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Code Block
public final class OffsetOrderedWindowsBatchedWindows extends Windows<TimeWindow> {
		
		/**
		 * Return a window definition with the given window size.
		 * <p> 
		 * This represents stream time window semantics, which are fixed-size,
		 * gap-less, non-overlapping windows. Static windows use the current
		 * stream time when determining which window an event fits into instead
		 * of the event time (as the other window types use).
		 * <p>
		 * Note that offset ordered windows will always accept late arriving data, and
		 * should only be used in situations where the ordering of data is not
		 * necessarily important to the semantics of your application.
		 */
		public static OffsetOrderedWindowsBatchedWindows ofSize(final Duration size) { ... }
    
}

...

Code Block
stream
	.windowBy(OffsetOrderedWindowsBatchedWindows.ofSize(Duration.ofSeconds(10))
	.aggregate((k, v, kvList) -> kvList.add((k, v)))
	.suppress(Suppressed.untilWindowCloses(...))

...

Code Block
public abstract class Windows<W extends Window> {
  ...
  public abstract Map<Long, W> windowsFor(
    final long timestamp, 
    final long observedStreamTime
  );
}

The implementation of OffsetOrderedWindows BatchedWindows will then use the observedStreamTime when computing which window the current event should fall into:

Code Block
@Override
public Map<Long, TimeWindow> windowsFor(
  final long timestamp, 
  final long observedStreamTime
) {
	long windowStart = (observedStreamTime / sizeMs) * sizeMs;
  return Map.of(windowStart, new TimeWindow(windowStart, windowStart + sizeMs));
}

The gracePeriodMs for OffsetOrderedWindows BatchedWindows is always zero, which means there will only ever be one open window per key and events will always fall within this window. Here is the documentation for the remaining public members of the class:

Code Block
/**
 * A fixed-size, stream-time based window specification used for aggregations.
 * <p>
 * The semantics of offset ordered aggregations are: Every size() milliseconds, compute the aggregate total for the last
 * size() milliseconds. This is equivalent in semantics to TimeWindows with an advance of 0.
 * <p>
 * This class differs from TimeWindows in that the windows for a given event is computed based on the current observed
 * stream time as opposed to the timestamp of the given record. This effectively makes the windows ordered with respect
 * to the offset of the record instead of the timestamp.
 */
public final class OffsetOrderedWindowsBatchedWindows extends Windows<TimeWindow> {
  
  /**
   * Returns the window size, measured in milliseconds.
   */
  public long size() { }

  /**
   * Returns 0, as offset ordered windows have no concept of "late arriving" records
   * because stream time is always monotonically increasing
   */
   public long gracePeriodMs() { return 0L; }

}

...

  • Instead of introducing a new window type, we could add a property like Strictness  on the existing TimeWindows  type. This would end up being more confusing than the proposed API since grace period do not apply to the type of windows suggested here.
  • Names we discussed but didn't like: GlobalWindow, StreamTimeWindow, ProcessTimeWindow, OffsetOrderedWindow.
  • Implementing a more general purpose batching mechanism that can batch based on number of records, wall clock or any other batching strategy. This is useful, but out of scope for this proposal as it introduces a different level of non-determinism that is not covered by the existing grace/windowing semantics. The way batching/suppression interrelate are also