面向多核处理器平台的并行Hash JOIN算法设计与实现

利用多核处理器提供的强大计算能力提升数据库系统性能是当前国内外数据库研究的重要问题.利用基于多核处理器上的并行编程模型MSI和Intel处理器上的SIMD(单指令流多数据流)指令有效地加速了数据库查询的Join操作,与串行实现相比其最大加速可以达13倍.同时,还对比不同数据分块大小情况下对算法的影响,找到了优化的数据分块方法.     

基于LBT树的并行数据库查询优化方法

本文提出了一种基于LBT(Linear-Bushy-Tree)树的查询优化方法，它是对基于浓密树(Bushy-Tree)查询优化方法的一种改进。这种优化方法大大地缩减了查询执行计划空间，确保了并行查询执行计划的优化性。这种优化方法已经在我们自主研制的并行数据库管理系统PDBMS^[1,4]中得到实现。     

并行数据操作算法和查询优化技术

本文是并行数据库的查询处理并行化技术和物理设计方法”一文的续篇，继续综述并行数据库系统的另外两个重要研究领域：并行数据操作算法和并行数据库查询优化技术．最后，作为并行数据库系统研究与进展情况综述的结尾，本文将探讨并行数据库系统今后的研究方向和问题．     

并行数据库查询优化技术的最新进展

传统的查询树及基于多重加权树的查询优化方法,研究得比较成熟,语义查询优化方法将一个查询变换成一个或数个语义等价的查询,基于Agent的并行数据库查询优化采用Multi-Agent技术自动查找与给定查询有关的完整性约束条件,使得多个关系间连接操作的效率得到很大地提高.并行数据库的查询优化领域的3个重要方向为基于机群系统的并行数据库查询优化研究,将MAS技术及专家系统引入本领域,将模拟退火算法及神经网络算法引入本领域.     

Postgres数据管理系统查询优化的研究与实现

查询优化是传统和并行数据库管理系统中的重要组成部分。该文通过介绍传统和并行数据库的查询优化技术,对Postges数据库中查询优化模块的工作流程及主要实现算法进行了分析,并对其进一步并行化提出了思考性建议。     

基于遗传算法的多连接表达式并行查询优化

多连接表达式的并行查询优化是提高数据库性能的关键问题之一.提出了使用遗传算法来解决多连接表达式的并行查询优化问题.为了提高查询处理器的执行效率,采用启发式规则来搜索最优的多连接表达式并行调度执行计划.文中给出了详细的测试结果和性能分析.实验结果表明,结合启发式知识的遗传算法是解决多连并行查询优化的有效途径,对提高数据库的性能起到重要作用.     

基于并行数据库的海量商务数据管理系统研究

为了使海量商务数据得到高效、稳定、安全的处理,提出了一种针对基于并行数据流技术的海量数据管理系统,详细探讨了该系统中并行数据加载模块和并行数据查询模块的关键技术及具体体系结构,并对其中的并行查询技术提出相关的优化方案。     

基于半连接的并行查询处理算法的研究

多元连接查询的并行执行是并行数据库的研究重点,传统的并行查询处理算法没有利用面向对象数据库及其查询的特点,算法效率较低.借鉴分布式数据库查询处理中基于半连接的优化思想,提出了基于半连接的并行查询处理算法.性能评价表明了其实用性和有效性.     

面向对象数据库的并行查询处理

一、引言随着数据库规模日趋庞大,其查询日趋复杂,对数据库系统性能的要求也越来越高。另一方面,并行计算机系统迅速发展,许多商品化的高性能并行计算机系统相继投入市场。许多数据库研究者们认为,在并行计算机系统上实现数据库管理系统能够极大地提高数据库管理系统的性能,并行数据库管理系统有望成为未来的高性能数据库管理系统。     

一种支持通用θ-Join的并行查询处理机制

并行查询处理,特别是并行连接查询处理技术是并行数据库中的关键技术.然而,目前的并行查询处理方法尚存在着一些局限性,如绝大多数的并行Join算法依赖于Hash方法对数据进行分治,因此只能支持等值Join等查询类型.为了解决这一问题,提出了一种基于伪半连接的通用θ-Join查询处理算法,并给出了基于查询语法树及并行执行计划的并行数据库通用查询处理方法.在此基础上,实现了一个并行分布式数据库原型系统PD-DBMS,实验结果表明,此方法提供了良好的并行查询处理性能.     

基于多重加权树的并行数据库查询优化方法

本文提出了一种基于多重加权树的查询优化方法，包括多重加权树并行查询计划模型、并行查询计划的复杂性模型和查询优化处工法。     

基于迭代回溯的数据库查询优化算法

查询优化器是数据库系统最重要的组件之一。该文提出了一类新的查询优化算法,并对其性能进行了评估、讨论。它是基于分层回溯的一种查询优化算法,其优点主要在于对于一些复杂的应用环境,它在枚举算法的高复杂性和算法产生方案的质量之间取一个权衡点,以产生“次优”的优化结果来换取算法的执行效率,大大节省了运行环境的资源。该算法可以应用于当今一些新型数据库,如对象数据库和XML数据库等。     

