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abstract class InputDStream[T] extends DStream[T]

This is the abstract base class for all input streams. This class provides methods start() and stop() which are called by Spark Streaming system to start and stop receiving data, respectively. Input streams that can generate RDDs from new data by running a service/thread only on the driver node (that is, without running a receiver on worker nodes), can be implemented by directly inheriting this InputDStream. For example, FileInputDStream, a subclass of InputDStream, monitors a HDFS directory from the driver for new files and generates RDDs with the new files. For implementing input streams that requires running a receiver on the worker nodes, use org.apache.spark.streaming.dstream.ReceiverInputDStream as the parent class.

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

  1. new InputDStream(_ssc: StreamingContext)(implicit arg0: ClassTag[T])

    _ssc

    Streaming context that will execute this input stream

Abstract Value Members

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

    Method that generates an RDD for the given time

    Method that generates an RDD for the given time

    Definition Classes
    DStream
  2. abstract def start(): Unit

    Method called to start receiving data.

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

  3. abstract def stop(): Unit

    Method called to stop receiving data.

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

Concrete 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. val baseScope: Option[String]

    The base scope associated with the operation that created this DStream.

    The base scope associated with the operation that created this DStream.

    For InputDStreams, we use the name of this DStream as the scope name. If an outer scope is given, we assume that it includes an alternative name for this stream.

    Attributes
    protected[org.apache.spark.streaming]
    Definition Classes
    InputDStream → DStream
  6. 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
  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

    Definition Classes
    DStream
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  9. def context: StreamingContext

    Return the StreamingContext associated with this DStream

    Return the StreamingContext associated with this DStream

    Definition Classes
    DStream
  10. 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
  11. 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
  12. 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
  13. 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
  14. def createRDDWithLocalProperties[U](time: Time, displayInnerRDDOps: Boolean)(body: ⇒ U): U

    Wrap a body of code such that the call site and operation scope information are passed to the RDDs created in this body properly.

    Wrap a body of code such that the call site and operation scope information are passed to the RDDs created in this body properly.

    time

    Current batch time that should be embedded in the scope names

    displayInnerRDDOps

    Whether the detailed callsites and scopes of the inner RDDs generated by body will be displayed in the UI; only the scope and callsite of the DStream operation that generated this will be displayed.

    body

    RDD creation code to execute with certain local properties.

    Attributes
    protected[org.apache.spark.streaming]
    Definition Classes
    DStream
  15. def dependencies: List[DStream[_]]

    List of parent DStreams on which this DStream depends on

    List of parent DStreams on which this DStream depends on

    Definition Classes
    InputDStream → DStream
  16. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  18. 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
  19. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  20. def flatMap[U](flatMapFunc: (T) ⇒ TraversableOnce[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
  21. 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
  22. 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
  23. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  24. 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
  25. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  26. val id: Int

    This is an unique identifier for the input stream.

  27. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  28. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  29. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  30. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  31. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  32. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  33. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  34. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  35. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  36. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  37. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  38. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  39. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  40. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  41. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  42. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  43. 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
  44. 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
  45. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  46. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  47. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  48. 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
  49. 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
  50. 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
    DStream
  51. 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
  52. val rateController: Option[RateController]
    Attributes
    protected[org.apache.spark.streaming]
  53. 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
  54. 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 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
    DStream
  55. 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 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
    DStream
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. def slideDuration: Duration

    Time interval after which the DStream generates an RDD

    Time interval after which the DStream generates an RDD

    Definition Classes
    InputDStream → DStream
  62. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  63. def toString(): String
    Definition Classes
    AnyRef → Any
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  70. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  71. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  72. 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
  73. 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

Inherited from DStream[T]

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped