Packages

trait ContinuousStream extends SparkDataStream

A SparkDataStream for streaming queries with continuous mode.

Annotations
@Evolving()
Source
ContinuousStream.java
Since

3.0.0

Linear Supertypes
SparkDataStream, AnyRef, Any
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Inherited
  1. ContinuousStream
  2. SparkDataStream
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Visibility
  1. Public
  2. Protected

Abstract Value Members

  1. abstract def commit(end: Offset): Unit

    Informs the source that Spark has completed processing all data for offsets less than or equal to end and will only request offsets greater than end in the future.

    Informs the source that Spark has completed processing all data for offsets less than or equal to end and will only request offsets greater than end in the future.

    Definition Classes
    SparkDataStream
  2. abstract def createContinuousReaderFactory(): ContinuousPartitionReaderFactory

    Returns a factory to create a ContinuousPartitionReader for each InputPartition.

  3. abstract def deserializeOffset(json: String): Offset

    Deserialize a JSON string into an Offset of the implementation-defined offset type.

    Deserialize a JSON string into an Offset of the implementation-defined offset type.

    Definition Classes
    SparkDataStream
    Exceptions thrown

    IllegalArgumentException if the JSON does not encode a valid offset for this reader

  4. abstract def initialOffset(): Offset

    Returns the initial offset for a streaming query to start reading from.

    Returns the initial offset for a streaming query to start reading from. Note that the streaming data source should not assume that it will start reading from its initial offset: if Spark is restarting an existing query, it will restart from the check-pointed offset rather than the initial one.

    Definition Classes
    SparkDataStream
  5. abstract def mergeOffsets(offsets: Array[PartitionOffset]): Offset

    Merge partitioned offsets coming from ContinuousPartitionReader instances for each partition to a single global offset.

  6. abstract def planInputPartitions(start: Offset): Array[InputPartition]

    Returns a list of input partitions given the start offset.

    Returns a list of input partitions given the start offset. Each InputPartition represents a data split that can be processed by one Spark task. The number of input partitions returned here is the same as the number of RDD partitions this scan outputs.

    If the Scan supports filter pushdown, this stream is likely configured with a filter and is responsible for creating splits for that filter, which is not a full scan.

    This method will be called to launch one Spark job for reading the data stream. It will be called more than once, if #needsReconfiguration() returns true and Spark needs to launch a new job.

  7. abstract def stop(): Unit

    Stop this source and free any resources it has allocated.

    Stop this source and free any resources it has allocated.

    Definition Classes
    SparkDataStream

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. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  8. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  9. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  10. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  11. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def needsReconfiguration(): Boolean

    The execution engine will call this method in every epoch to determine if new input partitions need to be generated, which may be required if for example the underlying source system has had partitions added or removed.

    The execution engine will call this method in every epoch to determine if new input partitions need to be generated, which may be required if for example the underlying source system has had partitions added or removed.

    If true, the Spark job to scan this continuous data stream will be interrupted and Spark will launch it again with a new list of input partitions.

  13. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  15. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  16. def toString(): String
    Definition Classes
    AnyRef → Any
  17. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  18. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  19. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from SparkDataStream

Inherited from AnyRef

Inherited from Any

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