DataStreamWriter

Value Members

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

   

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2. final def ##(): Int

   

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

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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   protected[java.lang]

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   @throws( ... )

6. def continuouslyWithCheckpointEvery(duration: zio.Duration): DataStreamWriter[T]

   

   A ZIO-Spark specific function to describe a continuously stream with checkpoint.

   A ZIO-Spark specific function to describe a continuously stream with checkpoint.

   Scala Example, using ZIO duration ops:

   df.writeStream.continuouslyWithCheckpointEvery(5.seconds)

7. val ds: Dataset[T]

   

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

   

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9. def finalize(): Unit

   

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   protected[java.lang]

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10. def foreach(writer: ForeachWriter[T]): DataStreamWriter[T]

    

    Sets the output of the streaming query to be processed using the provided writer object.

    Sets the output of the streaming query to be processed using the provided writer object. object. See org.apache.spark.sql.ForeachWriter for more details on the lifecycle and semantics.

    Since

    2.0.0

11. val foreachWriter: Option[ForeachWriter[T]]

    

12. def format(source: String): DataStreamWriter[T]

    

    Change the source (sink) of the stream.

    Change the source (sink) of the stream.

    Since

    2.0.0

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

    

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14. final def isInstanceOf[T0]: Boolean

    

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

    

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16. final def notify(): Unit

    

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17. final def notifyAll(): Unit

    

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18. def once: DataStreamWriter[T]

    

    A ZIO-Spark specific function to run the streaming job only once.

19. def option(key: String, value: Double): DataStreamWriter[T]

    

    Adds an option to the DataFrameWriter.

20. def option(key: String, value: Float): DataStreamWriter[T]

    

    Adds an option to the DataFrameWriter.

21. def option(key: String, value: Int): DataStreamWriter[T]

    

    Adds an option to the DataFrameWriter.

22. def option(key: String, value: Boolean): DataStreamWriter[T]

    

    Adds an option to the DataFrameWriter.

23. def option(key: String, value: String): DataStreamWriter[T]

    

    Adds an option to the DataFrameWriter.

24. def options(options: Map[String, String]): DataStreamWriter[T]

    

    Adds multiple options to the DataFrameWriter.

25. val options: Map[String, String]

    

26. def outputMode(outputMode: OutputMode): DataStreamWriter[T]

    

    Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink.

    Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink.

    • OutputMode.Append(): only the new rows in the streaming DataFrame/Dataset will be written to the sink.
    • OutputMode.Complete(): all the rows in the streaming DataFrame/Dataset will be written to the sink every time there are some updates.
    • OutputMode.Update(): only the rows that were updated in the streaming DataFrame/Dataset will be written to the sink every time there are some updates. If the query doesn't contain aggregations, it will be equivalent to OutputMode.Append() mode.

    Since

    2.0.0

27. val outputMode: OutputMode

    

28. def partitionBy(colName: String, colNames: String*): DataStreamWriter[T]

    

    Partitions the output by the given columns on the file system.

    Partitions the output by the given columns on the file system. If specified, the output is laid out on the file system similar to Hive's partitioning scheme. As an example, when we partition a dataset by year and then month, the directory layout would look like:

    • year=2016/month=01/
    • year=2016/month=02/

    Partitioning is one of the most widely used techniques to optimize physical data layout. It provides a coarse-grained index for skipping unnecessary data reads when queries have predicates on the partitioned columns. In order for partitioning to work well, the number of distinct values in each column should typically be less than tens of thousands.

    Since

    2.0.0

29. val partitioningColumns: Option[Seq[String]]

    

30. def queryName(queryName: String): DataStreamWriter[T]

    

    Specifies the name of the StreamingQuery that can be started with start().

    Specifies the name of the StreamingQuery that can be started with start(). This name must be unique among all the currently active queries in the associated SQLContext.

    Since

    2.0.0

31. def run: Task[Unit]

    

    Generate the stream as a stoppable blocking task handled by ZIO.

32. val source: Option[String]

    

33. def start: Task[StreamingQuery]

    

    Starts the execution of the streaming query, which will continually output results to the given path as new data arrives.

    Starts the execution of the streaming query, which will continually output results to the given path as new data arrives. The returned StreamingQuery object can be used to interact with the stream. Throws a TimeoutException if the following conditions are met:

    • Another run of the same streaming query, that is a streaming query sharing the same checkpoint location, is already active on the same Spark Driver
    • The SQL configuration spark.sql.streaming.stopActiveRunOnRestart is enabled
    • The active run cannot be stopped within the timeout controlled by the SQL configuration spark.sql.streaming.stopTimeout

    Annotations

    @throws( ... )

    Since

    2.0.0

34. def start(path: String): Task[StreamingQuery]

    

    Starts the execution of the streaming query, which will continually output results to the given path as new data arrives.

    Starts the execution of the streaming query, which will continually output results to the given path as new data arrives. The returned StreamingQuery object can be used to interact with the stream.

    Since

    2.0.0

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

    

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36. def test: Task[Unit]

    

    Generate a stream with only the available current input.

    Generate a stream with only the available current input. Generally used for testing purpose.

37. def trigger(trigger: Trigger): DataStreamWriter[T]

    

    Set the trigger for the stream query.

    Set the trigger for the stream query. The default value is ProcessingTime(0) and it will run the query as fast as possible.

    Scala Example:

    df.writeStream.trigger(ProcessingTime("10 seconds"))
    
    import scala.concurrent.duration._
    df.writeStream.trigger(ProcessingTime(10.seconds))

    Since

    2.0.0

38. val trigger: Trigger

    

39. def triggerEvery(duration: zio.Duration): DataStreamWriter[T]

    

    A ZIO-Spark specific function to describe a micro batch stream.

    A ZIO-Spark specific function to describe a micro batch stream.

    Scala Example, using ZIO duration ops:

    df.writeStream.triggerEvery(5.seconds)

40. final def wait(): Unit

    

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

    

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

    

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