Rising energy costs in large data centers are driving an agenda for energy-efficient computing. In this paper, we focus on the role of database software in affecting, and, ultimately, improving the energy efficiency of a server. We first characterize the power-use profiles of database operators under different configuration parameters. We find that common database operations can exercise the full dynamic power range of a server, and that the CPU power consumption of different operators, for the same CPU utilization, can differ by as much as 60%. We also find that for these operations CPU power does not vary linearly with CPU utilization.We then experiment with several classes of database systems and storage managers, varying parameters that span from different query plans to compression algorithms and from physical layout to CPU frequency and operating system scheduling. Contrary to what recent work has suggested, we find that within a single node intended for use in scale-out (shared-nothing) architectures, the most energy-efficient configuration is typically the highest performing one. We explain under which circumstances this is not the case, and argue that these circumstances do not warrant a retargeting of database system optimization goals. Further, our results reveal opportunities for cross-node energy optimizations and point out directions for new scale-out architectures.

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Energy is a growing component of the operational cost for many "big data" deployments, and hence has become increasingly important for practitioners of large-scale data analysis who require scale-out clusters or parallel DBMS appliances. Although a number of recent studies have investigated the energy efficiency of DBMSs, none of these studies have looked at the architectural design space of energy-efficient parallel DBMS clusters. There are many challenges to increasing the energy efficiency of a DBMS cluster, including dealing with the inherent scaling inefficiency of parallel data processing, and choosing the appropriate energy-efficient hardware. In this paper, we experimentally examine and analyze a number of key parameters related to these challenges for designing energy-efficient database clusters. We explore the cluster design space using empirical results and propose a model that considers the key bottlenecks to energy efficiency in a parallel DBMS. This paper represents a key first step in designing energy-efficient database clusters, which is increasingly important given the trend toward parallel database appliances.

List intersection is a central operation, utilized excessively for query processing on text and databases. We present list intersection algorithms for an arbitrary number of sorted and unsorted lists tailored to the characteristics of modern hardware architectures. Two new list intersection algorithms are presented for sorted lists. The first algorithm, termed Dynamic Probes, dynamically decides the probing order on the lists exploiting information from previous probes at runtime. This information is utilized as a cache-resident microindex. The second algorithm, termed Quantile-based, deduces in advance a good probing order, thus avoiding the overhead of adaptivity and is based on detecting lists with non-uniform distribution of document identifiers. For unsorted lists, we present a novel hashbased algorithm that avoids the overhead of sorting.A detailed experimental evaluation is presented based on real and synthetic data using existing chip multiprocessor architectures with eight cores, validating the efficiency and efficacy of the proposed algorithms.