Automatic Physical Design Tuning based on Hypothetical Plans

Abstract

It is well-known that fine tuning in database physical design is an important strategy for speeding up data access. In this paper, we introduce a new approach, denoted HypoPlans, to make relational database systems able to execute self-tuning actions, based on the notion of Hypothetical Query Execution Plans. HypoPlans is non-intrusive and completely autonomous. In this sense, it is DBMS-independent and does not require any DBA intervention. Our approach is based on heuristics that run continuously. Thus, HypoPlans is able to guide decisions on the current physical database configuration in order to dynamically react to workload changes. More specifically, we present in this paper the software architecture of a framework, which implements HypoPlans. In order to evaluate the viability of our approach, we have instantiated this framework for the database physical design concerning index (self)tuning. Our experiments show that HypoPlans is quite effective and efficient, also presenting low resource consumption.