org.apache.spark.streaming

StreamingContext

class StreamingContext extends Logging

Main entry point for Spark Streaming functionality. It provides methods used to create org.apache.spark.streaming.dstream.DStreams from various input sources. It can be either created by providing a Spark master URL and an appName, or from a org.apache.spark.SparkConf configuration (see core Spark documentation), or from an existing org.apache.spark.SparkContext. The associated SparkContext can be accessed using context.sparkContext. After creating and transforming DStreams, the streaming computation can be started and stopped using context.start() and context.stop(), respectively. context.awaitTransformation() allows the current thread to wait for the termination of the context by stop() or by an exception.

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Instance Constructors

  1. new StreamingContext(path: String)

    Recreate a StreamingContext from a checkpoint file.

    Recreate a StreamingContext from a checkpoint file.

    path

    Path to the directory that was specified as the checkpoint directory

  2. new StreamingContext(path: String, hadoopConf: Configuration)

    Recreate a StreamingContext from a checkpoint file.

    Recreate a StreamingContext from a checkpoint file.

    path

    Path to the directory that was specified as the checkpoint directory

    hadoopConf

    Optional, configuration object if necessary for reading from HDFS compatible filesystems

  3. new StreamingContext(master: String, appName: String, batchDuration: Duration, sparkHome: String = null, jars: Seq[String] = Nil, environment: Map[String, String] = Map())

    Create a StreamingContext by providing the details necessary for creating a new SparkContext.

    Create a StreamingContext by providing the details necessary for creating a new SparkContext.

    master

    cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).

    appName

    a name for your job, to display on the cluster web UI

    batchDuration

    the time interval at which streaming data will be divided into batches

  4. new StreamingContext(conf: SparkConf, batchDuration: Duration)

    Create a StreamingContext by providing the configuration necessary for a new SparkContext.

    Create a StreamingContext by providing the configuration necessary for a new SparkContext.

    conf

    a org.apache.spark.SparkConf object specifying Spark parameters

    batchDuration

    the time interval at which streaming data will be divided into batches

  5. new StreamingContext(sparkContext: SparkContext, batchDuration: Duration)

    Create a StreamingContext using an existing SparkContext.

    Create a StreamingContext using an existing SparkContext.

    sparkContext

    existing SparkContext

    batchDuration

    the time interval at which streaming data will be divided into batches

Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. def actorStream[T](props: Props, name: String, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2, supervisorStrategy: SupervisorStrategy = ...)(implicit arg0: ClassTag[T]): ReceiverInputDStream[T]

    Create an input stream with any arbitrary user implemented actor receiver.

    Create an input stream with any arbitrary user implemented actor receiver. Find more details at: http://spark.apache.org/docs/latest/streaming-custom-receivers.html

    props

    Props object defining creation of the actor

    name

    Name of the actor

    storageLevel

    RDD storage level. Defaults to memory-only.

    Note

    An important point to note: Since Actor may exist outside the spark framework, It is thus user's responsibility to ensure the type safety, i.e parametrized type of data received and actorStream should be same.

  7. def addStreamingListener(streamingListener: StreamingListener): Unit

    Add a org.apache.spark.streaming.scheduler.StreamingListener object for receiving system events related to streaming.

  8. final def asInstanceOf[T0]: T0

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  9. def awaitTermination(timeout: Long): Unit

    Wait for the execution to stop.

    Wait for the execution to stop. Any exceptions that occurs during the execution will be thrown in this thread.

    timeout

    time to wait in milliseconds

  10. def awaitTermination(): Unit

    Wait for the execution to stop.

    Wait for the execution to stop. Any exceptions that occurs during the execution will be thrown in this thread.

  11. def checkpoint(directory: String): Unit

    Set the context to periodically checkpoint the DStream operations for driver fault-tolerance.

