Trait

org.apache.spark.streaming.api.java

JavaDStreamLike

Related Doc: package java

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

Source
JavaDStreamLike.scala
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Serializable, Serializable, AnyRef, Any
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Abstract Value Members

  1. implicit abstract val classTag: ClassTag[T]

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  2. abstract def dstream: DStream[T]

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

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Concrete Value Members

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

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  2. final def ##(): Int

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

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  4. final def asInstanceOf[T0]: T0

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

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    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

  6. def clone(): AnyRef

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    @throws( ... )
  7. def context(): StreamingContext

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

  8. def count(): JavaDStream[Long]

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

  9. def countByValue(numPartitions: Int): JavaPairDStream[T, Long]

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    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.

  10. def countByValue(): JavaPairDStream[T, Long]

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    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.

  11. def countByValueAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int): JavaPairDStream[T, Long]

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    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.

  12. def countByValueAndWindow(windowDuration: Duration, slideDuration: Duration): JavaPairDStream[T, Long]

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    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

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

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    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()

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

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  15. def equals(arg0: Any): Boolean

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  16. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )
  17. def flatMap[U](f: FlatMapFunction[T, U]): JavaDStream[U]

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

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

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

  19. def foreachRDD(foreachFunc: VoidFunction2[R, Time]): Unit

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    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.

  20. def foreachRDD(foreachFunc: VoidFunction[R]): Unit

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    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.

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

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

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    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.

  23. def hashCode(): Int

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  24. final def isInstanceOf[T0]: Boolean

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

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

  26. def mapPartitions[U](f: FlatMapFunction[Iterator[T], U]): JavaDStream[U]

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    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.

  27. def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[T], K2, V2]): JavaPairDStream[K2, V2]

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    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.

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

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

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

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  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

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  32. def print(num: Int): Unit

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    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.

  33. def print(): Unit

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    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.

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

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

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

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    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

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

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    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

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

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

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

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

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  40. def toString(): String

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  41. def transform[U](transformFunc: Function2[R, Time, JavaRDD[U]]): JavaDStream[U]

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

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

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

  43. def transformToPair[K2, V2](transformFunc: Function2[R, Time, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2]

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

  44. def transformToPair[K2, V2](transformFunc: Function[R, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2]

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

  45. def transformWith[K2, V2, W](other: JavaPairDStream[K2, V2], transformFunc: Function3[R, JavaPairRDD[K2, V2], Time, JavaRDD[W]]): JavaDStream[W]

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

  46. def transformWith[U, W](other: JavaDStream[U], transformFunc: Function3[R, JavaRDD[U], Time, JavaRDD[W]]): JavaDStream[W]

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

  47. def transformWithToPair[K2, V2, K3, V3](other: JavaPairDStream[K2, V2], transformFunc: Function3[R, JavaPairRDD[K2, V2], Time, JavaPairRDD[K3, V3]]): JavaPairDStream[K3, V3]

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    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 transformWithToPair[U, K2, V2](other: JavaDStream[U], transformFunc: Function3[R, JavaRDD[U], Time, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2]

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

  49. final def wait(): Unit

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

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

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