General Dynamic Yannakakis: Conjunctive Queries with Theta Joins under Updates

Abstract

The ability to efficiently analyze changing data is a key requirement of many real-time analytics applications. In prior work, we have proposed general dynamic Yannakakis (GDyn), a general framework for dynamically processing acyclic conjunctive queries with theta-joins in the presence of data updates. Whereas traditional approaches face a trade-off between materialization of subresults (to avoid inefficient recomputation) and recomputation of subresults (to avoid the potentially large space overhead of materialization), GDyn is able to avoid this trade-off. It intelligently maintains a succinct data structure that supports efficient maintenance under updates and from which the full query result can quickly be enumerated. In this paper, we consolidate and extend the development of GDyn. First, we give full formal proof of GDyn 's correctness and complexity. Second, we present a novel algorithm for computing GDyn query plans. Finally, we instantiate GDyn to the case where all theta-joins are inequalities and present extended experimental comparison against state-of-the-art engines. Our approach performs consistently better than the competitor systems with multiple orders of magnitude improvements in both time and memory consumption.