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Posted to commits@spark.apache.org by zs...@apache.org on 2015/12/10 05:47:21 UTC
[2/2] spark git commit: [SPARK-12244][SPARK-12245][STREAMING] Rename
trackStateByKey to mapWithState and change tracking function signature
[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 <ta...@gmail.com>
Closes #10224 from tdas/rename.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/bd2cd4f5
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/bd2cd4f5
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/bd2cd4f5
Branch: refs/heads/master
Commit: bd2cd4f53d1ca10f4896bd39b0e180d4929867a2
Parents: 2166c2a
Author: Tathagata Das <ta...@gmail.com>
Authored: Wed Dec 9 20:47:15 2015 -0800
Committer: Shixiong Zhu <sh...@databricks.com>
Committed: Wed Dec 9 20:47:15 2015 -0800
----------------------------------------------------------------------
.../streaming/JavaStatefulNetworkWordCount.java | 16 +-
.../streaming/StatefulNetworkWordCount.scala | 12 +-
.../apache/spark/streaming/Java8APISuite.java | 18 +-
.../org/apache/spark/streaming/State.scala | 20 +-
.../org/apache/spark/streaming/StateSpec.scala | 160 ++---
.../api/java/JavaMapWithStateDStream.scala | 44 ++
.../streaming/api/java/JavaPairDStream.scala | 50 +-
.../api/java/JavaTrackStateDStream.scala | 44 --
.../streaming/dstream/MapWithStateDStream.scala | 170 ++++++
.../dstream/PairDStreamFunctions.scala | 41 +-
.../streaming/dstream/TrackStateDStream.scala | 171 ------
.../spark/streaming/rdd/MapWithStateRDD.scala | 223 +++++++
.../spark/streaming/rdd/TrackStateRDD.scala | 228 --------
.../spark/streaming/JavaMapWithStateSuite.java | 210 +++++++
.../streaming/JavaTrackStateByKeySuite.java | 210 -------
.../spark/streaming/MapWithStateSuite.scala | 581 +++++++++++++++++++
.../spark/streaming/TrackStateByKeySuite.scala | 581 -------------------
.../streaming/rdd/MapWithStateRDDSuite.scala | 389 +++++++++++++
.../streaming/rdd/TrackStateRDDSuite.scala | 389 -------------
19 files changed, 1782 insertions(+), 1775 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
index c400e42..14997c6 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
@@ -65,7 +65,7 @@ public class JavaStatefulNetworkWordCount {
JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1));
ssc.checkpoint(".");
- // Initial RDD input to trackStateByKey
+ // Initial state RDD input to mapWithState
@SuppressWarnings("unchecked")
List<Tuple2<String, Integer>> tuples = Arrays.asList(new Tuple2<String, Integer>("hello", 1),
new Tuple2<String, Integer>("world", 1));
@@ -90,21 +90,21 @@ public class JavaStatefulNetworkWordCount {
});
// Update the cumulative count function
- final Function4<Time, String, Optional<Integer>, State<Integer>, Optional<Tuple2<String, Integer>>> trackStateFunc =
- new Function4<Time, String, Optional<Integer>, State<Integer>, Optional<Tuple2<String, Integer>>>() {
+ final Function3<String, Optional<Integer>, State<Integer>, Tuple2<String, Integer>> mappingFunc =
+ new Function3<String, Optional<Integer>, State<Integer>, Tuple2<String, Integer>>() {
@Override
- public Optional<Tuple2<String, Integer>> call(Time time, String word, Optional<Integer> one, State<Integer> state) {
+ public Tuple2<String, Integer> call(String word, Optional<Integer> one, State<Integer> state) {
int sum = one.or(0) + (state.exists() ? state.get() : 0);
Tuple2<String, Integer> output = new Tuple2<String, Integer>(word, sum);
state.update(sum);
- return Optional.of(output);
+ return output;
}
};
- // This will give a Dstream made of state (which is the cumulative count of the words)
- JavaTrackStateDStream<String, Integer, Integer, Tuple2<String, Integer>> stateDstream =
- wordsDstream.trackStateByKey(StateSpec.function(trackStateFunc).initialState(initialRDD));
+ // DStream made of get cumulative counts that get updated in every batch
+ JavaMapWithStateDStream<String, Integer, Integer, Tuple2<String, Integer>> stateDstream =
+ wordsDstream.mapWithState(StateSpec.function(mappingFunc).initialState(initialRDD));
stateDstream.print();
ssc.start();
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
index a4f847f..2dce182 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
@@ -49,7 +49,7 @@ object StatefulNetworkWordCount {
val ssc = new StreamingContext(sparkConf, Seconds(1))
ssc.checkpoint(".")
- // Initial RDD input to trackStateByKey
+ // Initial state RDD for mapWithState operation
val initialRDD = ssc.sparkContext.parallelize(List(("hello", 1), ("world", 1)))
// Create a ReceiverInputDStream on target ip:port and count the
@@ -58,17 +58,17 @@ object StatefulNetworkWordCount {
val words = lines.flatMap(_.split(" "))
val wordDstream = words.map(x => (x, 1))
- // Update the cumulative count using updateStateByKey
+ // Update the cumulative count using mapWithState
// This will give a DStream made of state (which is the cumulative count of the words)
- val trackStateFunc = (batchTime: Time, word: String, one: Option[Int], state: State[Int]) => {
+ val mappingFunc = (word: String, one: Option[Int], state: State[Int]) => {
val sum = one.getOrElse(0) + state.getOption.getOrElse(0)
val output = (word, sum)
state.update(sum)
- Some(output)
+ output
}
- val stateDstream = wordDstream.trackStateByKey(
- StateSpec.function(trackStateFunc).initialState(initialRDD))
+ val stateDstream = wordDstream.mapWithState(
+ StateSpec.function(mappingFunc).initialState(initialRDD))
stateDstream.print()
ssc.start()
ssc.awaitTermination()
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/extras/java8-tests/src/test/java/org/apache/spark/streaming/Java8APISuite.java
----------------------------------------------------------------------
diff --git a/extras/java8-tests/src/test/java/org/apache/spark/streaming/Java8APISuite.java b/extras/java8-tests/src/test/java/org/apache/spark/streaming/Java8APISuite.java
index 4eee97b..89e0c7f 100644
--- a/extras/java8-tests/src/test/java/org/apache/spark/streaming/Java8APISuite.java
+++ b/extras/java8-tests/src/test/java/org/apache/spark/streaming/Java8APISuite.java
@@ -32,12 +32,10 @@ import org.apache.spark.Accumulator;
import org.apache.spark.HashPartitioner;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.function.Function2;
-import org.apache.spark.api.java.function.Function4;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
-import org.apache.spark.streaming.api.java.JavaTrackStateDStream;
+import org.apache.spark.streaming.api.java.JavaMapWithStateDStream;
/**
* Most of these tests replicate org.apache.spark.streaming.JavaAPISuite using java 8
@@ -863,12 +861,12 @@ public class Java8APISuite extends LocalJavaStreamingContext implements Serializ
/**
* This test is only for testing the APIs. It's not necessary to run it.
*/
- public void testTrackStateByAPI() {
+ public void testMapWithStateAPI() {
JavaPairRDD<String, Boolean> initialRDD = null;
JavaPairDStream<String, Integer> wordsDstream = null;
- JavaTrackStateDStream<String, Integer, Boolean, Double> stateDstream =
- wordsDstream.trackStateByKey(
+ JavaMapWithStateDStream<String, Integer, Boolean, Double> stateDstream =
+ wordsDstream.mapWithState(
StateSpec.<String, Integer, Boolean, Double> function((time, key, value, state) -> {
// Use all State's methods here
state.exists();
@@ -884,9 +882,9 @@ public class Java8APISuite extends LocalJavaStreamingContext implements Serializ
JavaPairDStream<String, Boolean> emittedRecords = stateDstream.stateSnapshots();
- JavaTrackStateDStream<String, Integer, Boolean, Double> stateDstream2 =
- wordsDstream.trackStateByKey(
- StateSpec.<String, Integer, Boolean, Double>function((value, state) -> {
+ JavaMapWithStateDStream<String, Integer, Boolean, Double> stateDstream2 =
+ wordsDstream.mapWithState(
+ StateSpec.<String, Integer, Boolean, Double>function((key, value, state) -> {
state.exists();
state.get();
state.isTimingOut();
@@ -898,6 +896,6 @@ public class Java8APISuite extends LocalJavaStreamingContext implements Serializ
.partitioner(new HashPartitioner(10))
.timeout(Durations.seconds(10)));
- JavaPairDStream<String, Boolean> emittedRecords2 = stateDstream2.stateSnapshots();
+ JavaPairDStream<String, Boolean> mappedDStream = stateDstream2.stateSnapshots();
}
}
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/main/scala/org/apache/spark/streaming/State.scala
----------------------------------------------------------------------
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 604e64f..b47bdda 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] {
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/main/scala/org/apache/spark/streaming/StateSpec.scala
----------------------------------------------------------------------
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 bea5b9d..9f6f952 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)
}
}
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaMapWithStateDStream.scala
----------------------------------------------------------------------
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaMapWithStateDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaMapWithStateDStream.scala
new file mode 100644
index 0000000..16c0d6f
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaMapWithStateDStream.scala
@@ -0,0 +1,44 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+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.MapWithStateDStream
+
+/**
+ * :: Experimental ::
+ * 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 keys
+ * @tparam ValueType Class of the values
+ * @tparam StateType Class of the state data
+ * @tparam MappedType Class of the mapped data
+ */
+@Experimental
+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())(
+ JavaSparkContext.fakeClassTag,
+ JavaSparkContext.fakeClassTag)
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
----------------------------------------------------------------------
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 70e32b3..42ddd63 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))
}
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaTrackStateDStream.scala
----------------------------------------------------------------------
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/JavaTrackStateDStream.scala
deleted file mode 100644
index f459930..0000000
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaTrackStateDStream.scala
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-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
-
-/**
- * :: 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
- * 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
- */
-@Experimental
-class JavaTrackStateDStream[KeyType, ValueType, StateType, EmittedType](
- dstream: TrackStateDStream[KeyType, ValueType, StateType, EmittedType])
- extends JavaDStream[EmittedType](dstream)(JavaSparkContext.fakeClassTag) {
-
- def stateSnapshots(): JavaPairDStream[KeyType, StateType] =
- new JavaPairDStream(dstream.stateSnapshots())(
- JavaSparkContext.fakeClassTag,
- JavaSparkContext.fakeClassTag)
-}
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapWithStateDStream.scala
----------------------------------------------------------------------
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapWithStateDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapWithStateDStream.scala
new file mode 100644
index 0000000..706465d
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapWithStateDStream.scala
@@ -0,0 +1,170 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.dstream
+
+import scala.reflect.ClassTag
+
+import org.apache.spark._
+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.{MapWithStateRDD, MapWithStateRDDRecord}
+import org.apache.spark.streaming.dstream.InternalMapWithStateDStream._
+
+/**
+ * :: Experimental ::
+ * 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 key
+ * @tparam ValueType Class of the value
+ * @tparam StateType Class of the state data
+ * @tparam MappedType Class of the mapped data
+ */
+@Experimental
+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 [[MapWithStateDStream]] */
+private[streaming] class MapWithStateDStreamImpl[
+ KeyType: ClassTag, ValueType: ClassTag, StateType: ClassTag, MappedType: ClassTag](
+ dataStream: DStream[(KeyType, ValueType)],
+ spec: StateSpecImpl[KeyType, ValueType, StateType, MappedType])
+ extends MapWithStateDStream[KeyType, ValueType, StateType, MappedType](dataStream.context) {
+
+ private val internalStream =
+ 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[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[MappedType] = {
+ internalStream.checkpoint(checkpointInterval)
+ this
+ }
+
+ /** Return a pair DStream where each RDD is the snapshot of the state of all the keys. */
+ def stateSnapshots(): DStream[(KeyType, StateType)] = {
+ internalStream.flatMap {
+ _.stateMap.getAll().map { case (k, s, _) => (k, s) }.toTraversable }
+ }
+
+ def keyClass: Class[_] = implicitly[ClassTag[KeyType]].runtimeClass
+
+ def valueClass: Class[_] = implicitly[ClassTag[ValueType]].runtimeClass
+
+ def stateClass: Class[_] = implicitly[ClassTag[StateType]].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 `mapWithState`
+ * operation on DStreams.
