org.apache.spark.streaming.dstream

ReceiverInputDStream

abstract class ReceiverInputDStream[T] extends InputDStream[T]

Abstract class for defining any org.apache.spark.streaming.dstream.InputDStream that has to start a receiver on worker nodes to receive external data. Specific implementations of NetworkInputDStream must define the getReceiver() function that gets the receiver object of type org.apache.spark.streaming.receiver.Receiver that will be sent to the workers to receive data.

T

Class type of the object of this stream

Linear Supertypes
InputDStream[T], DStream[T], Logging, Serializable, Serializable, AnyRef, Any
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Inherited
  1. ReceiverInputDStream
  2. InputDStream
  3. DStream
  4. Logging
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
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  1. Public
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Instance Constructors

  1. new ReceiverInputDStream(ssc_: StreamingContext)(implicit arg0: ClassTag[T])

    ssc_

    Streaming context that will execute this input stream

Abstract Value Members

  1. abstract def getReceiver(): Receiver[T]

    Gets the receiver object that will be sent to the worker nodes to receive data.

    Gets the receiver object that will be sent to the worker nodes to receive data. This method needs to defined by any specific implementation of a NetworkInputDStream.

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 cache(): DStream[T]

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

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

    Definition Classes
    DStream
  8. 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
    DStream
  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. def compute(validTime: Time): Option[RDD[T]]

    Ask ReceiverInputTracker for received data blocks and generates RDDs with them.

    Ask ReceiverInputTracker for received data blocks and generates RDDs with them.

    Definition Classes
    ReceiverInputDStreamDStream
  11. def context: StreamingContext

    Return the StreamingContext associated with this DStream

    Return the StreamingContext associated with this DStream

    Definition Classes
    DStream
  12. def count(): DStream[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
    DStream
  13. def countByValue(numPartitions: Int = ssc.sc.defaultParallelism)(implicit ord: Ordering[T] = null): DStream[(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 (Spark's default number of partitions if numPartitions not specified).

    Definition Classes
    DStream
  14. def countByValueAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int = ssc.sc.defaultParallelism)(implicit ord: Ordering[T] = null): DStream[(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 (Spark's default number of partitions if numPartitions not specified).

    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
    DStream
  15. def countByWindow(windowDuration: Duration, slideDuration: Duration): DStream[Long]

    Return a new DStream in which each RDD has a single element generated by counting the number of elements in a sliding 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 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
    DStream
  16. def dependencies: List[Nothing]

    List of parent DStreams on which this DStream depends on

    List of parent DStreams on which this DStream depends on

    Definition Classes
    InputDStreamDStream
  17. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  18. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  19. def filter(filterFunc: (T) ⇒ Boolean): DStream[T]

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

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

    Definition Classes
    DStream
  20. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  21. def flatMap[U](flatMapFunc: (T) ⇒ Traversable[U])(implicit arg0: ClassTag[U]): DStream[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
    DStream
  22. def foreachRDD(foreachFunc: (RDD[T], Time) ⇒ Unit): 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
    DStream
  23. def foreachRDD(foreachFunc: (RDD[T]) ⇒ Unit): 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
    DStream
  24. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  25. def glom(): DStream[Array[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
    DStream
  26. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  27. val id: Int

    This is an unique identifier for the network input stream.

  28. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  29. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  30. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  31. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  32. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  33. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  34. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  35. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  36. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  37. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  38. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  39. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  40. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  41. def map[U](mapFunc: (T) ⇒ U)(implicit arg0: ClassTag[U]): DStream[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
    DStream
  42. def mapPartitions[U](mapPartFunc: (Iterator[T]) ⇒ Iterator[U], preservePartitioning: Boolean = false)(implicit arg0: ClassTag[U]): DStream[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
    DStream
  43. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  44. final def notify(): Unit

    Definition Classes
    AnyRef
  45. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  46. def persist(): DStream[T]

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

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

    Definition Classes
    DStream
  47. def persist(level: StorageLevel): DStream[T]

    Persist the RDDs of this DStream with the given storage level

    Persist the RDDs of this DStream with the given storage level

    Definition Classes
    DStream
  48. 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
    DStream
  49. def reduce(reduceFunc: (T, T) ⇒ T): DStream[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
    DStream
  50. def reduceByWindow(reduceFunc: (T, T) ⇒ T, invReduceFunc: (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. 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

    Definition Classes
    DStream
  51. 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

    Definition Classes
    DStream
  52. def repartition(numPartitions: Int): DStream[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.

    Definition Classes
    DStream
  53. def saveAsObjectFiles(prefix: String, suffix: String = ""): Unit

    Save each RDD in this DStream as a Sequence file of serialized objects.

    Save each RDD in this DStream as a Sequence file of serialized objects. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS.suffix".

    Definition Classes
    DStream
  54. def saveAsTextFiles(prefix: String, suffix: String = ""): Unit

    Save each RDD in this DStream as at text file, using string representation of elements.

    Save each RDD in this DStream as at text file, using string representation of elements. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS.suffix".

    Definition Classes
    DStream
  55. def slice(fromTime: Time, toTime: Time): Seq[RDD[T]]

    Return all the RDDs between 'fromTime' to 'toTime' (both included)

    Return all the RDDs between 'fromTime' to 'toTime' (both included)

    Definition Classes
    DStream
  56. def slice(interval: Interval): Seq[RDD[T]]

    Return all the RDDs defined by the Interval object (both end times included)

    Return all the RDDs defined by the Interval object (both end times included)

    Definition Classes
    DStream
  57. def slideDuration: Duration

    Time interval after which the DStream generates a RDD

    Time interval after which the DStream generates a RDD

    Definition Classes
    InputDStreamDStream
  58. def start(): Unit

    Method called to start receiving data.

    Method called to start receiving data. Subclasses must implement this method.

    Definition Classes
    ReceiverInputDStreamInputDStream
  59. def stop(): Unit

    Method called to stop receiving data.

    Method called to stop receiving data. Subclasses must implement this method.

    Definition Classes
    ReceiverInputDStreamInputDStream
  60. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  61. def toString(): String

    Definition Classes
    AnyRef → Any
  62. def transform[U](transformFunc: (RDD[T], Time) ⇒ RDD[U])(implicit arg0: ClassTag[U]): DStream[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
    DStream
  63. def transform[U](transformFunc: (RDD[T]) ⇒ RDD[U])(implicit arg0: ClassTag[U]): DStream[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
    DStream
  64. def transformWith[U, V](other: DStream[U], transformFunc: (RDD[T], RDD[U], Time) ⇒ RDD[V])(implicit arg0: ClassTag[U], arg1: ClassTag[V]): DStream[V]

    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
    DStream
  65. def transformWith[U, V](other: DStream[U], transformFunc: (RDD[T], RDD[U]) ⇒ RDD[V])(implicit arg0: ClassTag[U], arg1: ClassTag[V]): DStream[V]

    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
    DStream
  66. def union(that: DStream[T]): DStream[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 slideDuration as this DStream.

    Definition Classes
    DStream
  67. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  68. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  69. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  70. def window(windowDuration: Duration, slideDuration: Duration): DStream[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

    Definition Classes
    DStream
  71. def window(windowDuration: Duration): DStream[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.

    Definition Classes
    DStream

Deprecated Value Members

  1. def foreach(foreachFunc: (RDD[T], Time) ⇒ Unit): 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
    DStream
    Annotations
    @deprecated
    Deprecated

    (Since version 0.9.0) use foreachRDD

  2. def foreach(foreachFunc: (RDD[T]) ⇒ Unit): 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
    DStream
    Annotations
    @deprecated
    Deprecated

    (Since version 0.9.0) use foreachRDD

Inherited from InputDStream[T]

Inherited from DStream[T]

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped