HeartSaVioR commented on code in PR #39931:
URL: https://github.com/apache/spark/pull/39931#discussion_r1119697176
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sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala:
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@@ -96,6 +98,25 @@ trait StateStoreReader extends StatefulOperator {
/** An operator that writes to a StateStore. */
trait StateStoreWriter extends StatefulOperator with PythonSQLMetrics { self:
SparkPlan =>
+ /**
+ * Produce the output watermark for given input watermark (ms).
+ *
+ * In most cases, this is same as the criteria of state eviction, as most
stateful operators
+ * produce the output from two different kinds:
+ *
+ * 1. without buffering
+ * 2. with buffering (state)
+ *
+ * The state eviction happens when event time exceeds a "certain threshold
of timestamp", which
+ * denotes a lower bound of event time values for output (output watermark).
+ *
+ * The default implementation provides the input watermark as it is. Most
built-in operators
+ * will evict based on min input watermark and ensure it will be minimum of
the event time value
+ * for the output so far (including output from eviction). Operators which
behave differently
+ * (e.g. different criteria on eviction) must override this method.
+ */
+ def produceOutputWatermark(inputWatermarkMs: Long): Option[Long] =
Some(inputWatermarkMs)
Review Comment:
> The example you provided if the 5 minute window operator outputted a
window of 0-5, this window record should be buffered as an entry in 3-6 window
range for the 3 minute window.
The representative time of the time window is just to make thing easy to
think through timestamp rather than time range (window). It does not mean it is
always semantically correct to apply the window time. If you add the 0-5 window
into 3-6 window range, it's already producing incorrect output. It's making no
sense to add the aggregation for 0-3 into 3-6 window.
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