Adaptive Merging on Phase Change Memory

IF 0.4 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Wojciech Macyna, Michal Kukowski
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引用次数: 1

Abstract

Indexing is a well-known database technique used to facilitate data access and speed up query processing. Nevertheless, the construction and modification of indexes are very expensive. In traditional approaches, all records in the database table are equally covered by the index. It is not effective, since some records may be queried very often and some never. To avoid this problem, adaptive merging has been introduced. The key idea is to create an index adaptively and incrementally as a side-product of query processing. As a result, the database table is indexed partially depending on the query workload. This paper faces the problem of adaptive merging for phase change memory (PCM). The most important features of this memory type are limited write endurance and high write latency. As a consequence, adaptive merging should be investigated from the scratch. We solve this problem in two steps. First, we apply several PCM optimization techniques to the traditional adaptive merging approach. We prove that the proposed method (eAM) outperforms a traditional approach by 60%. After that, we invent the framework for adaptive merging (PAM) and propose a new variant of the PCM-optimized index. It further improves the system performance by 20% for databases where search queries interleave with data modifications.
相变存储器的自适应归并
索引是一种众所周知的数据库技术,用于促进数据访问和加快查询处理。然而,索引的构建和修改是非常昂贵的。在传统方法中,索引平等地覆盖数据库表中的所有记录。这是无效的,因为有些记录可能会经常被查询,而有些记录则永远不会被查询。为了避免这个问题,引入了自适应合并。关键思想是自适应地、增量地创建索引,作为查询处理的副产品。因此,数据库表的索引部分取决于查询工作负载。本文研究了相变存储器(PCM)的自适应归并问题。这种内存类型最重要的特性是有限的写入持久性和高写入延迟。因此,应该从头开始研究自适应合并。我们分两步解决这个问题。首先,我们将几种PCM优化技术应用到传统的自适应合并方法中。我们证明了所提出的方法(eAM)比传统方法的性能高出60%。在此基础上,提出了自适应合并(PAM)框架,并提出了一种新的pcm优化索引。对于搜索查询与数据修改交织的数据库,它进一步提高了20%的系统性能。
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来源期刊
Fundamenta Informaticae
Fundamenta Informaticae 工程技术-计算机:软件工程
CiteScore
2.00
自引率
0.00%
发文量
61
审稿时长
9.8 months
期刊介绍: Fundamenta Informaticae is an international journal publishing original research results in all areas of theoretical computer science. Papers are encouraged contributing: solutions by mathematical methods of problems emerging in computer science solutions of mathematical problems inspired by computer science. Topics of interest include (but are not restricted to): theory of computing, complexity theory, algorithms and data structures, computational aspects of combinatorics and graph theory, programming language theory, theoretical aspects of programming languages, computer-aided verification, computer science logic, database theory, logic programming, automated deduction, formal languages and automata theory, concurrency and distributed computing, cryptography and security, theoretical issues in artificial intelligence, machine learning, pattern recognition, algorithmic game theory, bioinformatics and computational biology, quantum computing, probabilistic methods, algebraic and categorical methods.
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