This paper presents a methodology for the optimization of parallel join execution. Past research on parallel join methods mostly focused on the design of algorithms for partitioning (e.g. hash) relations and distributing data buckets as evenly as possible to the processors. Once data is distributed to the processors, it assumes that all processors will complete their tasks at about the same time. We stress that this is true if no further information such as page-level join index is available. Otherwise, the join execution can be further optimized and the workload in the processors may still be unbalanced. We study such problems that may incur in a shared-nothing architecture environment and propose algorithms for the problems. Also, a simulation study is performed to understand the characteristics of the proposed method.

A methodology to resolve balancing and scheduling issues for parallel join execution in a shared-nothing multiprocessor environment are presented. In the past, research on parallel join methods focused on the design of algorithms for partitioning relations and distributing data buckets as evenly as possible to the processors. Once data are uniformly distributed to the processors, it is assumed that all processors will complete their tasks at about the same time. The authors stress that this is true if no further information, such as page-level join index, is available. Otherwise, the join execution can be further optimized and the workload in the processors may still be unbalanced. The authors study these problems in a shared-nothing environment.>