+ *
+ * @param parent Parent (key, value) stream that is the source
+ * @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 mapped data
+ */
+private[streaming]
+class InternalMapWithStateDStream[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
+ parent: DStream[(K, V)], spec: StateSpecImpl[K, V, S, E])
+ 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 mappingFunction = spec.getFunction()
+
+ override def slideDuration: Duration = parent.slideDuration
+
+ override def dependencies: List[DStream[_]] = List(parent)
+
+ /** Enable automatic checkpointing */
+ override val mustCheckpoint = true
+
+ /** Override the default checkpoint duration */
+ override def initialize(time: Time): Unit = {
+ if (checkpointDuration == null) {
+ checkpointDuration = slideDuration * DEFAULT_CHECKPOINT_DURATION_MULTIPLIER
+ }
+ super.initialize(time)
+ }
+
+ /** Method that generates a RDD for the given time */
+ 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) =>
+ if (rdd.partitioner != Some(partitioner)) {
+ // 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
+ MapWithStateRDD.createFromRDD[K, V, S, E](
+ rdd.flatMap { _.stateMap.getAll() }, partitioner, validTime)
+ } else {
+ rdd
+ }
+ case None =>
+ MapWithStateRDD.createFromPairRDD[K, V, S, E](
+ spec.getInitialStateRDD().getOrElse(new EmptyRDD[(K, S)](ssc.sparkContext)),
+ partitioner,
+ validTime
+ )
+ }
+
+
+ // Compute the new state RDD with previous state RDD and partitioned data RDD
+ // Even if there is no data RDD, use an empty one to create a new state RDD
+ val dataRDD = parent.getOrCompute(validTime).getOrElse {
+ context.sparkContext.emptyRDD[(K, V)]
+ }
+ val partitionedDataRDD = dataRDD.partitionBy(partitioner)
+ val timeoutThresholdTime = spec.getTimeoutInterval().map { interval =>
+ (validTime - interval).milliseconds
+ }
+ Some(new MapWithStateRDD(
+ prevStateRDD, partitionedDataRDD, mappingFunction, validTime, timeoutThresholdTime))
+ }
+}
+
+private[streaming] object InternalMapWithStateDStream {
+ private val DEFAULT_CHECKPOINT_DURATION_MULTIPLIER = 10
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala
----------------------------------------------------------------------
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 2762309..a64a1fe 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]]
)
}
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/main/scala/org/apache/spark/streaming/dstream/TrackStateDStream.scala
----------------------------------------------------------------------
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TrackStateDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/TrackStateDStream.scala
deleted file mode 100644
index ea62134..0000000
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TrackStateDStream.scala
+++ /dev/null
@@ -1,171 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.streaming.dstream
-
-import scala.reflect.ClassTag
-
-import org.apache.spark._
-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._
-
-/**
- * :: Experimental ::
- * DStream representing the stream of records emitted by the tracking function in the
- * `trackStateByKey` 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 StateType Class of the state data
- * @tparam EmittedType Class of the emitted records
- */
-@Experimental
-sealed abstract class TrackStateDStream[KeyType, ValueType, StateType, EmittedType: ClassTag](
- ssc: StreamingContext) extends DStream[EmittedType](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](
- dataStream: DStream[(KeyType, ValueType)],
- spec: StateSpecImpl[KeyType, ValueType, StateType, EmittedType])
- extends TrackStateDStream[KeyType, ValueType, StateType, EmittedType](dataStream.context) {
-
- private val internalStream =
- new InternalTrackStateDStream[KeyType, ValueType, StateType, EmittedType](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 } }
- }
-
- /**
- * 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] = {
- internalStream.checkpoint(checkpointInterval)
- this
- }
-
- /** Return a pair DStream where each RDD is the snapshot of the state of all the keys. */
- def stateSnapshots(): DStream[(KeyType, StateType)] = {
- internalStream.flatMap {
- _.stateMap.getAll().map { case (k, s, _) => (k, s) }.toTraversable }
- }
-
- def keyClass: Class[_] = implicitly[ClassTag[KeyType]].runtimeClass
-
- def valueClass: Class[_] = implicitly[ClassTag[ValueType]].runtimeClass
-
- def stateClass: Class[_] = implicitly[ClassTag[StateType]].runtimeClass
-
- def emittedClass: Class[_] = implicitly[ClassTag[EmittedType]].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`
- * operation on DStreams.
