Package org.apache.spark.streaming

Class StreamingContext

Object
org.apache.spark.streaming.StreamingContext
All Implemented Interfaces:
org.apache.spark.internal.Logging
public class StreamingContext extends Object implements org.apache.spark.internal.Logging
Deprecated.
This is deprecated as of Spark 3.4.0. There are no longer updates to DStream and it's a legacy project. There is a newer and easier to use streaming engine in Spark called Structured Streaming. You should use Spark Structured Streaming for your streaming applications.
Main entry point for Spark Streaming functionality. It provides methods used to create 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.awaitTermination() allows the current thread to wait for the termination of the context by stop() or by an exception.

  • Nested Class Summary

    Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

    org.apache.spark.internal.Logging.SparkShellLoggingFilter

  • Constructor Summary

    Constructors
    Constructor
    Description
    StreamingContext(String path)
    Deprecated.
    Recreate a StreamingContext from a checkpoint file.
    StreamingContext(String master, String appName, Duration batchDuration, String sparkHome, scala.collection.Seq<String> jars, scala.collection.Map<String,String> environment)
    Deprecated.
    Create a StreamingContext by providing the details necessary for creating a new SparkContext.
    StreamingContext(String path, org.apache.hadoop.conf.Configuration hadoopConf)
    Deprecated.
    Recreate a StreamingContext from a checkpoint file.
    StreamingContext(String path, SparkContext sparkContext)
    Deprecated.
    Recreate a StreamingContext from a checkpoint file using an existing SparkContext.
    StreamingContext(SparkConf conf, Duration batchDuration)
    Deprecated.
    Create a StreamingContext by providing the configuration necessary for a new SparkContext.
    StreamingContext(SparkContext sparkContext, Duration batchDuration)
    Deprecated.
    Create a StreamingContext using an existing SparkContext.

  • Method Summary

    Modifier and Type
    Method
    Description
    void
    addStreamingListener(StreamingListener streamingListener)
    Deprecated.
    Add a StreamingListener object for receiving system events related to streaming.
    void
    awaitTermination()
    Deprecated.
    Wait for the execution to stop.
    boolean
    awaitTerminationOrTimeout(long timeout)
    Deprecated.
    Wait for the execution to stop.
    DStream<byte[]>
    binaryRecordsStream(String directory, int recordLength)
    Deprecated.
    Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them as flat binary files, assuming a fixed length per record, generating one byte array per record.
    void
    checkpoint(String directory)
    Deprecated.
    Set the context to periodically checkpoint the DStream operations for driver fault-tolerance.
    <K, V, F extends org.apache.hadoop.mapreduce.InputFormat<K, V>>
    InputDStream<scala.Tuple2<K,V>>
    fileStream(String directory, scala.Function1<org.apache.hadoop.fs.Path,Object> filter, boolean newFilesOnly, org.apache.hadoop.conf.Configuration conf, scala.reflect.ClassTag<K> evidence$10, scala.reflect.ClassTag<V> evidence$11, scala.reflect.ClassTag<F> evidence$12)
    Deprecated.
    Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
    <K, V, F extends org.apache.hadoop.mapreduce.InputFormat<K, V>>
    InputDStream<scala.Tuple2<K,V>>
    fileStream(String directory, scala.Function1<org.apache.hadoop.fs.Path,Object> filter, boolean newFilesOnly, scala.reflect.ClassTag<K> evidence$7, scala.reflect.ClassTag<V> evidence$8, scala.reflect.ClassTag<F> evidence$9)
    Deprecated.
    Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
    <K, V, F extends org.apache.hadoop.mapreduce.InputFormat<K, V>>
    InputDStream<scala.Tuple2<K,V>>
    fileStream(String directory, scala.reflect.ClassTag<K> evidence$4, scala.reflect.ClassTag<V> evidence$5, scala.reflect.ClassTag<F> evidence$6)
    Deprecated.
    Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
    static scala.Option<StreamingContext>
    getActive()
    Deprecated.
    Get the currently active context, if there is one.
    static StreamingContext
    getActiveOrCreate(String checkpointPath, scala.Function0<StreamingContext> creatingFunc, org.apache.hadoop.conf.Configuration hadoopConf, boolean createOnError)
    Deprecated.
    Either get the currently active StreamingContext (that is, started but not stopped), OR recreate a StreamingContext from checkpoint data in the given path.
    static StreamingContext
    getActiveOrCreate(scala.Function0<StreamingContext> creatingFunc)
    Deprecated.
    Either return the "active" StreamingContext (that is, started but not stopped), or create a new StreamingContext that is
    static StreamingContext
    getOrCreate(String checkpointPath, scala.Function0<StreamingContext> creatingFunc, org.apache.hadoop.conf.Configuration hadoopConf, boolean createOnError)
    Deprecated.
    Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
    StreamingContextState
    getState()
    Deprecated.
    :: DeveloperApi ::
    static scala.Option<String>
    jarOfClass(Class<?> cls)
    Deprecated.
    Find the JAR from which a given class was loaded, to make it easy for users to pass their JARs to StreamingContext.
    static org.slf4j.Logger
    org$apache$spark$internal$Logging$$log_()
    Deprecated.
     
