An Adaptive Parallel Distributive Join Algorithm on a Cluster of Workstations

摘要

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 what of the parallel Hybrid-Hash (HH) join algorithms. Our results Show that the parallel DJ algorithm perform comparably with the parallel HH join algorithms over the Entire range of the number of processors used and for different joint selectivities. A significant advantage Of the parallel DJ algorithms is that they can easily support non-equijoin operations.