Status

Current state: Under Discussion

...

Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).

Motivation

The DSL currently supports windowed aggregations for only two types of time-based window: hopping and tumbling. A third kind of window is defined, but only used in join operations: sliding windows. Users needing sliding window semantics can approximate them with hopping windows where the advance time is 1, but this workaround only artificially resembles the sliding window model; aggregates will be output for every defined hopping window, of which there will likely be a large number (specifically, the size of the window in milliseconds). The semantics for out-of-order data also differ, as sliding windows are inclusive at both ends (ie start and end time bounds)

...

final KTable<Windowed<String>, Long> counts = source
            .groupBy((key, value) -> value)
            .windowedBy(SlidingWindows.of(Duration.ofSeconds(5)))
            .count();

Proposed Changes 

---------WIP---------

The semantics of the sliding window based aggregations are as follows:

...

• Both time bounds are inclusive
• The "current" time is considered the window end time – so window start time < window end time
• Each record effectively triggers two output; one when it "enters" the window, and one when it "leaves" out the other side
• Out of order data that still falls within the current window triggers an update for every window that contains it 
• Out of order data that arrives outside the grace period is dropped

Compatibility, Deprecation, and Migration Plan

N/A

Rejected Alternatives

In general, many alternatives had to be rejected because the current aggregator can only apply a value to an aggregate; it can not be used to combine two aggregates. For example it might seem better to store the actual aggregate of each bucket rather than the "running aggregate" .. unfortunately we would have no way to combine them.

...