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1.
并行数据库中JOIN运算的并行算法   总被引:2,自引:0,他引:2       下载免费PDF全文
JOIN运算的并行算法一直是并行数据库领域中研究的热点问题,先后出现了一系列并行JOIN算法或改进算法,但它们都存在着通信效率较低、负载调度频繁等问题。本文针对这些问题,在分析比较前人工作的基础上对SABJ+算法与ABJ+算法加以改进,得到了效率更高的并行JOIN算法ABJ++。  相似文献
2.
基于IP网络流量数据仓库的KDD实现   总被引:1,自引:0,他引:1  
通过对IP网络流量数据仓库进行多表关联检索和决策树模型的数据挖掘,可以从中发现若干有用的知识和相互联联的规则,用于分析流量增长的趋势和寻找IP地址分布与流量大小之间的普遍规律,有助于资源的控制和异常情况的发现。另外,将多表关联算法和决策树挖掘于星型构架的多维数据集,可以显著地提高数据对象之间的关联性能和数据挖掘的效率。  相似文献
3.
基于共享Cache多核处理器的Hash连接优化   总被引:1,自引:0,他引:1       下载免费PDF全文
邓亚丹  景 宁  熊 伟 《软件学报》2010,21(6):1220-1232
针对目前主流的多核处理器,研究了基于共享缓存多核处理器环境下的数据库Hash连接优化.首先提出基于Radix-Join算法的Hash连接多线程执行框架,通过实例分析了影响多线程Radix-Join算法性能的因素.在此基础上,优化了Hash连接多线程执行框架中的各种线程及其访问共享Cache的性能,优化了聚集连接时Hash连接算法的内存访问,并分析了多线程聚集划分的加速比.基于开源数据库INGRES和EaseDB,实现了所提出的连接多线程执行框架,在实验中测试了多线程Hash连接框架的性能.实验结果表明,该算法可以有效解决Hash连接执行时共享Cache在多线程条件下的访问冲突和处理器负载均衡问题,极大地提高了Hash连接性能.  相似文献
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5.
Previous algorithms for unrestricted constraint satisfaction use reduction search, which inferentially removes values from domains in order to prune the backtrack search tree. This paper introduces partition search, which uses an efficient join mechanism instead of removing values from domains. Analytical prediction of quantitative performance of partition search appears to be intractable and evaluation therefore has to be by experimental comparison with reduction search algorithms that represent the state of the art. Instead of working only with available reduction search algorithms, this paper introduces enhancements such as semijoin reduction preprocessing using Bloom filtering.  相似文献
6.
王国仁  于戈  叶峰  郑怀远 《计算机学报》1999,22(10):1032-1041
提出了一个基于分布式共享虚拟存储器技术的并行Hash连接算法,然后设计了一个并行连接算法的测试评价基准,并评价和分析了该算法在均匀情况下3个不同负载的性能比较和Zipf顺斜数据分布情况下两种度策略的算法性能。同时与其它并行连接算法进行性能比较与分析。  相似文献
7.
Contemporary long-term storage devices feature powerful embedded processors and sizeable memory buffers. Active Storage Devices (ASD) is the hard disk technology that makes use of these significant resources to not only manage the disk operation but also to execute custom application code on large amounts of data. While prior research has shown that ASDs perform exceedingly well with filter-type algorithms, the evaluation of binary-relational operators has been limited. In this paper, we analyze and evaluate inter-operator parallelism of GRACE-based join algorithms that function atop ASDs. We derive accurate cost expressions for existing algorithms and expose performance bottlenecks; upon these findings we propose Active Hash Join, a new algorithm that exploits all system resources. Through experimentation, we confirm that existing algorithms are best suited for systems with either small or large numbers of ASDs. However, we find that the “adaptive” nature of Active Hash Join yields enhanced parallelism in all cases, especially when the aggregate ASD resources are comparable to the main CPU and main memory. Recommended by: Ahmed Elmagarmid Work partially supported by the University of Athens Research Foundation.  相似文献
8.
In this paper, we present an adaptive version of the parallel Distributive Join (DJ) algorithm that we proposed in [5]. The adaptive parallel DJ algorithm can handle the data skew in operand relations efficiently. We implemented the original and adaptive parallel DJ algorithms on a network of Alpha workstations using the Parallel Virtual Machine (PVM). We analyzed the performance of the algorithms, and compared it with that of the parallel Hybrid-Hash (HH) join algorithms. Our results show that the parallel DJ algorithms perform comparably with the parallel HH join algorithms over the entire range of the number of processors used and for different join selectivities. A significant advantage of the parallel DJ algorithms is that they can easily support non-equijoin operations.  相似文献
9.
Decision support queries typically involve several joins, a grouping with aggregation, and/or sorting of the result tuples. We propose two new classes of query evaluation algorithms that can be used to speed up the execution of such queries. The algorithms are based on (1) early sorting and (2) early partitioning– or a combination of both. The idea is to push the sorting and/or the partitioning to the leaves, i.e., the base relations, of the query evaluation plans (QEPs) and thereby avoid sorting or partitioning large intermediate results generated by the joins. Both early sorting and early partitioning are used in combination with hash-based algorithms for evaluating the join(s) and the grouping. To enable early sorting, the sort order generated at an early stage of the QEP is retained through an arbitrary number of so-called order-preserving hash joins. To make early partitioning applicable to a large class of decision support queries, we generalize the so-called hash teams proposed by Graefe et al. [GBC98]. Hash teams allow to perform several hash-based operations (join and grouping) on the same attribute in one pass without repartitioning intermediate results. Our generalization consists of indirectly partitioning the input data. Indirect partitioning means partitioning the input data on an attribute that is not directly needed for the next hash-based operation, and it involves the construction of bitmaps to approximate the partitioning for the attribute that is needed in the next hash-based operation. Our performance experiments show that such QEPs based on early sorting, early partitioning, or both in combination perform significantly better than conventional strategies for many common classes of decision support queries. Received April 4, 2000 / Accepted June 23, 2000  相似文献
10.
In this paper, we analyze the performance of the parallel Distributive Join algorithm that we proposed in Chung and Yang 1995. We implemented the algorithm on an Intel Paragon machine and analyzed the effect of the number of processors and the join selectivity on the performance of the algorithm. We also compared the performance of the Distributive Join (DJ) algorithm with that of the Hybrid-Hash(HH) join algorithm. Our results show that the DJ performs comparably with the HH over the entire range of number of processors used and different join selectivities. A big advantage of the parallel DJ algorithm over the HH join algorithm is that it can easily support non-equijoin operations. The results can also be used to estimate the performance of file I/O intensive applications to be implemented on the Intel Paragon machine.  相似文献
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