并行查询交互度量及执行计划选择

查询是数据库系统的主要负载,查询的执行效率直接影响着系统的性能。目前,由于查询交互（query interaction,QI）复杂多变,查询优化器不能准确地评估查询进入系统产生的影响,很难为并行查询选择较优执行计划。将查询的平均响应时间、平均执行时间、平均I/O时间和平均缓冲区命中率作为QI的特征参数,表示QI;提出多维度查询交互度量（multi-dimensional measurement of query interaction,MMQI）模型和执行计划选择（execution plan selection,EPS）模型,采用深度神经网络,在度量QI的基础上,把QI作为主要因素,为并行查询选择较优执行计划。考虑到查询执行计划是由一系列关系运算组成的,以及QI具有时域特性,MMQI采用双向长短期记忆神经网络（bidirectional long-short term memory,Bi-LSTM）度量QI,从查询执行计划提取特征作为输入,将QI特征参数的改变作为输出,预测查询采用不同执行计划进入系统后QI特征参数的改变;EPS把预测到的查询特征参数的改变作为查询交互特征（feature of query interaction,FQI）,与查询候选执行计划特征（features of candidate plan,FCP）融合,作为另一个Bi-LSTM的输入,为查询动态地选择较优执行计划。在PostgreSQL上的实验表明,MMQI-EPS比查询优化器选择较优执行计划的平均准确率提高38.6个百分点。     

Implementation and evaluation of parallel query processing algorithms and data partitioning heuristics in object-oriented databases

Object-oriented database management systems (OODBMSs) provide rich facilities for the modeling and processing of structural as well as behavioral properties of complex application objects. However, due to their inherent generality and continuously evolving functionalities, efficient implementations are important for these OODBMSs to support the present and future applications, particularly when the databases are very large. In this paper, we present several parallel, multi-wavefront algorithms based on two processing approaches, i.e., identification and elimination approaches, to verify association patterns specified in queries. Both approaches allow more processors to operate concurrently on a query than the traditional tree-structured query processing approach, thus introducing a higher degree of parallelism in query processing. A heuristic method is presented for partitioning an object-oriented database (OODB). The main consideration for partitioning the database is load balancing. This method also tries to reduce the communication time by reducing the length of the path that wavefronts need to be propagated. Multiple wavefront algorithms based on the two approaches for tree-structured queries have been implemented on an nCUBE 2 parallel computer. The implementation of the query processor allows multiple queries to be executed simultaneously. This implementation provides an environment for evaluating the algorithms and the heuristic method for partitioning the database. The evaluation results are presented in this paper.Recommended by: Patrick Valduriez     

Dynamic Query Optimization Approach for Semantic Database Grid

Fundamentally, semantic grid database is about bringing globally distributed databases together in order to coordinate resource sharing and problem solving in which information is given well-defined meaning, and DartGrid II is the implemented database gird system whose goal is to provide a semantic solution for integrating database resources on the Web. Although many algorithms have been proposed for optimizing query-processing in order to minimize costs and/or response time, associated with obtaining the answer to query in a distributed database system, database grid query optimization problem is fundamentally different from traditional distributed query optimization. These differences are shown to be the consequences of autonomy and heterogeneity of database nodes in database grid. Therefore, more challenges have arisen for query optimization in database grid than traditional distributed database. Following this observation, the design of a query optimizer in DartGrid II is presented, and a heuristic, dynamic and parallel query optimization approach to processing query in database grid is proposed. A set of semantic tools supporting relational database integration and semantic-based information browsing has also been implemented to realize the above vision.     

Dataflow query execution in a parallel main-memory environment

In this paper, the performance and characteristics of the execution of various join-trees on a parallel DBMS are studied. The results of this study are a step into the direction of the design of a query optimization strategy that is fit for parallel execution of complex queries.Among others, synchronization issues are identified to limit the performance gain from parallelism. A new hash-join algorithm is introduced that has fewer synchronization constraints than the known hash-join algorithms. Also, the behavior of individual join operations in a join-tree is studied in a simulation experiment. The results show that the introduced Pipelining hash-join algorithm yields a better performance for multi-join queries. The format of the optimal join-tree appears to depend on the size of the operands of the join: A multi-join between small operands performs best with a bushy schedule; larger operands are better off with a linear schedule. The results from the simulation study are confirmed with an analytic model for dataflow query execution.     

An efficient query optimization strategy for spatio-temporal queries in video databases

The interest for multimedia database management systems has grown rapidly due to the need for the storage of huge volumes of multimedia data in computer systems. An important building block of a multimedia database system is the query processor, and a query optimizer embedded to the query processor is needed to answer user queries efficiently. Query optimization problem has been widely studied for conventional database systems; however it is a new research area for multimedia database systems. Due to the differences in query processing strategies, query optimization techniques used in multimedia database systems are different from those used in traditional databases. In this paper, a query optimization strategy is proposed for processing spatio-temporal queries in video database systems. The proposed strategy includes reordering algorithms to be applied on query execution tree. The performance results obtained by testing the reordering algorithms on different query sets are also presented.     

并行数据库上的并行CMD－Join算法

并行数据库在多处理机之间的分布方法(简称数据分布方法)对并行数据操作算法的性能影响很大.如果在设计并行数据操作算法时充分利用数据分布方法的特点,可以得到十分有效的并行算法.本文研究如何充分利用数据分布方法的特点,设计并行数据操作算法的问题,提出了基于CMD多维数据分布方法的并行CMD－Join算法.理论分析和实验结果表明,并行CMD－Join算法的效率高于其它并行Join算法.