Constraint Processing techniques for improving join computation: A proof of concept

Constraint Processing and Database techniques overlap significantly. We discuss here the application of a constraint satisfaction technique, called dynamic bundling, to databases. We model the join query computation as a Constraint Satisfaction Problem (CSP) and solve it by search using dynamic bundling. First, we introduce a sort-based technique for computing dynamic bundling. Then, we describe the join algorithm that produces nested tuples. The resulting process yields a compact solution space and savings of memory, disk-space, and/or network bandwidth. We realize further savings by using bundling to reduce the number of join-condition checks. We place our bundling technique in the framework of the Progressive Merge Join (PMJ) [1] and use the XXL library [2] for implementing and testing our algorithm. PMJ assists in effective query-result-size prediction by producing early results. Our algorithm reinforces this feature of PMJ by producing the tuples as multiple solutions and is thus useful for improving size estimation.