Neoklis Polyzotis
ABSTRACT
Modern query optimizers select an efficient join ordering for a physical execution plan based essentially on the average join selectivity factors among the referenced tables. In this paper, we argue that this "monolithic" approach can miss important opportunities for the effective optimization of relational queries. We propose selectivity-based partitioning, a novel optimization paradigm that takes into account the join correlations among relation fragments in order to essentially enable multiple (and more effective) join orders for the evaluation of a single query. In a nutshell, the basic idea is to carefully partition a relation according to the selectivities of the join operations, and subsequently rewrite the query as a union of constituent queries over the computed partitions. We provide a formal definition of the related optimization problem and derive properties that characterize the set of optimal solutions. Based on our analysis, we develop a heuristic algorithm for computing efficiently an effective partitioning of the input query. Results from a preliminary experimental study verify the effectiveness of the proposed approach and demonstrate its potential as an effective optimization technique.
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Index Terms
- Selectivity-based partitioning: a divide-and-union paradigm for effective query optimization
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