This paper describes the concepts used in the implementation of DBDSGN, an experimental physical design tool for relational databases developed at the IBM San Jose Research Laboratory. Given a workload for System R (consisting of a set of SQL statements and their execution frequencies), DBDSGN suggests physical configurations for efficient performance. Each configuration consists of a set of indices and an ordering for each table. Workload statements are evaluated only for atomic configurations of indices, which have only one index per table. Costs for any configuration can be obtained from those of the atomic configurations. DBDSGN uses information supplied by the System R optimizer both to determine which columns might be worth indexing and to obtain estimates of the cost of executing statements in different configurations. The tool finds efficient solutions to the index-selection problem; if we assume the cost estimates supplied by the optimizer are the actual execution costs, it finds the optimal solution. Optionally, heuristics can be used to reduce execution time. The approach taken by DBDSGN in solving the index-selection problem for multiple-table statements significantly reduces the complexity of the problem. DBDSGN’s principles were used in the Relational Design Tool (RDT), an IBM product based on DBDSGN, which performs design for SQL/DS, a relational system based on System R. System R actually uses DBDSGN’s suggested solutions as the tool expects because cost estimates and other necessary information can be obtained from System R using a new SQL statement, the EXPLAIN statement. This illustrates how a system can export a model of its internal assumptions and behavior so that other systems (such as tools) can share this model.

We present techniques for optimizing queries in memory-resident database systems. Optimization techniques in memory-resident database systems differ significantly from those in conventional disk-resident database systems. In this paper we address the following aspects of query optimization in such systems and present specific solutions for them: (1) a new approach to developing a CPU-intensive cost model; (2) new optimization strategies for main-memory query processing; (3) new insight into join algorithms and access structures that take advantage of memory residency of data; and (4) the effect of the operating system’s scheduling algorithm on the memory-residency assumption. We present an interesting result that a major cost of processing queries in memory-resident database systems is incurred by evaluation of predicates. We discuss optimization techniques using the Office-by-Example (OBE) that has been under development at IBM Research. We also present the results of performance measurements, which prove to be excellent in the current state of the art. Despite recent work on memory-resident database systems, query optimization aspects in these systems have not been well studied. We believe this paper opens the issues of query optimization in memory-resident database systems and presents practical solutions to them.

Abstract- A theoretical approach to the optimal design of a large multifile'physical database is presented. The design algorithm is based on the theory that, given a set of join methods that satisfy a certain property called separability, the problem of optimal assignment of access structures to the whole database can be reduced to the subproblem of optimizing individual relations independently of one another. Coupling factors are defined to represent all the interactions among the relations. This approach not only reduces the complexity of the problem significantly, but also provides a better understanding of underlying mechanisms. Index Terms-Block accesses, index selection, join methods, physical database design, query optimization, selectivity. I.

The query reformulation approach (also called semantic query optimization) takes advantage of the semantic knowledge about the contents of databases for optimization. The basic idea is to use the knowledge to reformulate a query into a less expensive yet equivalent query. Previous work on semantic query optimization has shown the cost reduction that can be achieved by reformulation, we further point out that when applied to distributed multidatabase queries, the reformulation approach can reduce the cost of moving intermediate data from one site to another. However, a robust and efficient method to discover the required knowledge has not yet been developed. This paper presents an example-guided, data-driven learning approach to acquire the knowledge needed in reformulation. We use example queries to guide the learning to capture the database usage pattern. In contrast to the heuristic-driven approach proposed by Siegel, the data-driven approach is more likely to learn the re...

This work was supported by the Defense Advanced Research Project Agency under the KBMS Project, Contract N39-80-G-0132. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific