org.apache.spark.streaming.api.java

JavaDStreamLike

trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T, R]] extends Serializable

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Serializable, Serializable, AnyRef, Any
Known Subclasses
JavaDStream, JavaInputDStream, JavaPairDStream, JavaPairInputDStream, JavaPairReceiverInputDStream, JavaReceiverInputDStream
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  1. JavaDStreamLike
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Abstract Value Members

  1. implicit abstract val classTag: ClassTag[T]

  2. abstract def dstream: DStream[T]

  3. abstract def wrapRDD(in: RDD[T]): R

Concrete Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def checkpoint(interval: Duration): DStream[T]

    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

  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  9. def context(): StreamingContext

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

  10. def count(): JavaDStream[Long]

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

  11. def countByValue(numPartitions: Int): JavaPairDStream[T, 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.

  12. def countByValue(): JavaPairDStream[T, 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.

  13. def countByValueAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int): JavaPairDStream[T, 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.

  14. def countByValueAndWindow(windowDuration: Duration, slideDuration: Duration): JavaPairDStream[T, 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

  15. 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()

  16. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  17. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  18. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  19. def flatMap[U](f: FlatMapFunction[T, U]): JavaDStream[U]

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

  20. def flatMapToPair[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairDStream[K2, V2]

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

  21. def foreachRDD(foreachFunc: Function2[R, Time, Void]): 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.

  22. def foreachRDD(foreachFunc: Function[R, Void]): 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.

  23. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  24. def glom(): JavaDStream[List[T]]

    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.

  25. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  26. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  27. def map[R](f: Function[T, R]): JavaDStream[R]

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

  28. def mapPartitions[U](f: FlatMapFunction[Iterator[T], 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.

  29. def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[T], 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.

  30. def mapToPair[K2, V2](f: PairFunction[T, K2, V2]): JavaPairDStream[K2, V2]

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

  31. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  32. final def notify(): Unit

    Definition Classes
    AnyRef

  33. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  34. 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.

  35. 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.

  36. def reduce(f: Function2[T, T, T]): JavaDStream[T]

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

  37. def reduceByWindow(reduceFunc: Function2[T, T, T], invReduceFunc: Function2[T, T, T], windowDuration: Duration, slideDuration: Duration): JavaDStream[T]

    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 reduce function

    invReduceFunc

    inverse 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

  38. def reduceByWindow(reduceFunc: Function2[T, T, T], windowDuration: Duration, slideDuration: Duration): JavaDStream[T]

    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 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

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

  40. def slice(fromTime: Time, toTime: Time): List[R]

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

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

    Definition Classes
    AnyRef

  42. def toString(): String

    Definition Classes
    AnyRef → Any

  43. def transform[U](transformFunc: Function2[R, 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.

  44. def transform[U](transformFunc: Function[R, JavaRDD[U]]): JavaDStream[U]

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

  45. def transformToPair[K2, V2](transformFunc: Function2[R, 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.

  46. def transformToPair[K2, V2](transformFunc: Function[R, 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.

  47. def transformWith[K2, V2, W](other: JavaPairDStream[K2, V2], transformFunc: Function3[R, 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.

  48. def transformWith[U, W](other: JavaDStream[U], transformFunc: Function3[R, 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.

  49. def transformWithToPair[K2, V2, K3, V3](other: JavaPairDStream[K2, V2], transformFunc: Function3[R, 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.

  50. def transformWithToPair[U, K2, V2](other: JavaDStream[U], transformFunc: Function3[R, 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.

  51. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  53. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def foreach(foreachFunc: Function2[R, Time, Void]): 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.

    Annotations
    @Deprecated
    Deprecated

    As of release 0.9.0, replaced by foreachRDD

  2. def foreach(foreachFunc: Function[R, Void]): 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.

    Annotations
    @Deprecated
    Deprecated

    As of release 0.9.0, replaced by foreachRDD

  3. def reduceByWindow(reduceFunc: (T, T) ⇒ T, windowDuration: Duration, slideDuration: Duration): DStream[T]

    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 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

    Annotations
    @deprecated
    Deprecated

    (Since version 1.3.0) Use Java-compatible version of reduceByWindow

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