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@inproceedings{DBLP:conf/sigmod/GhandeharizadehDQ92,
author = {Shahram Ghandeharizadeh and
David J. DeWitt and
Waheed Qureshi},
editor = {Michael Stonebraker},
title = {A Performance Analysis of Alternative Multi-Attribute Declustering
Strategies},
booktitle = {Proceedings of the 1992 ACM SIGMOD International Conference on
Management of Data, San Diego, California, June 2-5, 1992},
publisher = {ACM Press},
year = {1992},
pages = {29-38},
ee = {http://doi.acm.org/10.1145/130283.130293, db/conf/sigmod/GhandeharizadehDQ92.html},
crossref = {DBLP:conf/sigmod/92},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
During the past decade, parallel database systems have gained increased popularity due to their high performance, scalability and availability characteristics. With the predicted future database sizes and the complexity of queries, the scalability of these systems to hundreds and thousands of processors is essential for satisfying the projected demand. Several studies have repeatedly demonstrated that both the performance and scalability of a parallel database system is contingent on the physical layout of data across the processors of the system. If the data is not declustered properly, the execution of an operator might waste resources, reducing the overall processing capability of the system.
With earlier, single attribute declustering strategies, such as those found in Tandem, Teradata, Gamma, and Bubba parallel database systems, a selection query including a range predicate on any attribute other than the partitioning attribute must be sent to all processors containing tuples of the relation. By directing a query with minimal resource requirements to processors that contain no relevant tuples, the system wastes CPU cycles, communication bandwidth, and I/O bandwidth, reducing its overall processing capability. As a solution, several multi-attribute declustering strategies have been proposed. However, the performance of these declustering techniques have not previously been compared to one another nor with a single attribute partitioning strategy. This paper, compares the performance of Multi-Attribute Grid deClustering (MAGIC) strategy and Bubba's Extended Range Declustering (BERD) strategy with one another and with the range partitioning strategy. Our results indicate that MAGIC outperforms both range and BERD in all experiments conducted in this study.
Copyright © 1992 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.
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,
SIGMOD Record 21(2),
June 1992