No abstract

In relational database model, the use of exhaustive search methods in the large join query optimization is prohibitive because of the exponential increase of search space. An alternative widely discussed is the use of randomized search techniques. Several previous researches have been showed that the use of randomized sampling in query optimization permits to find, in average, near optimal plans in polynomial time. However, due to their random components, the quality of yielded plans for the same query may vary a lot, making the response time of a submitted query unpredictable. On the other hand, the use of heuristic optimization may increase stability of response time. This characteristic is essential in environments where response time must be predicted. In this paper, we will compare a randomized algorithm and a heuristic algorithm applied to large join query optimization. We used an open source DBMS as experimental framework and we compared the quality and stability of these algorithms.

Cloud computing environments are attractive for IT service provision as they allow for greater flexibility and rationalization of IT infrastructure. In an attempt to benefit from these environments, IT professionals are incorporating legacy Relational Database Management Systems (RDBMSs) in them. However, the design of these legacy systems do not account to the changes in resource availability, present in cloud environments. This work evaluates the use of rules of thumb in RDBMS configuration. Through an evaluation method that simulates concurrent I/O workloads, we analyzed the RDBMS performance under various settings. The results show that well-known configuration rules are inefficient in these environments and that new definitions are necessary to harvest the benefits of cloud computing environments.

In relational database systems the optimization of select-project-join queries is a combinatorial problem. The use of exhaustive search methods is prohibitive because of the exponential increase of the search space. Randomized searches are used to find near optimal plans in polynomial time. In this paper, we investigate the large join query optimization (LJQO) problem by extending randomized algorithms and implementing a 2PO algorithm as a query optimizer in a popular open-source DBMS. We compare our solution with an implementation of a genetic algorithm. Through a multidimensional test schema, we discuss pros and cons about the behavior of these algorithms. Our results show that 2PO algorithm is fast to run and the costs of generated plans are better in most cases when compared to those of the genetic algorithms. * Work partially funded by the Datluge CNPq-INRIA project.