Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate the problem: In order to manipulate large sets of complex objects as efficiently as today's database systems manipulate simple records, query-processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and postrelational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set-matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

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Query evaluation techniques for large databases

In this paper, a terminological framework is provided for describing different transactionoriented recovery schemes for database systems in a conceptual rather than an implementation-dependent way. By introducing the terms materialized database, propagation strategy, and checkpoint, we obtain a means for classifying arbitrary implementations from a unified viewpoint. This is complemented by a classification scheme for logging techniques, which are precisely defined by using the other terms. It is shown that these criteria are related to all relevant questions such as speed and scope of recovery and amount of redundant information required. The primary purpose of this paper, however, is to establish an adequate and precise terminology for a topic in which the confusion of concepts and implementational aspects still imposes a lot of problems.

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Principles of transaction-oriented database recovery

DB2TM, IMS, and TandemTM systems. ARIES is applicable not only to database management systems but also to persistent object-oriented languages, recoverable file systems and transaction-based operating systems. ARIES has been implemented, to varying degrees, in IBM's OS/2TM Extended Edition Database Manager, DB2, Workstation Data Save Facility/VM, Starburst and QuickSilver, and in the University of Wisconsin's EXODUS and Gamma database machine.

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ARIES: a transaction recovery method supporting fine-granularity locking and partial rollbacks using write-ahead logging

Distributed data processing is becoming a reality. Businesses want to do it for many reasons, and they often must do it in order to stay competitive. While much of the infrastructure for distributed data processing is already there (e.g., modern network technology), a number of issues make distributed data processing still a complex undertaking: (1) distributed systems can become very large, involving thousands of heterogeneous sites including PCs and mainframe server machines; (2) the state of a distributed system changes rapidly because the load of sites varies over time and new sites are added to the system; (3) legacy systems need to be integrated—such legacy systems usually have not been designed for distributed data processing and now need to interact with other (modern) systems in a distributed environment. This paper presents the state of the art of query processing for distributed database and information systems. The paper presents the “textbook” architecture for distributed query processing and a series of techniques that are particularly useful for distributed database systems. These techniques include special join techniques, techniques to exploit intraquery paralleli sm, techniques to reduce communication costs, and techniques to exploit caching and replication of data. Furthermore, the paper discusses different kinds of distributed systems such as client-server, middleware (multitier), and heterogeneous database systems, and shows how query processing works in these systems.

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The state of the art in distributed query processing

The purpose of this paper is to show that logic provides a convenient formalism for studying classical database problems. There are two main parts to the paper, devoted respectively to conventional databases and deductive databases. In the first part, we focus on query languages, integrity modeling and maintenance, query optimization, and data dependencies. The second part deals mainly with the representation and manipulation of deduced facts and incomplete information. Categories and Subject Descriptors: H.2.1 [Database Management]: Logical Design— data models; H.2.3 [Database Management]: Languages— query languages; H.2.4 [Database Management]: Systems— query processing General Terms: Deductive Databases, Indefinite Data, Logic and Databases, Null Values, Relational Databases

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Logic and Databases: A Deductive Approach

System R, an experimental database system, was constructed to demonstrate that the usability advantages of the relational data model can be realized in a system with the complete function and high performance required for everyday production use. This paper describes the three principal phases of the System R project and discusses some of the lessons learned from System R about the design of relational systems and database systems in general.

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A history and evaluation of System R

This article presents and discusses the computation and communication model used by R*, a prototype distributed database management system. An R* computation consists of a tree of processes connected by virtual circuit communication paths. The process management and communication protocols used by R* enable the system to provide reliable, distributed transactions while maintaining adequate levels of performance. Of particular interest is the use of processes in R* to retain user context from one transaction to another, in order to improve the system performance and recovery characteristics.

http://www.dlsi.ua.es/asignaturas/bdd/articulos%20recomendados/LindsayEtAl1984.pdf

Computation and communication in R*: a distributed database manager

R*: An overview of the architecture

R*: An Overview of the Architecture.

System R supports a high-level relational user language called SQL which may be used by ad hoc users at terminals or as an embedded data sublanguage in PL/I or COBOL. Host-language programs with embedded SQL statements are processed by the System R precompiler which replaces the SQL statements by calls to a machine-language access module. The precompilation approach removes much of the work of parsing, name binding, and access path selection from the path of a running program, enabling highly efficient support for repetitive transactions. Ad hoc queries are processed by a similar approach of name binding and access path selection which takes place on-line when the query is specified. By providing a flexible spectrum of binding times, System R permits transaction-oriented programs and ad hoc query users to share a database without loss of efficiency.System R is an experimental database management system designed and built by members of the IBM San Jose Research Laboratory as part of a research program on the relational model of data. This paper describes the architecture of System R, and gives some preliminary measurements of system performance in both the ad hoc query and the “canned program” environments.

Robert A. Yost

Papers

A history and evaluation of System R

Computation and communication in R*: a distributed database manager

R*: An overview of the architecture

R*: An Overview of the Architecture.

Support for repetitive transactions and ad hoc queries in System R