    Set the context to periodically checkpoint the DStream operations for driver fault-tolerance.

    directory

    HDFS-compatible directory where the checkpoint data will be reliably stored. Note that this must be a fault-tolerant file system like HDFS for

  12. def clone(): AnyRef

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

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  14. def equals(arg0: Any): Boolean

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  15. def fileStream[K, V, F <: InputFormat[K, V]](directory: String, filter: (Path) ⇒ Boolean, newFilesOnly: Boolean)(implicit arg0: ClassTag[K], arg1: ClassTag[V], arg2: ClassTag[F]): InputDStream[(K, V)]

    Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.

    Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format. Files must be written to the monitored directory by "moving" them from another location within the same file system.

    K

    Key type for reading HDFS file

    V

    Value type for reading HDFS file

    F

    Input format for reading HDFS file

    directory

    HDFS directory to monitor for new file

    filter

    Function to filter paths to process

    newFilesOnly

    Should process only new files and ignore existing files in the directory

  16. def fileStream[K, V, F <: InputFormat[K, V]](directory: String)(implicit arg0: ClassTag[K], arg1: ClassTag[V], arg2: ClassTag[F]): InputDStream[(K, V)]

    Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.

    Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format. Files must be written to the monitored directory by "moving" them from another location within the same file system. File names starting with . are ignored.

    K

    Key type for reading HDFS file

    V

    Value type for reading HDFS file

    F

    Input format for reading HDFS file

    directory

    HDFS directory to monitor for new file

  17. def finalize(): Unit

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

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  19. def hashCode(): Int

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  20. final def isInstanceOf[T0]: Boolean

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  21. def isTraceEnabled(): Boolean

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  22. def log: Logger

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

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

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

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

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

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

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  29. def logName: String

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

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

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

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

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

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  35. final def notify(): Unit

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  36. final def notifyAll(): Unit

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  37. def queueStream[T](queue: Queue[RDD[T]], oneAtATime: Boolean, defaultRDD: RDD[T])(implicit arg0: ClassTag[T]): InputDStream[T]

    Create an input stream from a queue of RDDs.

    Create an input stream from a queue of RDDs. In each batch, it will process either one or all of the RDDs returned by the queue.

    T

    Type of objects in the RDD

    queue

    Queue of RDDs

    oneAtATime

    Whether only one RDD should be consumed from the queue in every interval

    defaultRDD

    Default RDD is returned by the DStream when the queue is empty. Set as null if no RDD should be returned when empty

  38. def queueStream[T](queue: Queue[RDD[T]], oneAtATime: Boolean = true)(implicit arg0: ClassTag[T]): InputDStream[T]

    Create an input stream from a queue of RDDs.

    Create an input stream from a queue of RDDs. In each batch, it will process either one or all of the RDDs returned by the queue.

    T

    Type of objects in the RDD

    queue

    Queue of RDDs

    oneAtATime

    Whether only one RDD should be consumed from the queue in every interval

  39. def rawSocketStream[T](hostname: String, port: Int, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2)(implicit arg0: ClassTag[T]): ReceiverInputDStream[T]

    Create a input stream from network source hostname:port, where data is received as serialized blocks (serialized using the Spark's serializer) that can be directly pushed into the block manager without deserializing them.

    Create a input stream from network source hostname:port, where data is received as serialized blocks (serialized using the Spark's serializer) that can be directly pushed into the block manager without deserializing them. This is the most efficient way to receive data.

    T

    Type of the objects in the received blocks

    hostname

    Hostname to connect to for receiving data

    port

    Port to connect to for receiving data

    storageLevel

    Storage level to use for storing the received objects (default: StorageLevel.MEMORY_AND_DISK_SER_2)

  40. def receiverStream[T](receiver: Receiver[T])(implicit arg0: ClassTag[T]): ReceiverInputDStream[T]

    Create an input stream with any arbitrary user implemented receiver.

    Create an input stream with any arbitrary user implemented receiver. Find more details at: http://spark.apache.org/docs/latest/streaming-custom-receivers.html

    receiver

    Custom implementation of Receiver

  41. def remember(duration: Duration): Unit

    Set each DStreams in this context to remember RDDs it generated in the last given duration.

