In-order sliding-window aggregation in worst-case constant time

Sliding-window aggregation is a widely-used approach for extracting insights from the most recent portion of a data stream. While aggregations of interest can usually be expressed as binary operators that are associative, they are not necessarily commutative nor invertible. Non-invertible operators, however, are difficult to support efficiently. DABA is the first algorithm for sliding-window aggregation with worst-case constant time. Prior to DABA, the best published algorithms would require O(log n) aggregation steps per window operation for a window of size n —and while for strictly in-order streams, this bound could be improved to O (1) aggregation steps in the amortized sense, it was not known how to achieve an O (1) bound in the worst case, which is critical for latency-sensitive applications. In this article, besides describing DABA in more detail, we introduce a new variant, DABA Lite, which achieves the same time bounds in less memory. Whereas DABA requires space for storing 2 n partial aggregates, DABA Lite only requires space for n+2 partial aggregates. Our experiments on synthetic and real data support the theoretical findings.