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Improving In-memory Column-Store Database Predicate Evaluation Performance on Multi-core Systems

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2 Author(s)
Hong Min ; IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA ; Franke, H.

The ability to analyze a large volume of data for the purpose of business intelligence has led to various innovations in database technology. One example is the increased interest of using column-oriented data layout to address query performance in analytical and warehousing workloads. As system architectures move towards multi-core designs, it is important to address optimizing performance for these workloads on these platforms. In this paper we present SPHINX, an architecture that utilizes multi-core systems for search-based predicate evaluation operations in analytical query workloads against in-memory column store. We discuss the natural parallelism of predicate evaluations and various bottlenecks that impact search performance. We present several performance improvement techniques and apply a scan sharing technique based on cache reuse efficiency to further improve the performance. We demonstrate the performance benefits of our scan sharing scheduler over other scheduling approaches in a workload of mixed search queries.

Published in:

Computer Architecture and High Performance Computing (SBAC-PAD), 2010 22nd International Symposium on

Date of Conference:

27-30 Oct. 2010

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