Selecting Subexpressions to Materialize for Dynamic Large-Scale Workloads.

The nature of analytical queries executed either inside or outside of a DBMS increases the redundant computations due to the presence of common query sub-expressions. More recently, a few largely industry-led studies have focussed on the problem of identifying beneficial sub-expressions for large-scale workloads running outside of a DBMS for materialization purposes. However, these works have unfortunately ignored the large-scale workloads running inside of a DBMS. To align them in terms of the materialization of beneficial sub-expressions for dynamic large-scale workloads, we propose a pro-active approach that uses hypergraphs. These structures exploit cost models towards capturing the common query sub-expressions and materializing the most beneficial ones. Our approach is accompanied by a strategy, which orients the first \(\delta \) queries to the offline phase that selects their appropriate views. To augment the benefit and sharing of the selected views, the initial \(\delta \) queries may be scheduled. The online phase manages the pool of views obtained by the first phase by adding/dropping views to optimize new incoming queries. We conducted extensive experiments to evaluate the efficiency of our proposal as well as its cost-effective integration in a commercial DBMS.