    Set each DStreams in this context to remember RDDs it generated in the last given duration. DStreams remember RDDs only for a limited duration of time and releases them for garbage collection. This method allows the developer to specify how to long to remember the RDDs ( if the developer wishes to query old data outside the DStream computation).

    duration

    Minimum duration that each DStream should remember its RDDs

  42. def socketStream[T](hostname: String, port: Int, converter: (InputStream) ⇒ Iterator[T], storageLevel: StorageLevel)(implicit arg0: ClassTag[T]): ReceiverInputDStream[T]

    Create a input stream from TCP source hostname:port.

    Create a input stream from TCP source hostname:port. Data is received using a TCP socket and the receive bytes it interepreted as object using the given converter.

    T

    Type of the objects received (after converting bytes to objects)

    hostname

    Hostname to connect to for receiving data

    port

    Port to connect to for receiving data

    converter

    Function to convert the byte stream to objects

    storageLevel

    Storage level to use for storing the received objects

  43. def socketTextStream(hostname: String, port: Int, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2): ReceiverInputDStream[String]

    Create a input stream from TCP source hostname:port.

    Create a input stream from TCP source hostname:port. Data is received using a TCP socket and the receive bytes is interpreted as UTF8 encoded \n delimited lines.

    hostname

    Hostname to connect to for receiving data

    port

    Port to connect to for receiving data

    storageLevel

    Storage level to use for storing the received objects (default: StorageLevel.MEMORY_AND_DISK_SER_2)

  44. def sparkContext: SparkContext

    Return the associated Spark context

  45. def start(): Unit

    Start the execution of the streams.

    Start the execution of the streams.

    Exceptions thrown
    SparkException

    if the context has already been started or stopped.

  46. def stop(stopSparkContext: Boolean, stopGracefully: Boolean): Unit

    Stop the execution of the streams, with option of ensuring all received data has been processed.

    Stop the execution of the streams, with option of ensuring all received data has been processed.

    stopSparkContext

    if true, stops the associated SparkContext. The underlying SparkContext will be stopped regardless of whether this StreamingContext has been started.

    stopGracefully

    if true, stops gracefully by waiting for the processing of all received data to be completed

  47. def stop(stopSparkContext: Boolean = true): Unit

    Stop the execution of the streams immediately (does not wait for all received data to be processed).

    Stop the execution of the streams immediately (does not wait for all received data to be processed).

    stopSparkContext

    if true, stops the associated SparkContext. The underlying SparkContext will be stopped regardless of whether this StreamingContext has been started.

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

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  49. def textFileStream(directory: String): DStream[String]

    Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them as text files (using key as LongWritable, value as Text and input format as TextInputFormat).

    Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them as text files (using key as LongWritable, value as Text and input format as TextInputFormat). Files must be written to the monitored directory by "moving" them from another location within the same file system. File names starting with . are ignored.

    directory

    HDFS directory to monitor for new file

  50. def toString(): String

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  51. def transform[T](dstreams: Seq[DStream[_]], transformFunc: (Seq[RDD[_]], Time) ⇒ RDD[T])(implicit arg0: ClassTag[T]): DStream[T]

    Create a new DStream in which each RDD is generated by applying a function on RDDs of the DStreams.

  52. def union[T](streams: Seq[DStream[T]])(implicit arg0: ClassTag[T]): DStream[T]

    Create a unified DStream from multiple DStreams of the same type and same slide duration.

  53. final def wait(): Unit

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

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

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Deprecated Value Members

  1. def networkStream[T](receiver: Receiver[T])(implicit arg0: ClassTag[T]): ReceiverInputDStream[T]

    Create an input stream with any arbitrary user implemented receiver.

    Create an input stream with any arbitrary user implemented receiver. Find more details at: http://spark.apache.org/docs/latest/streaming-custom-receivers.html

    receiver

    Custom implementation of Receiver

    Annotations
    @deprecated
    Deprecated

    (Since version 1.0.0) Use receiverStream

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