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Sliding-window aggregation is a widely-used approach for extracting insightsfrom the most recent portion of a data stream. The aggregations of interest canusually be expressed as binary operators that are associative but notnecessarily commutative nor invertible. Non-invertible operators, however, aredifficult to support efficiently. In a 2017 conference paper, we introducedDABA, the first algorithm for sliding-window aggregation with worst-caseconstant time. Before DABA, if a window had size $n$, the best publishedalgorithms would require $O(\log n)$ aggregation steps per windowoperation---and while for strictly in-order streams, this bound could beimproved to $O(1)$ aggregation steps on average, it was not known how toachieve an $O(1)$ bound for the worst-case, which is critical forlatency-sensitive applications.  This article is an extended version of our 2017 paper. Besides describingDABA in more detail, this article introduces a new variant, DABA Lite, whichachieves the same time bounds in less memory. Whereas DABA requires space forstoring $2n$ partial aggregates, DABA Lite only requires space for $n+2$partial aggregates. Our experiments on synthetic and real data support thetheoretical findings.

In-Order Sliding-Window Aggregation in Worst-Case Constant Time