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Towards materialized view selection for distributed databases
Zitatschlüssel DBLP:conf/edbt/ChavesBHB09
Autor Leonardo Weiss Ferreira Chaves, Erik Buchmann, Fabian Hueske, Klemens Böhm
Seiten 1088-1099
Jahr 2009
DOI http://dx.doi.org/10.1145/1516360.1516484
Journal Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology (EDBT 09)
Zusammenfassung Materialized views (MV) can significantly improve the query performance of relational databases. In this paper, we con- sider MVs to optimize complex scenarios where many het- erogeneous nodes with different resource constraints (e.g., CPU, IO and network bandwidth) query and update nu- merous tables on different nodes. Such problems are typical for large enterprises, e.g., global retailers storing thousands of relations on hundreds of nodes at different subsidiaries. Choosing which views to materialize in a distributed, com- plex scenario is NP-hard. Furthermore, the solution space is huge, and the large number of input factors results in non-monotonic cost models. This prohibits the straightfor- ward use of brute-force algorithms, greedy approaches or proposals from organic computing. For the same reason, all solutions for choosing MVs we are aware of do not consider either distributed settings or update costs. In this paper we describe an algorithmic framework which restricts the sets of considered MVs so that a genetic algo- rithm can be applied. In order to let the genetic algorithm converge quickly, we generate initial populations based on knowledge on database tuning, and devise a selection func- tion which restricts the solution space by taking the simi- larity of MV configurations into account. We evaluate our approach both with artificial settings and a real-world RFID scenario from retail. For a small setting consisting of 24 ta- bles distributed over 9 nodes, an exhaustive search needs 10 hours processing time. Our approach derives a compa- rable set of MVs within 30 seconds. Our approach scales well: Within 15 minutes it chooses a set of MVs for a real- world scenario consisting of 1,000 relations, 400 hosts, and a workload of 3,000 queries and updates.
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