- *
- * @param parent Parent (key, value) stream that is the source
- * @param spec Specifications of the trackStateByKey operation
- * @tparam K Key type
- * @tparam V Value type
- * @tparam S Type of the state maintained
- * @tparam E Type of the emitted data
- */
-private[streaming]
-class InternalTrackStateDStream[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) {
-
- persist(StorageLevel.MEMORY_ONLY)
-
- private val partitioner = spec.getPartitioner().getOrElse(
- new HashPartitioner(ssc.sc.defaultParallelism))
-
- private val trackingFunction = spec.getFunction()
-
- override def slideDuration: Duration = parent.slideDuration
-
- override def dependencies: List[DStream[_]] = List(parent)
-
- /** Enable automatic checkpointing */
- override val mustCheckpoint = true
-
- /** Override the default checkpoint duration */
- override def initialize(time: Time): Unit = {
- if (checkpointDuration == null) {
- checkpointDuration = slideDuration * DEFAULT_CHECKPOINT_DURATION_MULTIPLIER
- }
- super.initialize(time)
- }
-
- /** Method that generates a RDD for the given time */
- override def compute(validTime: Time): Option[RDD[TrackStateRDDRecord[K, S, E]]] = {
- // Get the previous state or create a new empty state RDD
- val prevStateRDD = getOrCompute(validTime - slideDuration) match {
- case Some(rdd) =>
- if (rdd.partitioner != Some(partitioner)) {
- // 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](
- rdd.flatMap { _.stateMap.getAll() }, partitioner, validTime)
- } else {
- rdd
- }
- case None =>
- TrackStateRDD.createFromPairRDD[K, V, S, E](
- spec.getInitialStateRDD().getOrElse(new EmptyRDD[(K, S)](ssc.sparkContext)),
- partitioner,
- validTime
- )
- }
-
-
- // Compute the new state RDD with previous state RDD and partitioned data RDD
- // Even if there is no data RDD, use an empty one to create a new state RDD
- val dataRDD = parent.getOrCompute(validTime).getOrElse {
- context.sparkContext.emptyRDD[(K, V)]
- }
- val partitionedDataRDD = dataRDD.partitionBy(partitioner)
- val timeoutThresholdTime = spec.getTimeoutInterval().map { interval =>
- (validTime - interval).milliseconds
- }
- Some(new TrackStateRDD(
- prevStateRDD, partitionedDataRDD, trackingFunction, validTime, timeoutThresholdTime))
- }
-}
-
-private[streaming] object InternalTrackStateDStream {
- private val DEFAULT_CHECKPOINT_DURATION_MULTIPLIER = 10
-}
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/main/scala/org/apache/spark/streaming/rdd/MapWithStateRDD.scala
----------------------------------------------------------------------
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/MapWithStateRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/MapWithStateRDD.scala
new file mode 100644
index 0000000..ed95171
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/MapWithStateRDD.scala
@@ -0,0 +1,223 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.rdd
+
+import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
+
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+
+import org.apache.spark.rdd.{MapPartitionsRDD, RDD}
+import org.apache.spark.streaming.{Time, StateImpl, State}
+import org.apache.spark.streaming.util.{EmptyStateMap, StateMap}
+import org.apache.spark.util.Utils
+import org.apache.spark._
+
+/**
+ * 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 MapWithStateRDDRecord[K, S, E](
+ var stateMap: StateMap[K, S], var mappedData: Seq[E])
+
+private[streaming] object MapWithStateRDDRecord {
+ def updateRecordWithData[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
+ prevRecord: Option[MapWithStateRDDRecord[K, S, E]],
+ dataIterator: Iterator[(K, V)],
+ mappingFunction: (Time, K, Option[V], State[S]) => Option[E],
+ batchTime: Time,
+ timeoutThresholdTime: Option[Long],
+ removeTimedoutData: Boolean
+ ): 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 mappedData = new ArrayBuffer[E]
+ val wrappedState = new StateImpl[S]()
+
+ // 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 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)
+ }
+ mappedData ++= returned
+ }
+
+ // 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 returned = mappingFunction(batchTime, key, None, wrappedState)
+ mappedData ++= returned
+ newStateMap.remove(key)
+ }
+ }
+
+ MapWithStateRDDRecord(newStateMap, mappedData)
+ }
+}
+
+/**
+ * Partition of the [[MapWithStateRDD]], which depends on corresponding partitions of prev state
+ * RDD, and a partitioned keyed-data RDD
+ */
+private[streaming] class MapWithStateRDDPartition(
+ idx: Int,
+ @transient private var prevStateRDD: RDD[_],
+ @transient private var partitionedDataRDD: RDD[_]) extends Partition {
+
+ private[rdd] var previousSessionRDDPartition: Partition = null