    static void
    org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)
    Deprecated.
     
    <T> InputDStream<T>
    queueStream(scala.collection.mutable.Queue<RDD<T>> queue, boolean oneAtATime, RDD<T> defaultRDD, scala.reflect.ClassTag<T> evidence$14)
    Deprecated.
    Create an input stream from a queue of RDDs.
    <T> InputDStream<T>
    queueStream(scala.collection.mutable.Queue<RDD<T>> queue, boolean oneAtATime, scala.reflect.ClassTag<T> evidence$13)
    Deprecated.
    Create an input stream from a queue of RDDs.
    <T> ReceiverInputDStream<T>
    rawSocketStream(String hostname, int port, StorageLevel storageLevel, scala.reflect.ClassTag<T> evidence$3)
    Deprecated.
    Create an 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.
    <T> ReceiverInputDStream<T>
    receiverStream(Receiver<T> receiver, scala.reflect.ClassTag<T> evidence$1)
    Deprecated.
    Create an input stream with any arbitrary user implemented receiver.
    void
    remember(Duration duration)
    Deprecated.
    Set each DStream in this context to remember RDDs it generated in the last given duration.
    void
    removeStreamingListener(StreamingListener streamingListener)
    Deprecated.
     
    <T> ReceiverInputDStream<T>
    socketStream(String hostname, int port, scala.Function1<InputStream,scala.collection.Iterator<T>> converter, StorageLevel storageLevel, scala.reflect.ClassTag<T> evidence$2)
    Deprecated.
    Creates an input stream from TCP source hostname:port.
    ReceiverInputDStream<String>
    socketTextStream(String hostname, int port, StorageLevel storageLevel)
    Deprecated.
    Creates an input stream from TCP source hostname:port.
    SparkContext
    sparkContext()
    Deprecated.
    Return the associated Spark context
    void
    start()
    Deprecated.
    Start the execution of the streams.
    void
    stop(boolean stopSparkContext)
    Deprecated.
    Stop the execution of the streams immediately (does not wait for all received data to be processed).
    void
    stop(boolean stopSparkContext, boolean stopGracefully)
    Deprecated.
    Stop the execution of the streams, with option of ensuring all received data has been processed.
    DStream<String>
    textFileStream(String directory)
    Deprecated.
    Create an 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).
    <T> DStream<T>
    transform(scala.collection.Seq<DStream<?>> dstreams, scala.Function2<scala.collection.Seq<RDD<?>>,Time,RDD<T>> transformFunc, scala.reflect.ClassTag<T> evidence$16)
    Deprecated.
    Create a new DStream in which each RDD is generated by applying a function on RDDs of the DStreams.
    <T> DStream<T>
    union(scala.collection.Seq<DStream<T>> streams, scala.reflect.ClassTag<T> evidence$15)
    Deprecated.
    Create a unified DStream from multiple DStreams of the same type and same slide duration.

    Methods inherited from class java.lang.Object

    equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    Methods inherited from interface org.apache.spark.internal.Logging

    initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq

  • Constructor Details

    • StreamingContext

      public StreamingContext(SparkContext sparkContext, Duration batchDuration)
      Deprecated.
      Create a StreamingContext using an existing SparkContext.
      Parameters:
      sparkContext - existing SparkContext
      batchDuration - the time interval at which streaming data will be divided into batches

    • StreamingContext

      public StreamingContext(SparkConf conf, Duration batchDuration)
      Deprecated.
      Create a StreamingContext by providing the configuration necessary for a new SparkContext.
      Parameters:
      conf - a org.apache.spark.SparkConf object specifying Spark parameters
      batchDuration - the time interval at which streaming data will be divided into batches

