Energy Efficiency vs. Performance of Analytical Queries: The case of Bitmap Join Indexes
Issam Ghabri, Ladjel Bellatreche, Sadok Ben Yahia · 2021 IEEE International Conference on Big Data (Big Data) · 2021
Today’s common consensus is that the world’s most valuable resource is no longer oil but data. But, like oil, data is a source of pollution, mainly caused by the processing of extensive amounts of data. Thus, providers of data storage and processing solutions are at the heart of the debate on green computing. These solutions shall satisfy at the same time two conflictual non-functional requirements (NFRs): (i) the performance of analytical queries and (ii) the reduction of the energy consumption. These NFRs are strongly connected to query processors. Contrary to the first NFR, which has been widely studied by academia and industry, the second one does not get the same attention. The current works dealing with query processors’ energy efficiency (EE) are mainly focused on logical optimizations of database operations. However, nobody can deny that the satisfaction of the first NFR passes necessarily through physical optimizations such as indexes. Based on this discussion, we highly recommend the usage of green physical optimizations by existing and ongoing query processors. To promote and defend our vision of a green World, we propose in this paper to study the problem of selecting Bitmap Join Indexes that balance our NFRs. Because of its hardness, we first introduce a pruning strategy that eliminates non-relevant indexable attributes. Second, an approach for selecting indexes includes a hypergraph structure to manage the large search space of our problem and a Skyline operator to find the compromise between these NFRs. Third, we conduct intensive experiments to assess the impact of our proposal on our studied NFRs.