+ private[rdd] var partitionedDataRDDPartition: Partition = null
+
+ override def index: Int = idx
+ override def hashCode(): Int = idx
+
+ @throws(classOf[IOException])
+ private def writeObject(oos: ObjectOutputStream): Unit = Utils.tryOrIOException {
+ // Update the reference to parent split at the time of task serialization
+ previousSessionRDDPartition = prevStateRDD.partitions(index)
+ partitionedDataRDDPartition = partitionedDataRDD.partitions(index)
+ oos.defaultWriteObject()
+ }
+}
+
+
+/**
+ * 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 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 MapWithStateRDD[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
+ private var prevStateRDD: RDD[MapWithStateRDDRecord[K, S, E]],
+ private var partitionedDataRDD: RDD[(K, V)],
+ mappingFunction: (Time, K, Option[V], State[S]) => Option[E],
+ batchTime: Time,
+ timeoutThresholdTime: Option[Long]
+ ) extends RDD[MapWithStateRDDRecord[K, S, E]](
+ partitionedDataRDD.sparkContext,
+ List(
+ new OneToOneDependency[MapWithStateRDDRecord[K, S, E]](prevStateRDD),
+ new OneToOneDependency(partitionedDataRDD))
+ ) {
+
+ @volatile private var doFullScan = false
+
+ require(prevStateRDD.partitioner.nonEmpty)
+ require(partitionedDataRDD.partitioner == prevStateRDD.partitioner)
+
+ override val partitioner = prevStateRDD.partitioner
+
+ override def checkpoint(): Unit = {
+ super.checkpoint()
+ doFullScan = true
+ }
+
+ override def compute(
+ partition: Partition, context: TaskContext): Iterator[MapWithStateRDDRecord[K, S, E]] = {
+
+ 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 = MapWithStateRDDRecord.updateRecordWithData(
+ prevRecord,
+ dataIterator,
+ mappingFunction,
+ batchTime,
+ timeoutThresholdTime,
+ removeTimedoutData = doFullScan // remove timedout data only when full scan is enabled
+ )
+ Iterator(newRecord)
+ }
+
+ override protected def getPartitions: Array[Partition] = {
+ Array.tabulate(prevStateRDD.partitions.length) { i =>
+ new MapWithStateRDDPartition(i, prevStateRDD, partitionedDataRDD)}
+ }
+
+ override def clearDependencies(): Unit = {
+ super.clearDependencies()
+ prevStateRDD = null
+ partitionedDataRDD = null
+ }
+
+ def setFullScan(): Unit = {
+ doFullScan = true
+ }
+}
+
+private[streaming] object MapWithStateRDD {
+
+ def createFromPairRDD[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
+ pairRDD: RDD[(K, S)],
+ partitioner: Partitioner,
+ updateTime: Time): MapWithStateRDD[K, V, S, E] = {
+
+ 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(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 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): MapWithStateRDD[K, V, S, E] = {
+
+ val pairRDD = rdd.map { x => (x._1, (x._2, x._3)) }
+ 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(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 MapWithStateRDD[K, V, S, E](
+ stateRDD, emptyDataRDD, noOpFunc, updateTime, None)
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/main/scala/org/apache/spark/streaming/rdd/TrackStateRDD.scala
----------------------------------------------------------------------
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/TrackStateRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/TrackStateRDD.scala
deleted file mode 100644
index 30aafcf..0000000
--- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/TrackStateRDD.scala
+++ /dev/null
@@ -1,228 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.streaming.rdd
-
-import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
-
-import scala.collection.mutable.ArrayBuffer
-import scala.reflect.ClassTag
-
-import org.apache.spark.rdd.{MapPartitionsRDD, RDD}
-import org.apache.spark.streaming.{Time, StateImpl, State}
-import org.apache.spark.streaming.util.{EmptyStateMap, StateMap}
-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`.
- */
-private[streaming] case class TrackStateRDDRecord[K, S, E](
- var stateMap: StateMap[K, S], var emittedRecords: Seq[E])
-
-private[streaming] object TrackStateRDDRecord {
- def updateRecordWithData[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
- prevRecord: Option[TrackStateRDDRecord[K, S, E]],
- dataIterator: Iterator[(K, V)],
- updateFunction: (Time, K, Option[V], State[S]) => Option[E],
- batchTime: Time,
- timeoutThresholdTime: Option[Long],
- removeTimedoutData: Boolean
- ): TrackStateRDDRecord[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 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
- dataIterator.foreach { case (key, value) =>
- wrappedState.wrap(newStateMap.get(key))
- val emittedRecord = updateFunction(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
- }
-
- // Get the timed out state records, call the tracking 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
- newStateMap.remove(key)
- }
- }
-
- TrackStateRDDRecord(newStateMap, emittedRecords)
- }
-}
-
-/**
- * Partition of the [[TrackStateRDD]], which depends on corresponding partitions of prev state
- * RDD, and a partitioned keyed-data RDD
- */
-private[streaming] class TrackStateRDDPartition(
- idx: Int,
- @transient private var prevStateRDD: RDD[_],
- @transient private var partitionedDataRDD: RDD[_]) extends Partition {
-
- private[rdd] var previousSessionRDDPartition: Partition = null
- private[rdd] var partitionedDataRDDPartition: Partition = null
-
- override def index: Int = idx
- override def hashCode(): Int = idx
-
- @throws(classOf[IOException])
- private def writeObject(oos: ObjectOutputStream): Unit = Utils.tryOrIOException {
- // Update the reference to parent split at the time of task serialization
- previousSessionRDDPartition = prevStateRDD.partitions(index)
- partitionedDataRDDPartition = partitionedDataRDD.partitions(index)
- oos.defaultWriteObject()
- }
-}
-
-
-/**
- * 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
- * @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 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 var partitionedDataRDD: RDD[(K, V)],
- trackingFunction: (Time, K, Option[V], State[S]) => Option[E],
- batchTime: Time,
- timeoutThresholdTime: Option[Long]
- ) extends RDD[TrackStateRDDRecord[K, S, E]](
- partitionedDataRDD.sparkContext,
- List(
- new OneToOneDependency[TrackStateRDDRecord[K, S, E]](prevStateRDD),
- new OneToOneDependency(partitionedDataRDD))
- ) {
-
- @volatile private var doFullScan = false
-
- require(prevStateRDD.partitioner.nonEmpty)
- require(partitionedDataRDD.partitioner == prevStateRDD.partitioner)
-
- override val partitioner = prevStateRDD.partitioner
-
- override def checkpoint(): Unit = {
- super.checkpoint()
- doFullScan = true
- }
-
- override def compute(
- partition: Partition, context: TaskContext): Iterator[TrackStateRDDRecord[K, S, E]] = {
-
- val stateRDDPartition = partition.asInstanceOf[TrackStateRDDPartition]
- 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(
- prevRecord,
- dataIterator,
- trackingFunction,
- batchTime,
- timeoutThresholdTime,
- removeTimedoutData = doFullScan // remove timedout data only when full scan is enabled
- )
- Iterator(newRecord)
- }
-
- override protected def getPartitions: Array[Partition] = {
- Array.tabulate(prevStateRDD.partitions.length) { i =>
- new TrackStateRDDPartition(i, prevStateRDD, partitionedDataRDD)}
- }
-
- override def clearDependencies(): Unit = {
- super.clearDependencies()
- prevStateRDD = null
- partitionedDataRDD = null
- }
-
- def setFullScan(): Unit = {
- doFullScan = true
- }
-}
-
-private[streaming] object TrackStateRDD {
-
- def createFromPairRDD[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
- pairRDD: RDD[(K, S)],
- partitioner: Partitioner,
- updateTime: Time): TrackStateRDD[K, V, S, E] = {
-
- val rddOfTrackStateRecords = 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]))
- }, 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)
- }
-
- def createFromRDD[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
- rdd: RDD[(K, S, Long)],
- partitioner: Partitioner,
- updateTime: Time): TrackStateRDD[K, V, S, E] = {
-
- val pairRDD = rdd.map { x => (x._1, (x._2, x._3)) }
- val rddOfTrackStateRecords = 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]))
- }, 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 }
- }
-}
http://git-wip-us.apache.org/repos/asf/spark/blob/bd2cd4f5/streaming/src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java
----------------------------------------------------------------------
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java
new file mode 100644
index 0000000..bc4bc2e
--- /dev/null
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java
@@ -0,0 +1,210 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming;
+
+import java.io.Serializable;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+import java.util.Set;
+
+import scala.Tuple2;
+
+import com.google.common.base.Optional;
+import com.google.common.collect.Lists;
+import com.google.common.collect.Sets;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.streaming.api.java.JavaDStream;
+import org.apache.spark.util.ManualClock;
+import org.junit.Assert;
+import org.junit.Test;
+
+import org.apache.spark.HashPartitioner;
+import org.apache.spark.api.java.JavaPairRDD;
+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.JavaMapWithStateDStream;
+
+public class JavaMapWithStateSuite extends LocalJavaStreamingContext implements Serializable {
+
+ /**
+ * This test is only for testing the APIs. It's not necessary to run it.