    • StreamingContext

      public StreamingContext(String master, String appName, Duration batchDuration, String sparkHome, scala.collection.Seq<String> jars, scala.collection.Map<String,String> environment)
      Deprecated.
      Create a StreamingContext by providing the details necessary for creating a new SparkContext.
      Parameters:
      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
      sparkHome - (undocumented)
      jars - (undocumented)
      environment - (undocumented)

    • StreamingContext

      public StreamingContext(String path, org.apache.hadoop.conf.Configuration hadoopConf)
      Deprecated.
      Recreate a StreamingContext from a checkpoint file.
      Parameters:
      path - Path to the directory that was specified as the checkpoint directory
      hadoopConf - Optional, configuration object if necessary for reading from HDFS compatible filesystems

    • StreamingContext

      public StreamingContext(String path)
      Deprecated.
      Recreate a StreamingContext from a checkpoint file.
      Parameters:
      path - Path to the directory that was specified as the checkpoint directory

    • StreamingContext

      public StreamingContext(String path, SparkContext sparkContext)
      Deprecated.
      Recreate a StreamingContext from a checkpoint file using an existing SparkContext.
      Parameters:
      path - Path to the directory that was specified as the checkpoint directory
      sparkContext - Existing SparkContext

  • Method Details

    • getActive

      public static scala.Option<StreamingContext> getActive()
      Deprecated.
      Get the currently active context, if there is one. Active means started but not stopped.
      Returns:
      (undocumented)

    • getActiveOrCreate

      public static StreamingContext getActiveOrCreate(scala.Function0<StreamingContext> creatingFunc)
      Deprecated.
      Either return the "active" StreamingContext (that is, started but not stopped), or create a new StreamingContext that is
      Parameters:
      creatingFunc - Function to create a new StreamingContext
      Returns:
      (undocumented)

    • getActiveOrCreate

      public static StreamingContext getActiveOrCreate(String checkpointPath, scala.Function0<StreamingContext> creatingFunc, org.apache.hadoop.conf.Configuration hadoopConf, boolean createOnError)
      Deprecated.
      Either get the currently active StreamingContext (that is, started but not stopped), OR recreate a StreamingContext from checkpoint data in the given path. If checkpoint data does not exist in the provided, then create a new StreamingContext by calling the provided creatingFunc.

      Parameters:
      checkpointPath - Checkpoint directory used in an earlier StreamingContext program
      creatingFunc - Function to create a new StreamingContext
      hadoopConf - Optional Hadoop configuration if necessary for reading from the file system
      createOnError - Optional, whether to create a new StreamingContext if there is an error in reading checkpoint data. By default, an exception will be thrown on error.
      Returns:
      (undocumented)

    • getOrCreate

      public static StreamingContext getOrCreate(String checkpointPath, scala.Function0<StreamingContext> creatingFunc, org.apache.hadoop.conf.Configuration hadoopConf, boolean createOnError)
      Deprecated.
      Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. If checkpoint data exists in the provided checkpointPath, then StreamingContext will be recreated from the checkpoint data. If the data does not exist, then the StreamingContext will be created by called the provided creatingFunc.

      Parameters:
      checkpointPath - Checkpoint directory used in an earlier StreamingContext program
      creatingFunc - Function to create a new StreamingContext
      hadoopConf - Optional Hadoop configuration if necessary for reading from the file system
      createOnError - Optional, whether to create a new StreamingContext if there is an error in reading checkpoint data. By default, an exception will be thrown on error.
      Returns:
      (undocumented)

    • jarOfClass

      public static scala.Option<String> jarOfClass(Class<?> cls)
      Deprecated.
      Find the JAR from which a given class was loaded, to make it easy for users to pass their JARs to StreamingContext.
      Parameters:
      cls - (undocumented)
      Returns:
      (undocumented)

    • org$apache$spark$internal$Logging$$log_

      public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()
      Deprecated.

    • org$apache$spark$internal$Logging$$log__$eq

      public static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)
      Deprecated.

    • sparkContext

      public SparkContext sparkContext()
      Deprecated.
      Return the associated Spark context
      Returns:
      (undocumented)

    • remember

      public void remember(Duration duration)
      Deprecated.
      Set each DStream in this context to remember RDDs it generated in the last given duration. DStreams remember RDDs only for a limited duration of time and release them for garbage collection. This method allows the developer to specify how long to remember the RDDs ( if the developer wishes to query old data outside the DStream computation).
      Parameters:
      duration - Minimum duration that each DStream should remember its RDDs

    • checkpoint

      public void checkpoint(String directory)
      Deprecated.
      Set the context to periodically checkpoint the DStream operations for driver fault-tolerance.
      Parameters:
      directory - HDFS-compatible directory where the checkpoint data will be reliably stored. Note that this must be a fault-tolerant file system like HDFS.

