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Parallel query processing for OLAP in grids
Author(s) -
Kotowski Nelson,
Lima Alexandre A. B.,
Pacitti Esther,
Valduriez Patrick,
Mattoso Marta
Publication year - 2008
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.1303
Subject(s) - online analytical processing , computer science , grid , grid file , benchmark (surveying) , database , speedup , query optimization , grid computing , middleware (distributed applications) , distributed computing , parallel computing , data warehouse , geometry , mathematics , geodesy , geography
OLAP query processing is critical for enterprise grids. Capitalizing on our experience with the ParGRES database cluster, we propose a middleware solution, GParGRES, which exploits database replication and inter‐ and intra‐query parallelism to efficiently support OLAP queries in a grid. GParGRES is designed as a wrapper that enables the use of ParGRES in PC clusters of a grid (in our case, Grid5000). Our approach has two levels of query splitting: grid‐level splitting, implemented by GParGRES, and node‐level splitting, implemented by ParGRES. GParGRES has been partially implemented as database grid services compatible with existing grid solutions such as the open grid service architecture and the Web services resource framework. We give preliminary experimental results obtained with two clusters of Grid5000 using queries of the TPC‐H Benchmark. The results show linear or almost linear speedup in query execution, as more nodes are added in all tested configurations. Copyright © 2008 John Wiley & Sons, Ltd.

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