Unraveling the Impact of Window Semantics: Optimizing Join Order for Efficient Stream Processing
Window joins (WJs) are fundamental operators in stream process ing systems (SPSs), enabling continuous, time-aware joins over unbounded data streams. Unlike time-agnostic relational joins, WJs incorporate temporal semantics associated with different window types (i.e., sliding, session, and interval windows), which introduce uncertainty in algebraic properties such as commutativity and as sociativity. As a result, state-of-the-art SPSs exploit only a single, f ixed join order, which limits optimization opportunities and often leads to suboptimal performance. In this work, we eliminate this restriction by introducing three transformation rules that enable WJ reordering while preserving query semantics for those window types. Based on them, we propose WJR, an algorithm that system atically enumerates semantically equivalent join orders, expanding the search space for finding efficient WJ execution plans. Our eval uation shows speedups of up to 10 for multi-way WJ queries under various window configurations and rate ratios, highlighting the per formance benefits of flexible join reordering in streaming queries.
