Object/relational query optimization with chase and backchase

OBJECT/RELATIONAL QUERY OPTIMIZATION WITH CHASE AND BACKCHASE Lucian Popa Supervisor: Val Tannen Traditionally, query optimizers assume a direct mapping from the logical entities modeling the data (e.g. relations) and the physical entities storing the data (e.g. indexes), each physical entity corresponding precisely to one logical entity. This assumption is no longer true in non-traditional applications (object-oriented and semi-structured databases, data integration), which often exhibit a mismatch between the logical view and the actual storage of data. In addition, there is an increased amount of redundancy, even at the logical level, that can greatly enhance optimization opportunities, if exploited. To deal with all this, we propose a novel architecture for query optimization, in which physical optimization is leveraged at the level of query rewriting. As a consequence, the other important aspect of query optimization, semantic optimization (that takes advantage of the redundancy at the logical level), can be naturally incorporated. The optimizer can then make global decisions based on both semantic and physical knowledge, leading to plans of higher quality than those obtainable by a traditional two-level approach. The main idea is to describe the relationship between physical and logical schemas by constraints, with the same syntactic form as the semantic constraints describing the logical schema. Many physical structures such as indexes, materialized views, access support relations, GMAPs, etc. can be captured in this way. The search space for query plans is then de ned and enumerated in a novel way: First, the input query is rewritten by chase with constraints into a "universal" plan that integrates all the relevant physical and logical structures. In a second phase (backchase), minimal plans are produced by eliminating, exhaustively, the various combinations of redundancies from the universal plan.