Packages

class JavaDStream[T] extends AbstractJavaDStreamLike[T, JavaDStream[T], JavaRDD[T]]

A Java-friendly interface to org.apache.spark.streaming.dstream.DStream, the basic abstraction in Spark Streaming that represents a continuous stream of data. DStreams can either be created from live data (such as, data from TCP sockets, Kafka, etc.) or it can be generated by transforming existing DStreams using operations such as map, window. For operations applicable to key-value pair DStreams, see org.apache.spark.streaming.api.java.JavaPairDStream.

Source
JavaDStream.scala
Linear Supertypes
AbstractJavaDStreamLike[T, JavaDStream[T], JavaRDD[T]], JavaDStreamLike[T, JavaDStream[T], JavaRDD[T]], Serializable, Serializable, AnyRef, Any
Known Subclasses
JavaInputDStream, JavaMapWithStateDStream, JavaReceiverInputDStream
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Inherited
  1. JavaDStream
  2. AbstractJavaDStreamLike
  3. JavaDStreamLike
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new JavaDStream(dstream: DStream[T])(implicit classTag: ClassTag[T])

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(): JavaDStream[T]

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

  6. 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

    Definition Classes
    JavaDStreamLike
  7. implicit val classTag: ClassTag[T]
    Definition Classes
    JavaDStream → JavaDStreamLike
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  9. def compute(validTime: Time): JavaRDD[T]

    Generate an RDD for the given duration

  10. 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
  11. 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
  12. 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.

    Definition Classes
    JavaDStreamLike
  13. 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.

    Definition Classes
    JavaDStreamLike
  14. 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.

    Definition Classes
    JavaDStreamLike
  15. 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

    Definition Classes
    JavaDStreamLike
  16. 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
  17. val dstream: DStream[T]
    Definition Classes
    JavaDStream → JavaDStreamLike
  18. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  20. def filter(f: Function[T, Boolean]): JavaDStream[T]

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

  21. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. 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

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

    Definition Classes
    JavaDStreamLike
  23. 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

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

    Definition Classes
    JavaDStreamLike
  24. def foreachRDD(foreachFunc: VoidFunction2[JavaRDD[T], 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
  25. def foreachRDD(foreachFunc: VoidFunction[JavaRDD[T]]): 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
  26. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  27. 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.

    Definition Classes
    JavaDStreamLike
  28. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  29. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  30. def map[U](f: Function[T, 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
  31. 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.

    Definition Classes
    JavaDStreamLike
  32. 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.

    Definition Classes
    JavaDStreamLike
  33. 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.

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

    Definition Classes
    JavaDStreamLike
  34. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  35. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  36. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  37. def persist(storageLevel: StorageLevel): JavaDStream[T]

    Persist the RDDs of this DStream with the given storage level

  38. def persist(): JavaDStream[T]

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

  39. 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
  40. 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
  41. 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.

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

    Definition Classes
    JavaDStreamLike
  42. 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 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
  43. 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 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
  44. def repartition(numPartitions: Int): JavaDStream[T]

    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.

  45. implicit def scalaIntToJavaLong(in: DStream[Long]): JavaDStream[Long]
    Definition Classes
    JavaDStreamLike
  46. def slice(fromTime: Time, toTime: Time): List[JavaRDD[T]]

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

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

    Definition Classes
    JavaDStreamLike
  47. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  48. def toString(): String
    Definition Classes
    AnyRef → Any
  49. def transform[U](transformFunc: Function2[JavaRDD[T], 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
  50. def transform[U](transformFunc: Function[JavaRDD[T], 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
  51. def transformToPair[K2, V2](transformFunc: Function2[JavaRDD[T], 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
  52. def transformToPair[K2, V2](transformFunc: Function[JavaRDD[T], 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
  53. def transformWith[K2, V2, W](other: JavaPairDStream[K2, V2], transformFunc: Function3[JavaRDD[T], 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
  54. def transformWith[U, W](other: JavaDStream[U], transformFunc: Function3[JavaRDD[T], 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
  55. def transformWithToPair[K2, V2, K3, V3](other: JavaPairDStream[K2, V2], transformFunc: Function3[JavaRDD[T], 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
  56. def transformWithToPair[U, K2, V2](other: JavaDStream[U], transformFunc: Function3[JavaRDD[T], 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
  57. def union(that: JavaDStream[T]): JavaDStream[T]

    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.

  58. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  59. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  60. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  61. def window(windowDuration: Duration, slideDuration: Duration): JavaDStream[T]

    Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.

    Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.

    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

  62. def window(windowDuration: Duration): JavaDStream[T]

    Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.

    Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over 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.

  63. def wrapRDD(rdd: RDD[T]): JavaRDD[T]
    Definition Classes
    JavaDStream → JavaDStreamLike

Inherited from AbstractJavaDStreamLike[T, JavaDStream[T], JavaRDD[T]]

Inherited from JavaDStreamLike[T, JavaDStream[T], JavaRDD[T]]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped