Package org.apache.spark.streaming.api.java

Class JavaPairDStream<K,V>

Object
org.apache.spark.streaming.api.java.JavaPairDStream<K,V>
All Implemented Interfaces:
Serializable, JavaDStreamLike<scala.Tuple2<K,V>,JavaPairDStream<K,V>,JavaPairRDD<K,V>>, scala.Serializable
Direct Known Subclasses:
JavaPairInputDStream
public class JavaPairDStream<K,V> extends Object
A Java-friendly interface to a DStream of key-value pairs, which provides extra methods like reduceByKey and join.
See Also:

  • Constructor Details

    • JavaPairDStream

      public JavaPairDStream(DStream<scala.Tuple2<K,V>> dstream, scala.reflect.ClassTag<K> kManifest, scala.reflect.ClassTag<V> vManifest)

  • Method Details

    • fromPairDStream

      public static <K, V> JavaPairDStream<K,V> fromPairDStream(DStream<scala.Tuple2<K,V>> dstream, scala.reflect.ClassTag<K> evidence$1, scala.reflect.ClassTag<V> evidence$2)

    • fromJavaDStream

      public static <K, V> JavaPairDStream<K,V> fromJavaDStream(JavaDStream<scala.Tuple2<K,V>> dstream)

    • scalaToJavaLong

      public static <K> JavaPairDStream<K,Long> scalaToJavaLong(JavaPairDStream<K,Object> dstream, scala.reflect.ClassTag<K> evidence$3)

    • dstream

      public DStream<scala.Tuple2<K,V>> dstream()

    • kManifest

      public scala.reflect.ClassTag<K> kManifest()

    • vManifest

      public scala.reflect.ClassTag<V> vManifest()

    • wrapRDD

      public JavaPairRDD<K,V> wrapRDD(RDD<scala.Tuple2<K,V>> rdd)

    • filter

      public JavaPairDStream<K,V> filter(Function<scala.Tuple2<K,V>,Boolean> f)
      Return a new DStream containing only the elements that satisfy a predicate.

    • cache

      public JavaPairDStream<K,V> cache()
      Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)

    • persist

      public JavaPairDStream<K,V> persist()
      Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)

    • persist

      public JavaPairDStream<K,V> persist(StorageLevel storageLevel)
      Persist the RDDs of this DStream with the given storage level

    • repartition

      public JavaPairDStream<K,V> repartition(int numPartitions)
      Return a new DStream with an increased or decreased level of parallelism. Each RDD in the returned DStream has exactly numPartitions partitions.
      Parameters:
      numPartitions - (undocumented)
      Returns:
      (undocumented)

    • compute

      public JavaPairRDD<K,V> compute(Time validTime)
      Method that generates an RDD for the given Duration

    • window

      public JavaPairDStream<K,V> window(Duration windowDuration)
      Return a new DStream which is computed based on windowed batches of this DStream. The new DStream generates RDDs with the same interval as this DStream.
      Parameters:
      windowDuration - width of the window; must be a multiple of this DStream's interval.
      Returns:

    • window

      public JavaPairDStream<K,V> window(Duration windowDuration, Duration slideDuration)
      Return a new DStream which is computed based on windowed batches of this DStream.
      Parameters:
      windowDuration - duration (i.e., width) of the window; must be a multiple of this DStream's interval
      slideDuration - sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's interval
      Returns:
      (undocumented)

    • union

      public JavaPairDStream<K,V> union(JavaPairDStream<K,V> that)
      Return a new DStream by unifying data of another DStream with this DStream.
      Parameters:
      that - Another DStream having the same interval (i.e., slideDuration) as this DStream.
      Returns:
      (undocumented)

    • groupByKey

      public JavaPairDStream<K,Iterable<V>> groupByKey()
      Return a new DStream by applying groupByKey to each RDD. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Returns:
      (undocumented)

