Introduction 1.2 Parallel Database Systems 1.2.1 Computation Model 2 1.2 Parallel Database Systems Introduction Select * from Employee, Department Where (employee.dept_no @bullet Department.dept_no) and (employee.position = "manager") (a) Sql Request 1.2.-猫眼课题宝

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M. Abdelguerfi;Kam-Fai Wong

1.1背景近年来，数据库管理系统(DBMS)处理的数据量持续增加。事实上，对于DBMS来说，管理大小从数百GB到TB的数据库不再少见。数据库大小的这种巨大增长伴随着对DBMS展示更复杂功能的日益增长的需求，例如支持面向对象、演绎和基于多媒体的应用程序。在许多情况下，这些新的要求使得现有的DBMS无法提供必要的系统性能，特别是考虑到许多大型机DBMS已经难以满足服务于大量并发用户和/或处理大量数据的传统信息系统的I/O和CPU性能要求[13]。为了达到所需的性能水平，数据库系统越来越需要利用并行性。如[1]中所指出的，对于使用诸如关系等行业标准数据库模型的传统DBMS来说，传统的并行化方法可以采取两种形式之一。第一种是通过使用大规模并行的通用硬件平台。例如，Ncube和SP1等商业平台现在支持Oracle的并行服务器[5]。此外，剑桥并行处理公司推出的分布式阵列处理器现在被用于生产商业大规模并行数据库系统[4]。第二种方法利用现成组件的阵列来形成定制的大规模并行系统。在很大程度上，这些硬件系统是基于MIMD并行架构的。NCR3700[1]和超级数据库计算机II(SDC-II)[27]就是两个这样的系统。NCR 3700使用称为Bynet和RAIDS(廉价磁盘冗余阵列[11])的高性能多级互连网络。该系统现在可以运行Sybase关系数据库管理系统的并行版本[5，26]。SDC-II由八个数据处理模块组成，每个模块由七个处理器和五个磁盘驱动器组成。数据处理模块通过欧米茄互连网络进行通信。1导言使用工作站集群作为虚拟并行系统是一种较新的方法，已经对数据库管理系统行业产生了影响。这些工作站网络提供了巨大的聚合计算能力，通常可与紧密耦合的多处理器系统相媲美。它们提供了可行的高性能计算环境，并且与大型专用并行机相比具有多项优势，包括成本、消除中心故障点和可扩展性。他们的…

1.1 Background There has been a continuing increase in the amount of data handled by database management systems (DBMSs) in recent years. Indeed, it is no longer unusual for a DBMS to manage databases ranging in size from hundreds of gigabytes to terabytes. This massive increase in database sizes is coupled with a growing need for DBMSs to exhibit more sophisticated functionality such as the support of object-oriented, deductive, and multimedia-based applications. In many cases, these new requirements have rendered existing DBMSs unable to provide the necessary system performance, especially given that many mainframe DBMSs already have difficulty meeting the I/O and CPU performance requirements of traditional information systems that service large numbers of concurrent users and/or handle massive amounts of data [13]. To achieve the required performance levels, database systems have been increasingly required to make use of parallelism. As noted in [1], the traditional approach to paral-lelism for conventional DBMSs which use industry-standard database models such as the relational, can take one of two forms. The first is through the use of massively parallel general-purpose hardware platforms. As an example of this, commercial platforms such as nCube and SP1 are now supporting Oracle's parallel server [5]. Also, the Distributed Array Processor marketed by Cambridge Parallel Processing is now being used to produce a commercial massively parallel database system [4]. The second approach makes use of arrays of off-the-shelf components to form custom massively parallel systems. For the most part, these hardware systems are based onMIMD parallel architectures. The NCR 3700 [1] and the Super Database Computer II (SDC-II) [27] are two such systems. The NCR 3700 uses a high-performance multistage interconnection network known as Bynet and RAIDS (Redundant Arrays of Inexpensive Disks [11]). This system can now run a parallel version of Sybase relational DBMS [5, 26]. The SDC-II consists of eight data processing modules , where each module is composed of seven processors and five disk drives. The data processing modules communicate through an omega interconnection network. 1 Introduction The use of clusters of workstations as virtual parallel systems is a more recent approach that is already impacting the DBMS industry. These networks of workstations provide enormous amounts of aggregate computational power, often rivaling that of tightly coupled multiprocessor systems. They provide a viable high-performance computing environment and have several benefits over large, dedicated parallel machines, including cost, elimination of central point of failure, and scalability. Their …

Introduction 1.2 Parallel Database Systems 1.2.1 Computation Model 2 1.2 Parallel Database Systems Introduction Select * from Employee, Department Where (employee.dept_no @bullet Department.dept_no) and (employee.position = "manager") (a) Sql Request 1.2.

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