An Adaptive Parallel Distributive Join Algorithm on a Cluster of Workstations

Authors:
Soon M. Chung;Arindam Chatterjee
Affiliations:
Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435 schung@cs.wright.edu;Microsoft Corporation, One Microsoft Way, Redmond, WA 98052-6399 arindamc@microsoft.com
Venue:
The Journal of Supercomputing
Year:
2002

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Abstract

In this paper, we present an adaptive version of the parallel Distributive Join (DJ) algorithm that we proposed in [5]. The adaptive parallel DJ algorithm can handle the data skew in operand relations efficiently. We implemented the original and adaptive parallel DJ algorithms on a network of Alpha workstations using the Parallel Virtual Machine (PVM). We analyzed the performance of the algorithms, and compared it with that of the parallel Hybrid-Hash (HH) join algorithms. Our results show that the parallel DJ algorithms perform comparably with the parallel HH join algorithms over the entire range of the number of processors used and for different join selectivities. A significant advantage of the parallel DJ algorithms is that they can easily support non-equijoin operations.