Approaches to Model Query Interactions

Abstract

A typical database workload consists of several query instances of

different query types running concurrently. The execution of each query may interact with the execution of the other queries. It is well known that such interactions can have a significant impact on database system performance. In this article we propose three new approaches to model and measure query instance and query type interactions. Our approaches require no prior assumptions about the internal aspects of the database system, making it non intrusive, namely, portable across systems. Furthermore, to demonstrate the profit of exploiting query interactions, we have developed a novel interaction-aware query scheduler for online workloads, called Intelligent Scheduler for Multiple-query Execution Ordering (ISO, for short). In order to verify the efficiency of the proposed approaches for measuring query interaction and of IOS, an experimental evaluation using TPC-H workloads running on PostgreSQL has been done. The results show that the proposed approach has potential to improve the efficiency of database tuning tools.