View and index selection for query-performance improvement: quality-centered algorithms and heuristics

Authors:
Maxim Kormilitsin;Rada Chirkova;Yahya Fathi;Matthias Stallmann
Affiliations:
NC State University, Raleigh, NC, USA;NC State University, Raleigh, NC, USA;NC State University, Raleigh, NC, USA;NC State University, Raleigh, NC, USA
Venue:
Proceedings of the 17th ACM conference on Information and knowledge management
Year:
2008

Citing 3
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Abstract

Selecting and precomputing indexes and materialized views, with the goal of improving query-processing performance, is an important part of database-performance tuning. The significant complexity of the view- and index-selection problem may result in high total cost of ownership for database systems. In this paper, we develop efficient methods that deliver user-specified quality of the set of selected views and indexes when given view- and index-based plans as problem inputs. Here, quality means proximity to the globally optimum performance for the input query workload given the input query plans. Our experimental results and comparisons on synthetic and benchmark instances demonstrate the competitiveness of our approach and show that it provides a winning combination with end-to-end view- and index-selection frameworks such as those of [1, 2].