Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

4.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

5.  def cache(): JavaPairDStream[K, V]

   Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)

6.  def checkpoint(interval: Duration): DStream[(K, V)]

   Enable periodic checkpointing of RDDs of this DStream.

   Enable periodic checkpointing of RDDs of this DStream.

   interval

   Time interval after which generated RDD will be checkpointed

   Definition Classes

   JavaDStreamLike

7.  val classTag: ClassTag[(K, V)]

   Definition Classes

   JavaPairDStream → JavaDStreamLike

8.  def clone(): AnyRef

   Attributes

   protected[lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... ) @native()

9.  def cogroup[W](other: JavaPairDStream[K, W], partitioner: Partitioner): JavaPairDStream[K, (Iterable[V], Iterable[W])]

   Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.

   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.

10.  def cogroup[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (Iterable[V], Iterable[W])]

    Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.

    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.

11.  def cogroup[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (Iterable[V], Iterable[W])]

    Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.

    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.

12.  def combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C], partitioner: Partitioner, mapSideCombine: Boolean): JavaPairDStream[K, C]

    Combine elements of each key in DStream's RDDs using custom function.

    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.

13.  def combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C], partitioner: Partitioner): JavaPairDStream[K, C]

    Combine elements of each key in DStream's RDDs using custom function.

    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.

14.  def compute(validTime: Time): JavaPairRDD[K, V]

    Method that generates an RDD for the given Duration

15.  def context(): StreamingContext

    Return the org.apache.spark.streaming.StreamingContext associated with this DStream

    Return the org.apache.spark.streaming.StreamingContext associated with this DStream

    Definition Classes

    JavaDStreamLike

16.  def count(): JavaDStream[Long]

    Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.

    Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.

    Definition Classes

    JavaDStreamLike

17.  def countByValue(numPartitions: Int): JavaPairDStream[(K, V), Long]

    Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.

    Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.

    numPartitions

    number of partitions of each RDD in the new DStream.

    Definition Classes

    JavaDStreamLike

18.  def countByValue(): JavaPairDStream[(K, V), Long]

    Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.

    Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.

    Definition Classes

    JavaDStreamLike

19.  def countByValueAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int): JavaPairDStream[(K, V), Long]

    Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.

    Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.

    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.

    Definition Classes

    JavaDStreamLike

20.  def countByValueAndWindow(windowDuration: Duration, slideDuration: Duration): JavaPairDStream[(K, V), Long]

    Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.

    Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.

    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

    Definition Classes

    JavaDStreamLike

21.  def countByWindow(windowDuration: Duration, slideDuration: Duration): JavaDStream[Long]

    Return a new DStream in which each RDD has a single element generated by counting the number of elements in a window over this DStream.

    Return a new DStream in which each RDD has a single element generated by counting the number of elements in a window over this DStream. windowDuration and slideDuration are as defined in the window() operation. This is equivalent to window(windowDuration, slideDuration).count()

    Definition Classes

    JavaDStreamLike

22.  val dstream: DStream[(K, V)]

    Definition Classes

    JavaPairDStream → JavaDStreamLike

23.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

24.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

25.  def filter(f: Function[(K, V), Boolean]): JavaPairDStream[K, V]

    Return a new DStream containing only the elements that satisfy a predicate.

26.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

27.  def flatMap[U](f: FlatMapFunction[(K, V), U]): JavaDStream[U]

    Return a new DStream by applying a function to all elements of this DStream, and then flattening the results

    Return a new DStream by applying a function to all elements of this DStream, and then flattening the results

    Definition Classes

    JavaDStreamLike

28.  def flatMapToPair[K2, V2](f: PairFlatMapFunction[(K, V), K2, V2]): JavaPairDStream[K2, V2]

    Return a new DStream by applying a function to all elements of this DStream, and then flattening the results

    Return a new DStream by applying a function to all elements of this DStream, and then flattening the results

    Definition Classes

    JavaDStreamLike

29.  def flatMapValues[U](f: FlatMapFunction[V, U]): JavaPairDStream[K, U]

    Return a new DStream by applying a flatmap function to the value of each key-value pairs in 'this' DStream without changing the key.

30.  def foreachRDD(foreachFunc: VoidFunction2[JavaPairRDD[K, V], Time]): Unit

    Apply a function to each RDD in this DStream.

    Apply a function to each RDD in this DStream. This is an output operator, so 'this' DStream will be registered as an output stream and therefore materialized.

    Definition Classes

    JavaDStreamLike

31.  def foreachRDD(foreachFunc: VoidFunction[JavaPairRDD[K, V]]): Unit

    Apply a function to each RDD in this DStream.

    Apply a function to each RDD in this DStream. This is an output operator, so 'this' DStream will be registered as an output stream and therefore materialized.

    Definition Classes

    JavaDStreamLike

32.  def fullOuterJoin[W](other: JavaPairDStream[K, W], partitioner: Partitioner): JavaPairDStream[K, (Optional[V], Optional[W])]

    Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream.

    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.

33.  def fullOuterJoin[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (Optional[V], Optional[W])]

    Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream.

    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.

34.  def fullOuterJoin[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (Optional[V], Optional[W])]

    Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream.

    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.

35.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

36.  def glom(): JavaDStream[List[(K, V)]]

    Return a new DStream in which each RDD is generated by applying glom() to each RDD of this DStream.

    Return a new DStream in which each RDD is generated by applying glom() to each RDD of this DStream. Applying glom() to an RDD coalesces all elements within each partition into an array.

    Definition Classes

    JavaDStreamLike

37.  def groupByKey(partitioner: Partitioner): JavaPairDStream[K, Iterable[V]]

    Return a new DStream by applying groupByKey on each RDD of this DStream.

    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.

38.  def groupByKey(numPartitions: Int): JavaPairDStream[K, Iterable[V]]

    Return a new DStream by applying groupByKey to each RDD.

    Return a new DStream by applying groupByKey to each RDD. Hash partitioning is used to generate the RDDs with numPartitions partitions.

39.  def groupByKey(): JavaPairDStream[K, Iterable[V]]

    Return a new DStream by applying groupByKey to each RDD.

    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.

40.  def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner): JavaPairDStream[K, Iterable[V]]

    Return a new DStream by applying groupByKey over a sliding window on this DStream.

    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.

    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.

41.  def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int): JavaPairDStream[K, Iterable[V]]

    Return a new DStream by applying groupByKey over a sliding window on this DStream.

    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.

    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.

42.  def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration): JavaPairDStream[K, Iterable[V]]

    Return a new DStream by applying groupByKey over a sliding window.

    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.

    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

43.  def groupByKeyAndWindow(windowDuration: Duration): JavaPairDStream[K, Iterable[V]]

    Return a new DStream by applying groupByKey over a sliding window.

    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.

    windowDuration

    width of the window; must be a multiple of this DStream's batching interval

44.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

45.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

46.  def join[W](other: JavaPairDStream[K, W], partitioner: Partitioner): JavaPairDStream[K, (V, W)]

    Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.

    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.

47.  def join[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (V, W)]

    Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.

    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.

48.  def join[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (V, W)]

    Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.

    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.

49.  implicit val kManifest: ClassTag[K]
50.  def leftOuterJoin[W](other: JavaPairDStream[K, W], partitioner: Partitioner): JavaPairDStream[K, (V, Optional[W])]

    Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.

    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.

51.  def leftOuterJoin[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (V, Optional[W])]

    Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.

    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.

52.  def leftOuterJoin[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (V, Optional[W])]

    Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.

    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.

53.  def map[U](f: Function[(K, V), U]): JavaDStream[U]

    Return a new DStream by applying a function to all elements of this DStream.

    Return a new DStream by applying a function to all elements of this DStream.

    Definition Classes

    JavaDStreamLike

54.  def mapPartitions[U](f: FlatMapFunction[Iterator[(K, V)], U]): JavaDStream[U]

    Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.

    Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream. Applying mapPartitions() to an RDD applies a function to each partition of the RDD.

    Definition Classes

    JavaDStreamLike

55.  def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[(K, V)], K2, V2]): JavaPairDStream[K2, V2]

    Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.

    Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream. Applying mapPartitions() to an RDD applies a function to each partition of the RDD.

    Definition Classes

    JavaDStreamLike

56.  def mapToPair[K2, V2](f: PairFunction[(K, V), K2, V2]): JavaPairDStream[K2, V2]

    Return a new DStream by applying a function to all elements of this DStream.

    Return a new DStream by applying a function to all elements of this DStream.

    Definition Classes

    JavaDStreamLike

57.  def mapValues[U](f: Function[V, U]): JavaPairDStream[K, U]

    Return a new DStream by applying a map function to the value of each key-value pairs in 'this' DStream without changing the key.

58.  def mapWithState[StateType, MappedType](spec: StateSpec[K, V, StateType, MappedType]): JavaMapWithStateDStream[K, V, StateType, MappedType]

    Return a JavaMapWithStateDStream by applying a function to every key-value element of this stream, while maintaining some state data for each unique key.

    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));

    StateType

    Class type of the state data

    MappedType

    Class type of the mapped data

    spec

    Specification of this transformation

59.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

60.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

61.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

62.  def persist(storageLevel: StorageLevel): JavaPairDStream[K, V]

    Persist the RDDs of this DStream with the given storage level

63.  def persist(): JavaPairDStream[K, V]

    Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)

64.  def print(num: Int): Unit

    Print the first num elements of each RDD generated in this DStream.

    Print the first num elements of each RDD generated in this DStream. This is an output operator, so this DStream will be registered as an output stream and there materialized.