    • receiverStream

      public <T> ReceiverInputDStream<T> receiverStream(Receiver<T> receiver, scala.reflect.ClassTag<T> evidence$1)
      Deprecated.
      Create an input stream with any arbitrary user implemented receiver. Find more details at https://spark.apache.org/docs/latest/streaming-custom-receivers.html
      Parameters:
      receiver - Custom implementation of Receiver
      evidence$1 - (undocumented)
      Returns:
      (undocumented)

    • socketTextStream

      public ReceiverInputDStream<String> socketTextStream(String hostname, int port, StorageLevel storageLevel)
      Deprecated.
      Creates an 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.
      Parameters:
      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)
      Returns:
      (undocumented)
      See Also:

    • socketStream

      public <T> ReceiverInputDStream<T> socketStream(String hostname, int port, scala.Function1<InputStream,scala.collection.Iterator<T>> converter, StorageLevel storageLevel, scala.reflect.ClassTag<T> evidence$2)
      Deprecated.
      Creates an input stream from TCP source hostname:port. Data is received using a TCP socket and the receive bytes it interpreted as object using the given converter.
      Parameters:
      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
      evidence$2 - (undocumented)
      Returns:
      (undocumented)

    • rawSocketStream

      public <T> ReceiverInputDStream<T> rawSocketStream(String hostname, int port, StorageLevel storageLevel, scala.reflect.ClassTag<T> evidence$3)
      Deprecated.
      Create an 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.
      Parameters:
      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)
      evidence$3 - (undocumented)
      Returns:
      (undocumented)

    • fileStream

      public <K, V, F extends org.apache.hadoop.mapreduce.InputFormat<K, V>> InputDStream<scala.Tuple2<K,V>> fileStream(String directory, scala.reflect.ClassTag<K> evidence$4, scala.reflect.ClassTag<V> evidence$5, scala.reflect.ClassTag<F> evidence$6)
      Deprecated.
      Create an 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.
      Parameters:
      directory - HDFS directory to monitor for new file
      evidence$4 - (undocumented)
      evidence$5 - (undocumented)
      evidence$6 - (undocumented)
      Returns:
      (undocumented)

    • fileStream

      public <K, V, F extends org.apache.hadoop.mapreduce.InputFormat<K, V>> InputDStream<scala.Tuple2<K,V>> fileStream(String directory, scala.Function1<org.apache.hadoop.fs.Path,Object> filter, boolean newFilesOnly, scala.reflect.ClassTag<K> evidence$7, scala.reflect.ClassTag<V> evidence$8, scala.reflect.ClassTag<F> evidence$9)
      Deprecated.
      Create an 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.
      Parameters:
      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
      evidence$7 - (undocumented)
      evidence$8 - (undocumented)
      evidence$9 - (undocumented)
      Returns:
      (undocumented)

    • fileStream

      public <K, V, F extends org.apache.hadoop.mapreduce.InputFormat<K, V>> InputDStream<scala.Tuple2<K,V>> fileStream(String directory, scala.Function1<org.apache.hadoop.fs.Path,Object> filter, boolean newFilesOnly, org.apache.hadoop.conf.Configuration conf, scala.reflect.ClassTag<K> evidence$10, scala.reflect.ClassTag<V> evidence$11, scala.reflect.ClassTag<F> evidence$12)
      Deprecated.
      Create an 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.
      Parameters:
      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
      conf - Hadoop configuration
      evidence$10 - (undocumented)
      evidence$11 - (undocumented)
      evidence$12 - (undocumented)
      Returns:
      (undocumented)

    • textFileStream

      public DStream<String> textFileStream(String directory)
      Deprecated.
      Create an 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. The text files must be encoded as UTF-8.

      Parameters:
      directory - HDFS directory to monitor for new file
      Returns:
      (undocumented)

    • binaryRecordsStream

      public DStream<byte[]> binaryRecordsStream(String directory, int recordLength)
      Deprecated.
      Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them as flat binary files, assuming a fixed length per record, generating one byte array per record. 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.

      Parameters:
      directory - HDFS directory to monitor for new file
      recordLength - length of each record in bytes

      Returns:
      (undocumented)
      Note:
      We ensure that the byte array for each record in the resulting RDDs of the DStream has the provided record length.

