Efficient methods of processing unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches to improve the performance of query evaluation algorithms have been proposed: logic-based and semantic transformations, fast implementations of basic operations, and combinatorial or heuristic algorithms for generating alternative access plans and choosing among them. These methods are presented in the framework of a general query evaluation procedure using the relational calculus representation of queries. In addition, nonstandard query optimization issues such as higher level query evaluation, query optimization in distributed databases, and use of database machines are addressed. The focus, however, is on query optimization in centralized database systems.

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Query Optimization in Database Systems

Decision support systems (DSS) and data warehousing workloads comprise an increasing fraction of the database market today. I/O capacity and associated processing requirements for DSS workloads are increasing at a rapid rate, doubling roughly every nine to twelve months [38]. In response to this increasing storage and computational demand, we present a computer architecture for decision support database servers that utilizes “intelligent” disks (IDISKs). IDISKs utilize low-cost embedded general-purpose processing, main memory, and high-speed serial communication links on each disk. IDISKs are connected to each other via these serial links and high-speed crossbar switches, overcoming the I/O bus bottleneck of conventional systems. By off-loading computation from expensive desktop processors, IDISK systems may improve cost-performance. More importantly, the IDISK architecture allows the processing of the system to scale with increasing storage demand.

A case for intelligent disks (IDISKs)

New hardware is described which allows the rapid execution of queries demanding the joining of physically stored relations. The main feature of the hardware is a special store which can rapidly remember or recall data. This data might be pointers from one file to another, in which case the memory helps with queries on joins of files. Alternatively, the memory can help remove redundant data during projection, giving a considerable speed advantage over conventional hardware.

Implementing a relational database by means of specialzed hardware

This paper presents and analyzes algorithms for parallel processing of relational database operations in a general multiprocessor framework. To analyze alternative algorithms, we introduce an analysis methodology which incorporates I/O, CPU, and message costs and which can be adjusted to fit different multiprocessor architectures. Algorithms are presented and analyzed for sorting, projection, and join operations. While some of these algorithms have been presented and analyzed previously, we have generalized each in order to handle the case where the number of pages is significantly larger than the number of processors. In addition, we present and analyze algorithms for the parallel execution of update and aggregate operations.

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Parallel algorithms for the execution of relational database operations

The design of DIRECT, a multiprocessor organization for supporting relational database management systems is presented. DIRECT has a multiple-instruction multiple-data stream (MIMD) architecture. It can simultaneously support both intra-query and inter-query concurrency. The number of processors assigned to a query is dynamically determined by the priority of the query, the type and number of relational algebra operations it contains, and the size of the relations referenced. Since DIRECT iS a virtual memory machine, the maximum relation size is not limited to that of the associative memory as in some other database machines. Concurrent updates are controlled through the use of locks on relations which are maintained by a controlling processor.

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Direct&#8212;A Multiprocessor Organization for Supporting Relational Database Management Systems

The design and motivation for a rotating associative relational store (RARES) is described. RARES is designed to enhance the performance of an optimizing relational query interface by supporting important high level optimization techniques. In particular, it can perform tuple selection operations at the storage device and also can provide a mechanism for efficient sorting. Like other designs for rotating associative stores, RARES contains search logic which is attached to the heads of a rotating head-per-track storage device. RARES is distinct from other designs in that it utilizes a novel “orthogonal” storage layout. This layout allows a high output rate of selected tuples even when a sort order in the stored relation must be preserved. As in certain other designs, RARES can usually output a tuple as soon as it is found to satisfy the selection criteria. However, relative to these designs, the orthogonal layout allows an order of magnitude reduction in the capacity of storage local to the search logic.

Chyuan Shiun Lin

Papers

The design of a rotating associative memory for relational database applications