The OO7 Benchmark represents a comprehensive test of OODBMS performance. In this report we describe the benchmark and present performance results from its implementation in four OODB systems. It is our hope that the OO7 Benchmark will provide useful insight for end-users evaluating the performance of OODB systems; we also hope that the research community will nd that OO7 provides a database schema, instance, and workload that is useful for evaluating new techniques and algorithms for OODBMS implementation.

Pointer swizzling is the conversion of database objects between an external form (object identifiers) and an internal form (direct memory pointers). Swizzling is used in some object-oriented databases, persistent object stores, and persistent and database programming language implementations to speed manipulation of memory resident data. Here we describe a simplifying model of application behavior, revealing those aspects where swizzling is most relevant in both benefits and costs. The model has a number of parameters, whichwehavemeasured for a particular instance of the Mneme persistent object store, varying the swizzling technique used. The results confirm most of the intuitive, qualitative tradeoffs, with the quantitative data showing that some performance differences between schemes are smaller than might be expected. However, there are some interesting effects that run counter to naive intuition, most of which we explain using deeper analysis of the algorithms and data struc...

Recent developments in software and hardware changed the way database systems are built and operate. In this paper we present database architectures based on the Client-Server paradigm and study their performance and scalability under different query/update workloads. The architectures are: Standard Client-Server, Client-Server with Multiple Disks, and Enhanced Client{Server. Data replication and client query result caching are used as the main mechanisms to improve the query throughput. The role of the server is to maintain system-wide data consistency and in the case of Enhanced Client-Server to selectively propagate updates on demand. Our study shows that except for the case of mostly update workloads, the Standard Client-Server architecture is outperformed by the other two architectures by one or more orders of magnitude. The Client-Server with Multiple Disks architecture offers performance comparable to that achieved by the Enhanced Client-Server for up to 100 clients, but the latter scales up a lot better for higher number of clients.

The OO7 Benchmark was first published in 1993, and has since found a home in the marketing literature of various object-oriented database management system (OODBMS) vendors. The OO7 Benchmark (as published) was the initial result of an ongoing OODBMS performance evaluation effort at the University of Wisconsin. This paper provides an update on the status of the effort on two fronts: single-user and multi-user. On the single-user front, we review and critique the design of the initial OO7 Benchmark. We discuss some of its faults, the reasons for those faults, and things that might be done to correct them. On the multi-user front, we describe our current work on the development of a multi-user benchmark for OODBMSs. This effort includes changes and extensions to the OO7 database and the design of a family of interesting multi-user workloads.

The introduction of powerful workstations connected through LAN networks inspired new DBMS architectures which offer high performance characteristics. In this paper, we examine three such software architecture configurations, namely: Client-Server (CS), RAD-UNIFY 1 type of DBMS (RU) and Enhanced ClientServer (ECS). Their specific functional components and design rationales are discussed. We use three simulation models to provide a performance comparison under different job workloads. Our simulation results show that the RU almost always performs slightly better than the CS especially under light workloads and that ECS offers significant performance improvement over both CS and RU. Under reasonable update rates, the ECS over CS (or RU) performance ratio is almost proportional to the number of participating clients (for less than 32 clients). We also examine the impact of certain key parameters on the performance of the three architectures and finally show that ECS is more ...

ABSTRACT- In this paper we describe the design, implemen-tation, and performance of an incremental join facility that has been added as an extension to the Starburst extensible DBMS. This facility provides an efficient access path for joins that materialize many-to-one relationships, and it works by maintain-ing hidden pointer fields embedded in related tuples. The facility was constructed for two reasons: as an experiment in using pointers in the internals of a relational DBMS, and as a stress-test of the Starburst extension architecture. In addition to describing the join facility and its performance, we also summarize what it taught us about extensibility both in Starburst and in general. 1.

One of the approaches for integrating object-oriented programs with databases is to instantiate objects from relational databases by evaluating view queries. In that approach, it is often necessary to evaluate some joins of the query by left outer joins to prevent information loss caused by the tuples discarded by inner joins. It is also necessary to filter some relations with selection conditions to prevent the retrieval of unwanted nulls. The system should automatically prescribe joins as inner or left outer joins and generate the filters, rather than letting them be specified manually for every view definition. We develop such a mechanism in this paper. We first develop a rigorous system model to facilitate the mapping between an object-oriented model and the relational model. The system model provides a well-defined context for developing a simple mechanism. The mechanism requires only one piece of information from users: null options on an object attribute. The semantics of these ...

. The majority of database benchmarks currently in use in the industry were designed for relational databases. A different class of benchmarks became required for object oriented databases once they appeared on the market. None of the currently existing benchmarks were designed to adequately exploit the distinctive features native to the semantic databases. A new semantic benchmark is proposed which allows evaluation of the performance of the features characteristic of semantic database applications. An application used in the benchmark represents a class of problems requiring databases with sparse data, complex inheritances and many-to-many relations. Such databases can be naturally accommodated by semantic databases. A predefined implementation is not enforced allowing a designer to choose the most efficient structures available in the DBMS tested. The second part of this paper compares the performance of Sem-ODB binary semantic database vs. one of the leading relationa...

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The deployment of efficient information systems has become a necessity for the success of any corporate or government operation. Critical system requirements that include maintenance of up-todate information, achievement of short transaction response times, utilization of networked computing resources, and handling of massive data volumes have resulted in the evolution of several specialized architectures for database management. Three families of such architectures that have been specifically developed to address the above requirements are: main-memory databases, client-server database and information systems, and parallel databases. By exploiting available system resources and workload characteristics, these architectures seek to optimize database processing for diverse application settings. In this chapter, we discuss the constraints as well as the implications that the various applications impose on system design, and describe key architectural features. We discuss issues in data storage and placement, query processing and optimization, concurrency control and recovery. Finally, we examine the different approaches taken by the three database architectures in order to provide efficient application support.