sahnib commented on code in PR #45376:
URL: https://github.com/apache/spark/pull/45376#discussion_r1589364420
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sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala:
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@@ -702,6 +742,39 @@ class KeyValueGroupedDataset[K, V] private[sql](
transformWithState(statefulProcessor, timeMode, outputMode)(outputEncoder)
}
+ /**
+ * (Java-specific)
+ * Invokes methods defined in the stateful processor used in arbitrary state
API v2.
+ * We allow the user to act on per-group set of input rows along with keyed
state and the
+ * user can choose to output/return 0 or more rows.
+ *
+ * For a streaming dataframe, we will repeatedly invoke the interface
methods for new rows
+ * in each trigger and the user's state/state variables will be stored
persistently across
+ * invocations.
+ *
+ * Downstream operators would use specified eventTimeColumnName to calculate
watermark.
+ * Note that TimeMode is set to EventTime to ensure correct flow of
watermark.
+ *
+ * @tparam U The type of the output objects. Must be encodable to Spark SQL
types.
+ * @param statefulProcessor Instance of statefulProcessor whose functions
will be invoked by the
+ * operator.
+ * @param eventTimeColumnName eventTime column in the output dataset. Any
operations after
+ * transformWithState will use the new
eventTimeColumn. The user
+ * needs to ensure that the eventTime for emitted
output adheres to
+ * the watermark boundary, otherwise streaming
query will fail.
+ * @param outputMode The output mode of the stateful processor.
+ * @param outputEncoder Encoder for the output type.
+ *
+ * See [[Encoder]] for more details on what types are encodable to Spark SQL.
+ */
+ private[sql] def transformWithState[U: Encoder](
+ statefulProcessor: StatefulProcessor[K, V, U],
+ eventTimeColumnName: String,
+ outputMode: OutputMode,
+ outputEncoder: Encoder[U]): Dataset[U] = {
+ transformWithState(statefulProcessor, TimeMode.EventTime(),
outputMode)(outputEncoder)
Review Comment:
Thanks, the second parameter needs to be `eventTimeColumnName`.
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