Reusing invariants: a new strategy for correlated queries

Correlated queries are very common and important in decision support systems. Traditional nested iteration evaluation methods for such queries can be very time consuming. When they apply, query rewriting techniques have been shown to be much more efficient. But query rewriting is not always possible. When query rewriting does not apply, can we do something better than the traditional nested iteration methods? In this paper, we propose a new invariant technique to evaluate correlated queries efficiently. The basic idea is to recognize the part of the subquery that is not related to the outer references and cache the result of that part after its first execution. Later, we can reuse the result and combine it with the result of the rest of the subquery that is changing for each iteration. Our technique applies to arbitrary correlated subqueries.

This paper introduces algorithms to recognize the invariant part of a data flow tree, and to restructure the evaluation plan to reuse the stored intermediate result. We also propose an efficient method to teach an existing join optimizer to understand the invariant feature and thus allow it to be able to generate better join plans in the new context. Some other related optimization techniques are also discussed. The proposed techniques were implemented within three months on an existing real commercial database system.

We also experimentally evaluate our proposed technique. Our evaluation indicates that, when query rewriting is not possible, the invariant technique is significantly better than the traditional nested iteration method. Even when query rewriting applies, the invariant technique is sometimes better than the query rewriting technique. Our conclusion is that the invariant technique should be considered as one of the alternatives in evaluating correlated queries since it fills the gap left by rewriting techniques.