint()
     assert(emptyStateRDD.flatMap { _.stateMap.getAll() }.collect().isEmpty)
-    val cpRDD = sc.checkpointFile[TrackStateRDDRecord[Int, Int, Int]](
+    val cpRDD = sc.checkpointFile[MapWithStateRDDRecord[Int, Int, Int]](
       emptyStateRDD.getCheckpointFile.get)
     assert(cpRDD.flatMap { _.stateMap.getAll() }.collect().isEmpty)
   }
 
-  /** Assert whether the `trackStateByKey` operation generates expected results */
+  /** Assert whether the `mapWithState` operation generates expected results */
   private def assertOperation[K: ClassTag, V: ClassTag, S: ClassTag, T: ClassTag](
-      testStateRDD: TrackStateRDD[K, V, S, T],
+      testStateRDD: MapWithStateRDD[K, V, S, T],
       newDataRDD: RDD[(K, V)],
-      trackStateFunc: (Time, K, Option[V], State[S]) => Option[T],
+      mappingFunction: (Time, K, Option[V], State[S]) => Option[T],
       currentTime: Long,
       expectedStates: Set[(K, S, Int)],
-      expectedEmittedRecords: Set[T],
+      expectedMappedData: Set[T],
       doFullScan: Boolean = false
-    ): TrackStateRDD[K, V, S, T] = {
+    ): MapWithStateRDD[K, V, S, T] = {
 
     val partitionedNewDataRDD = if (newDataRDD.partitioner != testStateRDD.partitioner) {
       newDataRDD.partitionBy(testStateRDD.partitioner.get)
@@ -359,31 +359,31 @@ class TrackStateRDDSuite extends SparkFunSuite with RDDCheckpointTester with Bef
       newDataRDD
     }
 
-    val newStateRDD = new TrackStateRDD[K, V, S, T](
-      testStateRDD, newDataRDD, trackStateFunc, Time(currentTime), None)
+    val newStateRDD = new MapWithStateRDD[K, V, S, T](
+      testStateRDD, newDataRDD, mappingFunction, Time(currentTime), None)
     if (doFullScan) newStateRDD.setFullScan()
 
     // Persist to make sure that it gets computed only once and we can track precisely how many
     // state keys the computing touched
     newStateRDD.persist().count()
-    assertRDD(newStateRDD, expectedStates, expectedEmittedRecords)
+    assertRDD(newStateRDD, expectedStates, expectedMappedData)
     newStateRDD
   }
 
-  /** Assert whether the [[TrackStateRDD]] has the expected state ad emitted records */
+  /** Assert whether the [[MapWithStateRDD]] has the expected state and mapped data */
   private def assertRDD[K: ClassTag, V: ClassTag, S: ClassTag, T: ClassTag](
-      trackStateRDD: TrackStateRDD[K, V, S, T],
+      stateRDD: MapWithStateRDD[K, V, S, T],
       expectedStates: Set[(K, S, Int)],
-      expectedEmittedRecords: Set[T]): Unit = {
-    val states = trackStateRDD.flatMap { _.stateMap.getAll() }.collect().toSet
-    val emittedRecords = trackStateRDD.flatMap { _.emittedRecords }.collect().toSet
+      expectedMappedData: Set[T]): Unit = {
+    val states = stateRDD.flatMap { _.stateMap.getAll() }.collect().toSet
+    val mappedData = stateRDD.flatMap { _.mappedData }.collect().toSet
     assert(states === expectedStates,
-      "states after track state operation were not as expected")
-    assert(emittedRecords === expectedEmittedRecords,
-      "emitted records after track state operation were not as expected")
+      "states after mapWithState operation were not as expected")
+    assert(mappedData === expectedMappedData,
+      "mapped data after mapWithState operation were not as expected")
   }
 }
 
-object TrackStateRDDSuite {
+object MapWithStateRDDSuite {
   private val touchedStateKeys = new ArrayBuffer[String]()
 }