Optimization of Conjunctive Predicates for Main Memory Column Stores

Optimization of queries with conjunctive predicates for main memory databases remains a challenging task. The traditional way of optimizing this class of queries relies on predicate ordering based on selectivities or ranks. However, the optimization of queries with conjunctive predicates is a much more challenging task, requiring a holistic approach in view of (1) an accurate cost model that is aware of CPU architectural characteristics such as branch (mis)prediction, (2) a storage layer, allowing for a streamlined query execution, (3) a common subexpression elimination technique, minimizing column access costs, and (4) an optimization algorithm able to pick the optimal plan even in presence of a small (bounded) estimation error. In this work, we embrace the holistic approach, and show its superiority experimentally. Current approaches typically base their optimization algorithms on at least one of two assumptions: (1) the predicate selectivities are assumed to be independent, (2) the predicate costs are assumed to be constant. Our approach is not based on these assumptions, as they in general do not hold.