+ */
+ public void testAPI() {
+ JavaPairRDD<String, Boolean> initialRDD = null;
+ JavaPairDStream<String, Integer> wordsDstream = null;
+
+ final Function4<Time, String, Optional<Integer>, State<Boolean>, Optional<Double>>
+ mappingFunc =
+ new Function4<Time, String, Optional<Integer>, State<Boolean>, Optional<Double>>() {
+
+ @Override
+ public Optional<Double> call(
+ Time time, String word, Optional<Integer> one, State<Boolean> state) {
+ // Use all State's methods here
+ state.exists();
+ state.get();
+ state.isTimingOut();
+ state.remove();
+ state.update(true);
+ return Optional.of(2.0);
+ }
+ };
+
+ 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> stateSnapshots = stateDstream.stateSnapshots();
+
+ final Function3<String, Optional<Integer>, State<Boolean>, Double> mappingFunc2 =
+ new Function3<String, Optional<Integer>, State<Boolean>, Double>() {
+
+ @Override
+ public Double call(String key, Optional<Integer> one, State<Boolean> state) {
+ // Use all State's methods here
+ state.exists();
+ state.get();
+ state.isTimingOut();
+ state.remove();
+ state.update(true);
+ return 2.0;
+ }
+ };
+
+ 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> stateSnapshots2 = stateDstream2.stateSnapshots();
+ }
+
+ @Test
+ public void testBasicFunction() {
+ List<List<String>> inputData = Arrays.asList(
+ Collections.<String>emptyList(),
+ Arrays.asList("a"),
+ Arrays.asList("a", "b"),
+ Arrays.asList("a", "b", "c"),
+ Arrays.asList("a", "b"),
+ Arrays.asList("a"),
+ Collections.<String>emptyList()
+ );
+
+ List<Set<Integer>> outputData = Arrays.asList(
+ Collections.<Integer>emptySet(),
+ Sets.newHashSet(1),
+ Sets.newHashSet(2, 1),
+ Sets.newHashSet(3, 2, 1),
+ Sets.newHashSet(4, 3),
+ Sets.newHashSet(5),
+ Collections.<Integer>emptySet()
+ );
+
+ List<Set<Tuple2<String, Integer>>> stateData = Arrays.asList(
+ Collections.<Tuple2<String, Integer>>emptySet(),
+ Sets.newHashSet(new Tuple2<String, Integer>("a", 1)),
+ Sets.newHashSet(new Tuple2<String, Integer>("a", 2), new Tuple2<String, Integer>("b", 1)),
+ Sets.newHashSet(
+ new Tuple2<String, Integer>("a", 3),
+ new Tuple2<String, Integer>("b", 2),
+ new Tuple2<String, Integer>("c", 1)),
+ Sets.newHashSet(
+ new Tuple2<String, Integer>("a", 4),
+ new Tuple2<String, Integer>("b", 3),
+ new Tuple2<String, Integer>("c", 1)),
+ Sets.newHashSet(
+ new Tuple2<String, Integer>("a", 5),
+ new Tuple2<String, Integer>("b", 3),
+ new Tuple2<String, Integer>("c", 1)),
+ Sets.newHashSet(
+ new Tuple2<String, Integer>("a", 5),
+ new Tuple2<String, Integer>("b", 3),
+ new Tuple2<String, Integer>("c", 1))
+ );
+
+ Function3<String, Optional<Integer>, State<Integer>, Integer> mappingFunc =
+ new Function3<String, Optional<Integer>, State<Integer>, Integer>() {
+
+ @Override
+ 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;
+ }
+ };
+ testOperation(
+ inputData,
+ StateSpec.<String, Integer, Integer, Integer>function(mappingFunc),
+ outputData,
+ stateData);
+ }
+
+ private <K, S, T> void testOperation(
+ List<List<K>> input,
+ 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);
+ 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);
+ }
+ })).mapWithState(mapWithStateSpec);
+
+ final List<Set<T>> collectedOutputs =
+ Collections.synchronizedList(Lists.<Set<T>>newArrayList());
+ mapWithStateDStream.foreachRDD(new Function<JavaRDD<T>, Void>() {
+ @Override
+ public Void call(JavaRDD<T> rdd) throws Exception {
+ collectedOutputs.add(Sets.newHashSet(rdd.collect()));
+ return null;
+ }
+ });
+ final List<Set<Tuple2<K, S>>> collectedStateSnapshots =
+ Collections.synchronizedList(Lists.<Set<Tuple2<K, S>>>newArrayList());
+ 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()));
+ return null;
+ }
+ });
+ BatchCounter batchCounter = new BatchCounter(ssc.ssc());
+ ssc.start();
+ ((ManualClock) ssc.ssc().scheduler().clock())
+ .advance(ssc.ssc().progressListener().batchDuration() * numBatches + 1);
+ batchCounter.waitUntilBatchesCompleted(numBatches, 10000);
+
+ Assert.assertEquals(expectedOutputs, collectedOutputs);
+ Assert.assertEquals(expectedStateSnapshots, collectedStateSnapshots);
+ }
+}
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