Optimization Strategy of Top-Down Join Enumeration on Modern Multi-Core CPUs

Abstract

<p class="Abstract">Most contemporary database systems query optimizers exploit System-R’s bottom-up dynamic programming method (DP) to find the optimal query execution plan (QEP) without evaluating redundant sub-plans. The distinguished exceptions are Volcano/Cascades using transforms to generate new plans according to a top-down approach. As recent research has revealed, bottom-up dynamic programming can improve performance with respect to the shape of the join graph and parallelism. However top-down join enumeration dynamic programming method can derive upper bounds for the costs of the plans it generates which is not available to typical bottom-up DP method. In this paper, we propose a comprehensive and practical framework for parallelizing top-down dynamic programming query optimization with complex non-inner join in the multi-core processor architecture, referred as PTDhyp. We have implemented such a search strategy and experimental results show that can improve optimization time effective compared to known existing algorithms.</p&gt

Parallelizing query optimization on shared-nothing architectures

“…Our work connects to prior work that parallelizes the classical dynamic programming based query optimization algorithm [8,9,24,25,5,17]. Prior algorithms have however implicitly been designed for shared-memory architectures that do not scale beyond a certain degree of parallelism [19].…”