You are building a Google Cloud Dataflow streaming pipeline that must publish a 15-minute rolling average of purchase amounts. The average must be recomputed every 5 minutes, and events that arrive up to 2 minutes late must still be reflected in the output. Which Apache Beam windowing configuration best satisfies these requirements?
Apply FixedWindows.of(Duration.minutes(15)) with an early trigger firing every 5 minutes.
Apply SlidingWindow.of(Duration.minutes(5)).every(Duration.minutes(15)) and set allowedLateness(Duration.minutes(2)).
Apply SlidingWindow.of(Duration.minutes(15)).every(Duration.minutes(5)) and set allowedLateness(Duration.minutes(2)).
Apply SessionWindows.withGapDuration(Duration.minutes(5)) and allowedLateness(Duration.minutes(2)).
Hopping (sliding) windows are designed for overlapping, fixed-length analyses that advance at a regular interval. A 15-minute sliding window with a 5-minute hop produces a new window every 5 minutes that contains the preceding 15 minutes of events, exactly matching the rolling-average requirement. Configuring allowed lateness for 2 minutes ensures late events within that bound are incorporated. Fixed windows would not overlap, early triggers alone do not replicate sliding semantics, session windows depend on activity gaps, and a 5-minute window sliding every 15 minutes would return the wrong aggregation period.
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