The Enhancement of Semijoin Strategies in Distributed Query Optimization

We investigate the problem of optimizing distributed queries by using semijoins in order to minimize the amount of data communication between sites. The problem is reduced to that of finding an optimal semijoin sequence that locally fully reduces the relations referenced in a general query graph before processing the join operations. The optimization of general queries, in a distributed database system, is an important and challenging research issue. The problem is to determine a sequence of database operations which process the query while minimizing some predetermined cost function. Join is a frequently used database operation. It is also the most expensive, specifically in a distributed database system; it may involve large communication costs when the relations are located at different sites. Hence, instead of performing joins in one step, semijoins [1], are performed first to reduce the size of the relations so as to minimize the data transmission cost for processing queries [2]. In the next step, joins are performed on the reduced relations. as follows: (i) project R on the join attribute A (i.e. R(A)); (ii) Ship R(A) to the site containing S; (iii) Join S with R(A). The transmission cost of sending S to the site containing R for the join R ~n S can thus be reduced. There are two main methods to process a join operation between two relations. One is called the nondistributed join, where a join is performed between two unfragmented relations. The other is called the distributed join, where the join operation is performed between the fragments of relations. As pointed out in [5], the problem of query processing has been proved to be NP-hard. This fact justifies the necessity of resorting to heuristics. The remaining of this paper is organized as follows: preliminaries are given in Section 2. Section 3 defines the main characteristics of two semijoin-based query optimization heuristics; then, we present and discuss the join query optimization in a fragmented database. Finally, Section 5 concludes the paper.