Sheldon J. Finkelstein

Physical database design for relational databases

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. DBDSGNs 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 DBDSGNs 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.

Magic is relevant

We define the magic-sets transformation for traditional relational systems (with duplicates, aggregation and grouping), as well as for relational systems extended with recursion. We compare the magic-sets rewriting to traditional optimization techniques for nonrecursive queries, and use performance experiments to argue that the magic-sets transformation is often a better optimization technique.

Magic conditions

Much recent work has focussed on the bottom-up evaluation of Datalog programs. One approach, called Magic-Sets, is based on rewriting a logic program so that bottom-up fixpoint evaluation of the program avoids generation of irrelevant facts ([BMSU86, BR87, Ram88]). It is widely believed that the principal application of the Magic-Sets technique is to restrict computation in recursive queries using equijoin predicates. We extend the Magic-Set transformation to use predicates other than equality (X > 10, for example). This Extended Magic-Set technique has practical utility in “real” relational databases, not only for recursive queries, but for non-recursive queries as well; in ([MFPR90]) we use the results in this paper and those in [MPR89] to define a magic-set transformation for relational databases supporting SQL and its extensions, going on to describe an implementation of magic in Starburst ([HFLP89]). We also give preliminary performance measurements. In extending Magic-Sets, we describe a natural generalization of the common class of bound (b) and free (ƒ) adornments. We also present a formalism to compare adornment classes.

A syntax and semantics for set-oriented production rules in relational database systems

We propose incorporating a production rules facility into a relational database system. Such a facility allows definition of database operations that are automatically executed whenever certain conditions are met. In keeping with the set-oriented approach of relational data manipulation languages, our production rules are also set-oriented—they are triggered by sets of changes to the database and may consequently perform sets of changes. The condition and action parts of our production rules may refer to the current state of the database as well as to the sets of changes triggering the rules. We define a syntax for production rule definition as an extension to SQL. A model of system behavior is used to give an exact semantics for production rule execution, taking into account externally-generated operations, self-triggering rules, and simultaneous triggering of multiple rules. Due to space constraints, some details and discussion are omitted, and only a few examples are included. See [19] for a more extensive description.

Algorithms and System Design in the Highly Available Systems Project

א Phd (Computer Engg.) University of North Carolina, Charlotte, NC, (GPA 4.0) [Fall 2004 to Current] VLSI System and Design, Fault Tolerant Systems, Digital System Test, Advanced Computer Architecture, Advanced VHDL, Physical Design Automation of VLSI Systems, Reconfigurable Computing Systems. א Bachelor of Engineering (B.E. Hons.) in Computer Science and Engineering Jadavpur University, Kolkata, India June 2001 (GPA 3.59) [Aug 1997 to Jun 2001] Introduction to C C++, Compiler Design, Graph Theory, IC Design, Computer Architecture.