    Definition Classes

    JavaDStreamLike

65.  def print(): Unit

    Print the first ten elements of each RDD generated in this DStream.

    Print the first ten elements of each RDD generated in this DStream. This is an output operator, so this DStream will be registered as an output stream and there materialized.

    Definition Classes

    JavaDStreamLike

66.  def reduce(f: Function2[(K, V), (K, V), (K, V)]): JavaDStream[(K, V)]

    Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream.

    Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream.

    Definition Classes

    JavaDStreamLike

67.  def reduceByKey(func: Function2[V, V, V], partitioner: Partitioner): JavaPairDStream[K, V]

    Return a new DStream by applying reduceByKey to each RDD.

    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.

68.  def reduceByKey(func: Function2[V, V, V], numPartitions: Int): JavaPairDStream[K, V]

    Return a new DStream by applying reduceByKey to each RDD.

    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.

69.  def reduceByKey(func: Function2[V, V, V]): JavaPairDStream[K, V]

    Return a new DStream by applying reduceByKey to each RDD.

    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.

70.  def reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], invReduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner, filterFunc: Function[(K, V), Boolean]): JavaPairDStream[K, V]

    Return a new DStream by applying incremental reduceByKey over a sliding window.

    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".

    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

71.  def reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], invReduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration, numPartitions: Int, filterFunc: Function[(K, V), Boolean]): JavaPairDStream[K, V]

    Return a new DStream by applying incremental reduceByKey over a sliding window.

    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.

    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

72.  def reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], invReduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration): JavaPairDStream[K, V]

    Return a new DStream by reducing over a using incremental computation.

    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.

    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

73.  def reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner): JavaPairDStream[K, V]

    Return a new DStream by applying reduceByKey over a sliding window.

    Return a new DStream by applying reduceByKey over a sliding window. Similar to DStream.reduceByKey(), but applies it over a sliding window.

    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.

74.  def reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration, numPartitions: Int): JavaPairDStream[K, V]

    Return a new DStream by applying reduceByKey over a sliding window.

    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.

    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.

75.  def reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration): JavaPairDStream[K, V]

    Return a new DStream by applying reduceByKey over a sliding window.

    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.

    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

76.  def reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], windowDuration: Duration): JavaPairDStream[K, V]

    Create a new DStream by applying reduceByKey over a sliding window on this DStream.

    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.

    reduceFunc

    associative and commutative reduce function

    windowDuration

    width of the window; must be a multiple of this DStream's batching interval

77.  def reduceByWindow(reduceFunc: Function2[(K, V), (K, V), (K, V)], invReduceFunc: Function2[(K, V), (K, V), (K, V)], windowDuration: Duration, slideDuration: Duration): JavaDStream[(K, V)]

    Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.

    Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream. However, the reduction is done incrementally using the old window's reduced 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 than reduceByWindow without "inverse reduce" function. However, it is applicable to only "invertible reduce functions".

    reduceFunc

    associative and commutative reduce function

    invReduceFunc

    inverse reduce 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

    Definition Classes

    JavaDStreamLike

78.  def reduceByWindow(reduceFunc: Function2[(K, V), (K, V), (K, V)], windowDuration: Duration, slideDuration: Duration): JavaDStream[(K, V)]

    Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.

    Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.

    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

    Definition Classes

    JavaDStreamLike

79.  def repartition(numPartitions: Int): JavaPairDStream[K, V]

    Return a new DStream with an increased or decreased level of parallelism.

    Return a new DStream with an increased or decreased level of parallelism. Each RDD in the returned DStream has exactly numPartitions partitions.

80.  def rightOuterJoin[W](other: JavaPairDStream[K, W], partitioner: Partitioner): JavaPairDStream[K, (Optional[V], W)]

    Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.

    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.

81.  def rightOuterJoin[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (Optional[V], W)]

    Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.

    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.

82.  def rightOuterJoin[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (Optional[V], W)]

    Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.

    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.

83.  def saveAsHadoopFiles[F <: OutputFormat[_, _]](prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], conf: JobConf): Unit

    Save each RDD in this DStream as a Hadoop file.

    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".

84.  def saveAsHadoopFiles[F <: OutputFormat[_, _]](prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F]): Unit

    Save each RDD in this DStream as a Hadoop file.

    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".

85.  def saveAsHadoopFiles(prefix: String, suffix: String): Unit

    Save each RDD in this DStream as a Hadoop file.

    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".

86.  def saveAsNewAPIHadoopFiles[F <: OutputFormat[_, _]](prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], conf: Configuration = ...): Unit

    Save each RDD in this DStream as a Hadoop file.

    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".

87.  def saveAsNewAPIHadoopFiles[F <: OutputFormat[_, _]](prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F]): Unit

    Save each RDD in this DStream as a Hadoop file.

