A Study Of Multi-join Query Optimization Algorithm In Distributed Database

As the distributed database is widely used in all walks of life, the correspondingquery processing operations become more complex. The data query has been an importantfactor that affects the performance of database. The ability to effectively conduct queryprocessing operations depends largely on the merits of distributed query optimizationalgorithm. In this paper, proposed improved optimization algorithms based on the analysisand summarization about domestic and overseas query optimization algorithms.Firstly, through studying the ordinary semi-join query optimization algorithm,proposed an improved algorithm that contraposes the shortcoming of sequential executionof semi-join operations. After multi-join is composed into small cost tree structure, makeuse of simple semi-join operations to extremely reduce the relationship at the root fromthe leaves to root node. Then reduce the other relationships using PERF bit vector basedon the most contracted root node. Last step is to recycle the reduced relationships to jointogether. Algorithm theory analysis shows that the improved semi-join query optimizationalgorithm can reduce the amount of intermediate results, decrease query cost.Secondly, the performance of optimizing complex multi-join query based on geneticsimulated annealing algorithm is unstable, so propose an improved query optimizationalgorithm. The new algorithm makes use of a niche technology to expand the explorationarea of genetic simulated annealing algorithm, so as to prevent premature phenomenon.Then simplify Meteopolis rule to eliminate the functional redundancy which is resulted inby the introduction of new technology. At last, the new hybrid algorithm is reasonablyapplied to explore best distributed query strategy, achieve query optimization. Algorithmtheory analysis shows that the improved distributed query optimization algorithm cansteadily obtain the optimal query strategy, reduce the cost of distributed database queriesand improve query efficiency.Finally, the empirical study verifying the feasibility and effectiveness of the proposedtwo improved query optimization algorithm, and the experimental results are analyzed.