Parallel Optimization of Large Join Queries with Set Operators and Aggregates in a Parallel Environment Supporting Pipeline
Issue No. 03 - June (1996 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/69.506710
<p><b>Abstract</b>—We propose a parallel optimizer for queries containing a large number of joins, as well as set operators and aggregate functions. The platform of execution is a shared-disk multiprocessor machine supporting bushy parallelism and pipeline. Our model partitions the query into almost independent subtrees that can be optimized simultaneously and applies an enhanced variation of the iterative improvement technique on those of the subtrees, which contain a large number of joins. This technique is parallelized, too. In order to estimate the cost of the states constructed during optimization of join subtrees, cost formulae are developed that estimate the cost of relational algebra operators when executed across coalescing pipes.</p>
Parallel query optimization, parallelism in optimization, iterative improvement, large join queries, bushy parallelism, pipeline, shared-disk architectures, query optimization, parallelism, databases.
Y. Cotronis, M. Spiliopoulou and M. Hatzopoulos, "Parallel Optimization of Large Join Queries with Set Operators and Aggregates in a Parallel Environment Supporting Pipeline," in IEEE Transactions on Knowledge & Data Engineering, vol. 8, no. , pp. 429-445, 1996.