    • groupByKey

      public JavaPairDStream<K,Iterable<V>> groupByKey(int numPartitions)
      Return a new DStream by applying groupByKey to each RDD. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      numPartitions - (undocumented)
      Returns:
      (undocumented)

    • groupByKey

      public JavaPairDStream<K,Iterable<V>> groupByKey(Partitioner partitioner)
      Return a new DStream by applying groupByKey on each RDD of this DStream. Therefore, the values for each key in this DStream's RDDs are grouped into a single sequence to generate the RDDs of the new DStream. org.apache.spark.Partitioner is used to control the partitioning of each RDD.
      Parameters:
      partitioner - (undocumented)
      Returns:
      (undocumented)

    • reduceByKey

      public JavaPairDStream<K,V> reduceByKey(Function2<V,V,V> func)
      Return a new DStream by applying reduceByKey to each RDD. The values for each key are merged using the associative and commutative reduce function. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      func - (undocumented)
      Returns:
      (undocumented)

    • reduceByKey

      public JavaPairDStream<K,V> reduceByKey(Function2<V,V,V> func, int numPartitions)
      Return a new DStream by applying reduceByKey to each RDD. The values for each key are merged using the supplied reduce function. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      func - (undocumented)
      numPartitions - (undocumented)
      Returns:
      (undocumented)

    • reduceByKey

      public JavaPairDStream<K,V> reduceByKey(Function2<V,V,V> func, Partitioner partitioner)
      Return a new DStream by applying reduceByKey to each RDD. The values for each key are merged using the supplied reduce function. org.apache.spark.Partitioner is used to control the partitioning of each RDD.
      Parameters:
      func - (undocumented)
      partitioner - (undocumented)
      Returns:
      (undocumented)

    • combineByKey

      public <C> JavaPairDStream<K,C> combineByKey(Function<V,C> createCombiner, Function2<C,V,C> mergeValue, Function2<C,C,C> mergeCombiners, Partitioner partitioner)
      Combine elements of each key in DStream's RDDs using custom function. This is similar to the combineByKey for RDDs. Please refer to combineByKey in org.apache.spark.rdd.PairRDDFunctions for more information.
      Parameters:
      createCombiner - (undocumented)
      mergeValue - (undocumented)
      mergeCombiners - (undocumented)
      partitioner - (undocumented)
      Returns:
      (undocumented)

    • combineByKey

      public <C> JavaPairDStream<K,C> combineByKey(Function<V,C> createCombiner, Function2<C,V,C> mergeValue, Function2<C,C,C> mergeCombiners, Partitioner partitioner, boolean mapSideCombine)
      Combine elements of each key in DStream's RDDs using custom function. This is similar to the combineByKey for RDDs. Please refer to combineByKey in org.apache.spark.rdd.PairRDDFunctions for more information.
      Parameters:
      createCombiner - (undocumented)
      mergeValue - (undocumented)
      mergeCombiners - (undocumented)
      partitioner - (undocumented)
      mapSideCombine - (undocumented)
      Returns:
      (undocumented)

    • groupByKeyAndWindow

      public JavaPairDStream<K,Iterable<V>> groupByKeyAndWindow(Duration windowDuration)
      Return a new DStream by applying groupByKey over a sliding window. This is similar to DStream.groupByKey() but applies it over a sliding window. The new DStream generates RDDs with the same interval as this DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      Returns:
      (undocumented)

    • groupByKeyAndWindow

      public JavaPairDStream<K,Iterable<V>> groupByKeyAndWindow(Duration windowDuration, Duration slideDuration)
      Return a new DStream by applying groupByKey over a sliding window. Similar to DStream.groupByKey(), but applies it over a sliding window. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      slideDuration - sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
      Returns:
      (undocumented)

    • groupByKeyAndWindow

      public JavaPairDStream<K,Iterable<V>> groupByKeyAndWindow(Duration windowDuration, Duration slideDuration, int numPartitions)
      Return a new DStream by applying groupByKey over a sliding window on this DStream. Similar to DStream.groupByKey(), but applies it over a sliding window. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      slideDuration - sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
      numPartitions - Number of partitions of each RDD in the new DStream.
      Returns:
      (undocumented)

