Research And Implementation Of Query Optimization In Distributed Database

Based on the research of distributed database query optimization,this paper proposes some algorithms to speed up the query processing of distributed database system.Firstly the paper explains the basic concept of distributed database system,including the distributed database systems' s definition,classification,pattern and architecture,as well as the advantages and disadvantages of the distributed database system.The third chapter introduces the content of query optimization in distributed database system,including the goal of query optimization in distributed database system,the hierarchical structure of the distributed query processing and the function of each layer structure proposed above.Then this article introduces two kinds of query optimization algorithms commonly used: query optimization algorithm based on fulljoin and query optimization algorithm based on semi-join.Reducing the number of the tuples in the relation involved in the join operation is the main idea of the semijoin algorithm.The semi-join algorithm is suitable for the wide-area network.In this paper,an improved algorithm is proposed to solve the problem that semi join operation in SDD-1 algorithm can not be executed parallelly.The improved algorithm first adds some redundant join expression to the query graph according to the connection property between the relations,and the new query graph with redundant conditions is called QGq+.Finding all segmentation points in the graph in figure QGq+,and using the Breadth-First-Search algorithm generates each query block with the information of all segmentation in the query graph.Then using Kursal algorithm generates minimum spanning tree for each query block.Using the SDD-1 algorithm to reduce the number of relation tuples in the minimum spanning tree for each query block.The multiple query blocks can be executed in parallel.he shortcoming that the SDD-1 algorithm can only be carried out in single step before the partition of query graph can be solved,which significantly reduces the cost of the distributed system and speeds up the query response time.Finally,the test results show that the algorithm can significantly reduce the amount of intermediate result data,effectively reduce the total cost of network communication,and improve the efficiency of optimization.