Next-Level Dynamic Queries Optimization: Smarter Joins, Faster Views

Abstract

In modern database systems, query workload optimization through materialized views is crucial for achieving high performance. This paper introduces a novel intelligent framework that identifies frequent subexpressions from SQL workloads, selects candidate materialized views, and predicts their potential benefits using a Deep Neural Network (DNN) model.Unlike existing static heuristic methods, our approach adopts a dynamically learning mechanism with proper determination of properties required for effective views in line with distributed workloads and predicate normalization. By excluding an extra step needed in choosing views or query rewriting, query execution is reduced with the proposed framework, as illustrated in existing experimental results of proposed methods in comparison with existing ones.