    • queueStream

      public <T> InputDStream<T> queueStream(scala.collection.mutable.Queue<RDD<T>> queue, boolean oneAtATime, scala.reflect.ClassTag<T> evidence$13)
      Deprecated.
      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.

      Parameters:
      queue - Queue of RDDs. Modifications to this data structure must be synchronized.
      oneAtATime - Whether only one RDD should be consumed from the queue in every interval
      evidence$13 - (undocumented)
      Returns:
      (undocumented)
      Note:
      Arbitrary RDDs can be added to queueStream, there is no way to recover data of those RDDs, so queueStream doesn't support checkpointing.

    • queueStream

      public <T> InputDStream<T> queueStream(scala.collection.mutable.Queue<RDD<T>> queue, boolean oneAtATime, RDD<T> defaultRDD, scala.reflect.ClassTag<T> evidence$14)
      Deprecated.
      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.

      Parameters:
      queue - Queue of RDDs. Modifications to this data structure must be synchronized.
      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
      evidence$14 - (undocumented)
      Returns:
      (undocumented)
      Note:
      Arbitrary RDDs can be added to queueStream, there is no way to recover data of those RDDs, so queueStream doesn't support checkpointing.

    • union

      public <T> DStream<T> union(scala.collection.Seq<DStream<T>> streams, scala.reflect.ClassTag<T> evidence$15)
      Deprecated.
      Create a unified DStream from multiple DStreams of the same type and same slide duration.
      Parameters:
      streams - (undocumented)
      evidence$15 - (undocumented)
      Returns:
      (undocumented)

    • transform

      public <T> DStream<T> transform(scala.collection.Seq<DStream<?>> dstreams, scala.Function2<scala.collection.Seq<RDD<?>>,Time,RDD<T>> transformFunc, scala.reflect.ClassTag<T> evidence$16)
      Deprecated.
      Create a new DStream in which each RDD is generated by applying a function on RDDs of the DStreams.
      Parameters:
      dstreams - (undocumented)
      transformFunc - (undocumented)
      evidence$16 - (undocumented)
      Returns:
      (undocumented)

    • addStreamingListener

      public void addStreamingListener(StreamingListener streamingListener)
      Deprecated.
      Add a StreamingListener object for receiving system events related to streaming.
      Parameters:
      streamingListener - (undocumented)

    • removeStreamingListener

      public void removeStreamingListener(StreamingListener streamingListener)
      Deprecated.

    • getState

      public StreamingContextState getState()
      Deprecated.
      :: DeveloperApi ::

      Return the current state of the context. The context can be in three possible states -

      - StreamingContextState.INITIALIZED - The context has been created, but not started yet. Input DStreams, transformations and output operations can be created on the context. - StreamingContextState.ACTIVE - The context has been started, and not stopped. Input DStreams, transformations and output operations cannot be created on the context. - StreamingContextState.STOPPED - The context has been stopped and cannot be used any more.

      Returns:
      (undocumented)

    • start

      public void start()
      Deprecated.
      Start the execution of the streams.

      Throws:
      IllegalStateException - if the StreamingContext is already stopped.

    • awaitTermination

      public void awaitTermination()
      Deprecated.
      Wait for the execution to stop. Any exceptions that occurs during the execution will be thrown in this thread.

    • awaitTerminationOrTimeout

      public boolean awaitTerminationOrTimeout(long timeout)
      Deprecated.
      Wait for the execution to stop. Any exceptions that occurs during the execution will be thrown in this thread.

      Parameters:
      timeout - time to wait in milliseconds
      Returns:
      true if it's stopped; or throw the reported error during the execution; or false if the waiting time elapsed before returning from the method.

    • stop

      public void stop(boolean stopSparkContext)
      Deprecated.
      Stop the execution of the streams immediately (does not wait for all received data to be processed). By default, if stopSparkContext is not specified, the underlying SparkContext will also be stopped. This implicit behavior can be configured using the SparkConf configuration spark.streaming.stopSparkContextByDefault.

      Parameters:
      stopSparkContext - If true, stops the associated SparkContext. The underlying SparkContext will be stopped regardless of whether this StreamingContext has been started.

    • stop

      public void stop(boolean stopSparkContext, boolean stopGracefully)
      Deprecated.
      Stop the execution of the streams, with option of ensuring all received data has been processed.

      Parameters:
      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