    • groupByKeyAndWindow

      public JavaPairDStream<K,Iterable<V>> groupByKeyAndWindow(Duration windowDuration, Duration slideDuration, Partitioner partitioner)
      Return a new DStream by applying groupByKey over a sliding window on this DStream. Similar to DStream.groupByKey(), but applies it over a sliding window.
      Parameters:
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      slideDuration - sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
      partitioner - Partitioner for controlling the partitioning of each RDD in the new DStream.
      Returns:
      (undocumented)

    • reduceByKeyAndWindow

      public JavaPairDStream<K,V> reduceByKeyAndWindow(Function2<V,V,V> reduceFunc, Duration windowDuration)
      Create a new DStream by applying reduceByKey over a sliding window on this DStream. Similar to DStream.reduceByKey(), but applies it over a sliding window. The new DStream generates RDDs with the same interval as this DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      reduceFunc - associative and commutative reduce function
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      Returns:
      (undocumented)

    • reduceByKeyAndWindow

      public JavaPairDStream<K,V> reduceByKeyAndWindow(Function2<V,V,V> reduceFunc, Duration windowDuration, Duration slideDuration)
      Return a new DStream by applying reduceByKey over a sliding window. This is similar to DStream.reduceByKey() but applies it over a sliding window. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      reduceFunc - associative and commutative reduce function
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      slideDuration - sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
      Returns:
      (undocumented)

    • reduceByKeyAndWindow

      public JavaPairDStream<K,V> reduceByKeyAndWindow(Function2<V,V,V> reduceFunc, Duration windowDuration, Duration slideDuration, int numPartitions)
      Return a new DStream by applying reduceByKey over a sliding window. This is similar to DStream.reduceByKey() but applies it over a sliding window. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      reduceFunc - associative and commutative reduce function
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      slideDuration - sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
      numPartitions - Number of partitions of each RDD in the new DStream.
      Returns:
      (undocumented)

    • reduceByKeyAndWindow

      public JavaPairDStream<K,V> reduceByKeyAndWindow(Function2<V,V,V> reduceFunc, Duration windowDuration, Duration slideDuration, Partitioner partitioner)
      Return a new DStream by applying reduceByKey over a sliding window. Similar to DStream.reduceByKey(), but applies it over a sliding window.
      Parameters:
      reduceFunc - associative rand commutative educe function
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      slideDuration - sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
      partitioner - Partitioner for controlling the partitioning of each RDD in the new DStream.
      Returns:
      (undocumented)

    • reduceByKeyAndWindow

      public JavaPairDStream<K,V> reduceByKeyAndWindow(Function2<V,V,V> reduceFunc, Function2<V,V,V> invReduceFunc, Duration windowDuration, Duration slideDuration)
      Return a new DStream by reducing over a using incremental computation. The reduced value of over a new window is calculated using the old window's reduce value : 1. reduce the new values that entered the window (e.g., adding new counts) 2. "inverse reduce" the old values that left the window (e.g., subtracting old counts) This is more efficient that reduceByKeyAndWindow without "inverse reduce" function. However, it is applicable to only "invertible reduce functions". Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      reduceFunc - associative and commutative reduce function
      invReduceFunc - inverse function; such that for all y, invertible x: invReduceFunc(reduceFunc(x, y), x) = y
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      slideDuration - sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
      Returns:
      (undocumented)

