Issue No. 03 - June (1996 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/69.506709
<p><b>Abstract</b>—In this paper, we study the subject of exploiting interoperator parallelism to optimize the execution of multi-join queries. Specifically, we focus on two major issues: 1) scheduling the execution sequence of multiple joins within a query, and 2) determining the number of processors to be allocated for the execution of each join operation obtained in 1). For the first issue, we propose and evaluate by simulation several methods to determine the general join sequences, or bushy trees. Despite their simplicity, the heuristics proposed can lead to the general join sequences that significantly outperform the optimal sequential join sequence. The quality of the join sequences obtained by the proposed heuristics is shown to be fairly close to that of the optimal one. For the second issue, it is shown that the processor allocation for exploiting interoperator parallelism is subject to more constraints—such as execution dependency and system fragmentation—than those in the study of intraoperator parallelism for a single join. The concept of synchronous execution time is proposed to alleviate these constraints. Several heuristics to deal with the processor allocation, categorized by bottom-up and top-down approaches, are derived and are evaluated by simulation. The relationship between issues 1) and 2) is explored. Among all the schemes evaluated, the two-step approach proposed, which first applies the join sequence heuristic to build a bushy tree as if under a single processor system, and then, in light of the concept of synchronous execution time, allocates processors to execute each join in the bushy tree in a top-down manner, emerges as the best solution to minimize the query execution time.</p>
Bushy trees, synchronous execution time, multi-join query, execution dependency, system fragmentation.
K. Wu, P. S. Yu and M. Chen, "Optimization of Parallel Execution for Multi-Join Queries," in IEEE Transactions on Knowledge & Data Engineering, vol. 8, no. , pp. 416-428, 1996.