org.apache.spark.streaming.dstream

DStream

abstract class DStream[T] extends Serializable with Logging

A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous sequence of RDDs (of the same type) representing a continuous stream of data (see org.apache.spark.rdd.RDD in the Spark core documentation for more details on RDDs). DStreams can either be created from live data (such as, data from TCP sockets, Kafka, Flume, etc.) using a org.apache.spark.streaming.StreamingContext or it can be generated by transforming existing DStreams using operations such as map, window and reduceByKeyAndWindow. While a Spark Streaming program is running, each DStream periodically generates a RDD, either from live data or by transforming the RDD generated by a parent DStream.

This class contains the basic operations available on all DStreams, such as map, filter and window. In addition, org.apache.spark.streaming.dstream.PairDStreamFunctions contains operations available only on DStreams of key-value pairs, such as groupByKeyAndWindow and join. These operations are automatically available on any DStream of pairs (e.g., DStream[(Int, Int)] through implicit conversions when org.apache.spark.streaming.StreamingContext._ is imported.

DStreams internally is characterized by a few basic properties:

Linear Supertypes
Logging, Serializable, Serializable, AnyRef, Any
Known Subclasses
ConstantInputDStream, InputDStream, ReceiverInputDStream
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  1. DStream
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Instance Constructors

  1. new DStream(ssc: StreamingContext)(implicit arg0: ClassTag[T])

Abstract Value Members

  1. abstract def compute(validTime: Time): Option[RDD[T]]

    Method that generates a RDD for the given time

  2. abstract def dependencies: List[DStream[_]]

    List of parent DStreams on which this DStream depends on

  3. abstract def slideDuration: Duration

    Time interval after which the DStream generates a RDD

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)

  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

  9. def clone(): AnyRef

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

  10. def context: StreamingContext

    Return the StreamingContext associated with this DStream

  11. def count(): DStream[Long]

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

  12. 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).

  13. 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.

  14. 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

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

    Definition Classes
    AnyRef

  16. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  17. def filter(filterFunc: (T) ⇒ Boolean): DStream[T]

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

  18. def finalize(): Unit

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

  19. 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

  20. 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.

  21. 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.

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

    Definition Classes
    AnyRef → Any

  23. 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.

  24. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  25. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  26. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging

  27. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging

  28. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  29. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  30. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  31. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  32. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  33. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  34. def logName: String

    Attributes
    protected
    Definition Classes
    Logging

  35. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  36. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  37. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  38. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  39. 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.

  40. 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.

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

    Definition Classes
    AnyRef

  42. final def notify(): Unit

    Definition Classes
    AnyRef

  43. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  44. def persist(): DStream[T]

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

  45. def persist(level: StorageLevel): DStream[T]

    Persist the RDDs of this DStream with the given storage level

  46. 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.

  47. 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.

  48. 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

  49. 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

  50. 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.

  51. 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".

  52. 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".

  53. def slice(fromTime: Time, toTime: Time): Seq[RDD[T]]

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

  54. def slice(interval: Interval): Seq[RDD[T]]

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

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

    Definition Classes
    AnyRef

  56. def toString(): String

    Definition Classes
    AnyRef → Any

  57. 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.

  58. 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.

  59. 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.

  60. 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.

  61. 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.

  62. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  65. 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

  66. 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.

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.

    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.

    Annotations
    @deprecated
    Deprecated

    (Since version 0.9.0) use foreachRDD

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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