    • reduceByKeyAndWindow

      public JavaPairDStream<K,V> reduceByKeyAndWindow(Function2<V,V,V> reduceFunc, Function2<V,V,V> invReduceFunc, Duration windowDuration, Duration slideDuration, int numPartitions, Function<scala.Tuple2<K,V>,Boolean> filterFunc)
      Return a new DStream by applying incremental reduceByKey over a sliding window. The reduced value of over a new window is calculated using the old window's reduce value : 1. reduce the new values that entered the window (e.g., adding new counts) 2. "inverse reduce" the old values that left the window (e.g., subtracting old counts) This is more efficient that reduceByKeyAndWindow without "inverse reduce" function. However, it is applicable to only "invertible reduce functions". Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      reduceFunc - associative and commutative reduce function
      invReduceFunc - inverse function
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      slideDuration - sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
      numPartitions - number of partitions of each RDD in the new DStream.
      filterFunc - function to filter expired key-value pairs; only pairs that satisfy the function are retained set this to null if you do not want to filter
      Returns:
      (undocumented)

    • reduceByKeyAndWindow

      public JavaPairDStream<K,V> reduceByKeyAndWindow(Function2<V,V,V> reduceFunc, Function2<V,V,V> invReduceFunc, Duration windowDuration, Duration slideDuration, Partitioner partitioner, Function<scala.Tuple2<K,V>,Boolean> filterFunc)
      Return a new DStream by applying incremental reduceByKey over a sliding window. The reduced value of over a new window is calculated using the old window's reduce value : 1. reduce the new values that entered the window (e.g., adding new counts) 2. "inverse reduce" the old values that left the window (e.g., subtracting old counts) This is more efficient that reduceByKeyAndWindow without "inverse reduce" function. However, it is applicable to only "invertible reduce functions".
      Parameters:
      reduceFunc - associative and commutative reduce function
      invReduceFunc - inverse function
      windowDuration - width of the window; must be a multiple of this DStream's batching interval
      slideDuration - sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
      partitioner - Partitioner for controlling the partitioning of each RDD in the new DStream.
      filterFunc - function to filter expired key-value pairs; only pairs that satisfy the function are retained set this to null if you do not want to filter
      Returns:
      (undocumented)

    • mapWithState

      public <StateType, MappedType> JavaMapWithStateDStream<K,V,StateType,MappedType> mapWithState(StateSpec<K,V,StateType,MappedType> spec)
      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.

      Example of using mapWithState: 

      
   // 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
           }
       };

    JavaMapWithStateDStream<String, Integer, Integer, String> mapWithStateDStream =
        keyValueDStream.mapWithState(StateSpec.function(mappingFunc));

      Parameters:
      spec - Specification of this transformation
      Returns:
      (undocumented)

    • updateStateByKey

      public <S> JavaPairDStream<K,S> updateStateByKey(Function2<List<V>,Optional<S>,Optional<S>> updateFunc)
      Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      updateFunc - State update function. If this function returns None, then corresponding state key-value pair will be eliminated.
      Returns:
      (undocumented)

    • updateStateByKey

      public <S> JavaPairDStream<K,S> updateStateByKey(Function2<List<V>,Optional<S>,Optional<S>> updateFunc, int numPartitions)
      Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      updateFunc - State update function. If this function returns None, then corresponding state key-value pair will be eliminated.
      numPartitions - Number of partitions of each RDD in the new DStream.
      Returns:
      (undocumented)

    • updateStateByKey

      public <S> JavaPairDStream<K,S> updateStateByKey(Function2<List<V>,Optional<S>,Optional<S>> updateFunc, Partitioner partitioner)
      Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of the key. org.apache.spark.Partitioner is used to control the partitioning of each RDD.
      Parameters:
      updateFunc - State update function. If this function returns None, then corresponding state key-value pair will be eliminated.
      partitioner - Partitioner for controlling the partitioning of each RDD in the new DStream.
      Returns:
      (undocumented)

    • updateStateByKey

      public <S> JavaPairDStream<K,S> updateStateByKey(Function2<List<V>,Optional<S>,Optional<S>> updateFunc, Partitioner partitioner, JavaPairRDD<K,S> initialRDD)
      Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of the key. org.apache.spark.Partitioner is used to control the partitioning of each RDD.
      Parameters:
      updateFunc - State update function. If this function returns None, then corresponding state key-value pair will be eliminated.
      partitioner - Partitioner for controlling the partitioning of each RDD in the new DStream.
      initialRDD - initial state value of each key.
      Returns:
      (undocumented)

    • mapValues

      public <U> JavaPairDStream<K,U> mapValues(Function<V,U> f)
      Return a new DStream by applying a map function to the value of each key-value pairs in 'this' DStream without changing the key.
      Parameters:
      f - (undocumented)
      Returns:
      (undocumented)

    • flatMapValues

      public <U> JavaPairDStream<K,U> flatMapValues(FlatMapFunction<V,U> f)
      Return a new DStream by applying a flatmap function to the value of each key-value pairs in 'this' DStream without changing the key.
      Parameters:
      f - (undocumented)
      Returns:
      (undocumented)

    • cogroup

      public <W> JavaPairDStream<K,scala.Tuple2<Iterable<V>,Iterable<W>>> cogroup(JavaPairDStream<K,W> other)
      Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      other - (undocumented)
      Returns:
      (undocumented)

    • cogroup

      public <W> JavaPairDStream<K,scala.Tuple2<Iterable<V>,Iterable<W>>> cogroup(JavaPairDStream<K,W> other, int numPartitions)
      Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      other - (undocumented)
      numPartitions - (undocumented)
      Returns:
      (undocumented)

    • cogroup

      public <W> JavaPairDStream<K,scala.Tuple2<Iterable<V>,Iterable<W>>> cogroup(JavaPairDStream<K,W> other, Partitioner partitioner)
      Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      other - (undocumented)
      partitioner - (undocumented)
      Returns:
      (undocumented)

    • join

      public <W> JavaPairDStream<K,scala.Tuple2<V,W>> join(JavaPairDStream<K,W> other)
      Return a new DStream by applying 'join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      other - (undocumented)
      Returns:
      (undocumented)

    • join

      public <W> JavaPairDStream<K,scala.Tuple2<V,W>> join(JavaPairDStream<K,W> other, int numPartitions)
      Return a new DStream by applying 'join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      other - (undocumented)
      numPartitions - (undocumented)
      Returns:
      (undocumented)

    • join

      public <W> JavaPairDStream<K,scala.Tuple2<V,W>> join(JavaPairDStream<K,W> other, Partitioner partitioner)
      Return a new DStream by applying 'join' between RDDs of this DStream and other DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.
      Parameters:
      other - (undocumented)
      partitioner - (undocumented)
      Returns:
      (undocumented)

    • leftOuterJoin

      public <W> JavaPairDStream<K,scala.Tuple2<V,Optional<W>>> leftOuterJoin(JavaPairDStream<K,W> other)
      Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      other - (undocumented)
      Returns:
      (undocumented)

    • leftOuterJoin

      public <W> JavaPairDStream<K,scala.Tuple2<V,Optional<W>>> leftOuterJoin(JavaPairDStream<K,W> other, int numPartitions)
      Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      other - (undocumented)
      numPartitions - (undocumented)
      Returns:
      (undocumented)

    • leftOuterJoin

      public <W> JavaPairDStream<K,scala.Tuple2<V,Optional<W>>> leftOuterJoin(JavaPairDStream<K,W> other, Partitioner partitioner)
      Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.
      Parameters:
      other - (undocumented)
      partitioner - (undocumented)
      Returns:
      (undocumented)

    • rightOuterJoin

      public <W> JavaPairDStream<K,scala.Tuple2<Optional<V>,W>> rightOuterJoin(JavaPairDStream<K,W> other)
      Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      other - (undocumented)
      Returns:
      (undocumented)

    • rightOuterJoin

      public <W> JavaPairDStream<K,scala.Tuple2<Optional<V>,W>> rightOuterJoin(JavaPairDStream<K,W> other, int numPartitions)
      Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      other - (undocumented)
      numPartitions - (undocumented)
      Returns:
      (undocumented)

