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org.apache.spark.sql.streaming

DataStreamWriter

final class DataStreamWriter[T] extends AnyRef

Interface used to write a streaming Dataset to external storage systems (e.g. file systems, key-value stores, etc). Use Dataset.writeStream to access this.

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Since

2.0.0

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  9. 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

  10. def foreachBatch(function: VoidFunction2[Dataset[T], Long]): DataStreamWriter[T]

    :: Experimental ::

    :: Experimental ::

    (Java-specific) Sets the output of the streaming query to be processed using the provided function. This is supported only the in the micro-batch execution modes (that is, when the trigger is not continuous). In every micro-batch, the provided function will be called in every micro-batch with (i) the output rows as a Dataset and (ii) the batch identifier. The batchId can be used deduplicate and transactionally write the output (that is, the provided Dataset) to external systems. The output Dataset is guaranteed to exactly same for the same batchId (assuming all operations are deterministic in the query).

    Annotations
    @Evolving()
    Since

    2.4.0

  11. def foreachBatch(function: (Dataset[T], Long) ⇒ Unit): DataStreamWriter[T]

    :: Experimental ::

    :: Experimental ::

    (Scala-specific) Sets the output of the streaming query to be processed using the provided function. This is supported only the in the micro-batch execution modes (that is, when the trigger is not continuous). In every micro-batch, the provided function will be called in every micro-batch with (i) the output rows as a Dataset and (ii) the batch identifier. The batchId can be used deduplicate and transactionally write the output (that is, the provided Dataset) to external systems. The output Dataset is guaranteed to exactly same for the same batchId (assuming all operations are deterministic in the query).

    Annotations
    @Evolving()
    Since

    2.4.0

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

    Specifies the underlying output data source.

    Specifies the underlying output data source.

    Since

    2.0.0

  13. final def getClass(): Class[_]
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  18. final def notifyAll(): Unit
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  19. def option(key: String, value: Double): DataStreamWriter[T]

    Adds an output option for the underlying data source.

    Adds an output option for the underlying data source.

    Since

    2.0.0

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

    Adds an output option for the underlying data source.

    Adds an output option for the underlying data source.

    Since

    2.0.0

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

    Adds an output option for the underlying data source.

    Adds an output option for the underlying data source.

    Since

    2.0.0

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

    Adds an output option for the underlying data source.

    Adds an output option for the underlying data source.

    You can set the following option(s):

    • timeZone (default session local timezone): sets the string that indicates a timezone to be used to format timestamps in the JSON/CSV datasources or partition values.
    Since

    2.0.0

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

    Adds output options for the underlying data source.

    Adds output options for the underlying data source.

    You can set the following option(s):

    • timeZone (default session local timezone): sets the string that indicates a timezone to be used to format timestamps in the JSON/CSV datasources or partition values.
    Since

    2.0.0

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

    (Scala-specific) Adds output options for the underlying data source.

    (Scala-specific) Adds output options for the underlying data source.

    You can set the following option(s):

    • timeZone (default session local timezone): sets the string that indicates a timezone to be used to format timestamps in the JSON/CSV datasources or partition values.
    Since

    2.0.0

  25. def outputMode(outputMode: String): 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.

    • append: only the new rows in the streaming DataFrame/Dataset will be written to the sink.
    • complete: all the rows in the streaming DataFrame/Dataset will be written to the sink every time there are some updates.
    • 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 append mode.
    Since

    2.0.0

  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. def partitionBy(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.

    Annotations
    @varargs()
    Since

    2.0.0

  28. 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

  29. def start(): 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

  30. def start(path: String): 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

  31. final def synchronized[T0](arg0: ⇒ T0): T0
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  32. def toString(): String
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  33. 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))

    Java Example:

    df.writeStream().trigger(ProcessingTime.create("10 seconds"))

import java.util.concurrent.TimeUnit
df.writeStream().trigger(ProcessingTime.create(10, TimeUnit.SECONDS))
    Since

    2.0.0

  34. final def wait(): Unit
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