    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".

88.  def saveAsNewAPIHadoopFiles(prefix: String, suffix: String): Unit

    Save each RDD in this DStream as a Hadoop file.

    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".

89.  implicit def scalaIntToJavaLong(in: DStream[Long]): JavaDStream[Long]

    Definition Classes

    JavaDStreamLike

90.  def slice(fromTime: Time, toTime: Time): List[JavaPairRDD[K, V]]

    Return all the RDDs between 'fromDuration' to 'toDuration' (both included)

    Return all the RDDs between 'fromDuration' to 'toDuration' (both included)

    Definition Classes

    JavaDStreamLike

91.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

92.  def toJavaDStream(): JavaDStream[(K, V)]

    Convert to a JavaDStream

93.  def toString(): String

    Definition Classes

    AnyRef → Any

94.  def transform[U](transformFunc: Function2[JavaPairRDD[K, V], Time, JavaRDD[U]]): JavaDStream[U]

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.

    Definition Classes

    JavaDStreamLike

95.  def transform[U](transformFunc: Function[JavaPairRDD[K, V], JavaRDD[U]]): JavaDStream[U]

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.

    Definition Classes

    JavaDStreamLike

96.  def transformToPair[K2, V2](transformFunc: Function2[JavaPairRDD[K, V], Time, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2]

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.

    Definition Classes

    JavaDStreamLike

97.  def transformToPair[K2, V2](transformFunc: Function[JavaPairRDD[K, V], JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2]

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.

    Definition Classes

    JavaDStreamLike

98.  def transformWith[K2, V2, W](other: JavaPairDStream[K2, V2], transformFunc: Function3[JavaPairRDD[K, V], JavaPairRDD[K2, V2], Time, JavaRDD[W]]): JavaDStream[W]

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.

    Definition Classes

    JavaDStreamLike

99.  def transformWith[U, W](other: JavaDStream[U], transformFunc: Function3[JavaPairRDD[K, V], JavaRDD[U], Time, JavaRDD[W]]): JavaDStream[W]

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.

    Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.

    Definition Classes

    JavaDStreamLike

100.  def transformWithToPair[K2, V2, K3, V3](other: JavaPairDStream[K2, V2], transformFunc: Function3[JavaPairRDD[K, V], JavaPairRDD[K2, V2], Time, JavaPairRDD[K3, V3]]): JavaPairDStream[K3, V3]

     Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.

     Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.

     Definition Classes

     JavaDStreamLike

101.  def transformWithToPair[U, K2, V2](other: JavaDStream[U], transformFunc: Function3[JavaPairRDD[K, V], JavaRDD[U], Time, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2]

     Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.

     Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.

     Definition Classes

     JavaDStreamLike

102.  def union(that: JavaPairDStream[K, V]): JavaPairDStream[K, V]

     Return a new DStream by unifying data of another DStream with this DStream.

     Return a new DStream by unifying data of another DStream with this DStream.

     that

     Another DStream having the same interval (i.e., slideDuration) as this DStream.

103.  def updateStateByKey[S](updateFunc: Function2[List[V], Optional[S], Optional[S]], partitioner: Partitioner, initialRDD: JavaPairRDD[K, S]): JavaPairDStream[K, S]

     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.

     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.

     S

     State type

     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.

104.  def updateStateByKey[S](updateFunc: Function2[List[V], Optional[S], Optional[S]], partitioner: Partitioner): JavaPairDStream[K, S]

     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.

     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.

     S

     State type

     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.

105.  def updateStateByKey[S](updateFunc: Function2[List[V], Optional[S], Optional[S]], numPartitions: Int): JavaPairDStream[K, S]

     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.

     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.

     S

     State type

     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.

106.  def updateStateByKey[S](updateFunc: Function2[List[V], Optional[S], Optional[S]]): JavaPairDStream[K, S]

     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.

     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.

     S

     State type

     updateFunc

     State update function. If this function returns None, then corresponding state key-value pair will be eliminated.

107.  implicit val vManifest: ClassTag[V]
108.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

109.  final def wait(arg0: Long, arg1: Int): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

110.  final def wait(arg0: Long): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... ) @native()

111.  def window(windowDuration: Duration, slideDuration: Duration): JavaPairDStream[K, V]

     Return a new DStream which is computed based on windowed batches of this DStream.

     Return a new DStream which is computed based on windowed batches of this DStream.

     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

112.  def window(windowDuration: Duration): JavaPairDStream[K, V]

     Return a new DStream which is computed based on windowed batches of this DStream.

     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.

     windowDuration

     width of the window; must be a multiple of this DStream's interval.

113.  def wrapRDD(rdd: RDD[(K, V)]): JavaPairRDD[K, V]

     Definition Classes

     JavaPairDStream → JavaDStreamLike