    • rightOuterJoin

      public <W> JavaPairDStream<K,scala.Tuple2<Optional<V>,W>> rightOuterJoin(JavaPairDStream<K,W> other, Partitioner partitioner)
      Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.
      Parameters:
      other - (undocumented)
      partitioner - (undocumented)
      Returns:
      (undocumented)

    • fullOuterJoin

      public <W> JavaPairDStream<K,scala.Tuple2<Optional<V>,Optional<W>>> fullOuterJoin(JavaPairDStream<K,W> other)
      Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
      Parameters:
      other - (undocumented)
      Returns:
      (undocumented)

    • fullOuterJoin

      public <W> JavaPairDStream<K,scala.Tuple2<Optional<V>,Optional<W>>> fullOuterJoin(JavaPairDStream<K,W> other, int numPartitions)
      Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.
      Parameters:
      other - (undocumented)
      numPartitions - (undocumented)
      Returns:
      (undocumented)

    • fullOuterJoin

      public <W> JavaPairDStream<K,scala.Tuple2<Optional<V>,Optional<W>>> fullOuterJoin(JavaPairDStream<K,W> other, Partitioner partitioner)
      Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.
      Parameters:
      other - (undocumented)
      partitioner - (undocumented)
      Returns:
      (undocumented)

    • saveAsHadoopFiles

      public void saveAsHadoopFiles(String prefix, String suffix)
      Save each RDD in this DStream as a Hadoop file. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS.suffix".
      Parameters:
      prefix - (undocumented)
      suffix - (undocumented)

    • saveAsHadoopFiles

      public <F extends org.apache.hadoop.mapred.OutputFormat<?, ?>> void saveAsHadoopFiles(String prefix, String suffix, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass)
      Save each RDD in this DStream as a Hadoop file. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS.suffix".
      Parameters:
      prefix - (undocumented)
      suffix - (undocumented)
      keyClass - (undocumented)
      valueClass - (undocumented)
      outputFormatClass - (undocumented)

    • saveAsHadoopFiles

      public <F extends org.apache.hadoop.mapred.OutputFormat<?, ?>> void saveAsHadoopFiles(String prefix, String suffix, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, org.apache.hadoop.mapred.JobConf conf)
      Save each RDD in this DStream as a Hadoop file. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS.suffix".
      Parameters:
      prefix - (undocumented)
      suffix - (undocumented)
      keyClass - (undocumented)
      valueClass - (undocumented)
      outputFormatClass - (undocumented)
      conf - (undocumented)

    • saveAsNewAPIHadoopFiles

      public void saveAsNewAPIHadoopFiles(String prefix, String suffix)
      Save each RDD in this DStream as a Hadoop file. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS.suffix".
      Parameters:
      prefix - (undocumented)
      suffix - (undocumented)

    • saveAsNewAPIHadoopFiles

      public <F extends org.apache.hadoop.mapreduce.OutputFormat<?, ?>> void saveAsNewAPIHadoopFiles(String prefix, String suffix, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass)
      Save each RDD in this DStream as a Hadoop file. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS.suffix".
      Parameters:
      prefix - (undocumented)
      suffix - (undocumented)
      keyClass - (undocumented)
      valueClass - (undocumented)
      outputFormatClass - (undocumented)

    • saveAsNewAPIHadoopFiles

      public <F extends org.apache.hadoop.mapreduce.OutputFormat<?, ?>> void saveAsNewAPIHadoopFiles(String prefix, String suffix, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, org.apache.hadoop.conf.Configuration conf)
      Save each RDD in this DStream as a Hadoop file. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS.suffix".
      Parameters:
      prefix - (undocumented)
      suffix - (undocumented)
      keyClass - (undocumented)
      valueClass - (undocumented)
      outputFormatClass - (undocumented)
      conf - (undocumented)

    • toJavaDStream

      public JavaDStream<scala.Tuple2<K,V>> toJavaDStream()
      Convert to a JavaDStream

    • classTag

      public scala.reflect.ClassTag<scala.Tuple2<K,V>> classTag()