基于偏移值编码的鲁棒高效排序

IF 2.2 2区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Thanh Do, Goetz Graefe
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引用次数: 0

摘要

排序和搜索是数据库查询处理的重要组成部分,例如以索引创建、索引维护和索引查找的形式进行排序和搜索,比较键对是排序和搜索工作的重要组成部分。我们致力于简单、高效地实现几十年来被忽视的快速比较和快速排序的有效技术,特别是偏移值编码。在这个过程中,我们偶然发现了它与运行文件中的前缀截断以及行格式和列格式存储结构中压缩技术的对偶性,即前缀截断和前导键列的运行长度编码之间的互利关系。我们还发现了与已排序流的消费者之间的有益关系,例如合并并行流、流内聚合和合并连接。我们报告了我们在Google的Napa和F1查询系统中的实现,以及对性能和可扩展性的实验评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust and Efficient Sorting with Offset-value Coding

Sorting and searching are large parts of database query processing, e.g., in the forms of index creation, index maintenance, and index lookup, and comparing pairs of keys is a substantial part of the effort in sorting and searching. We have worked on simple, efficient implementations of decades-old, neglected, effective techniques for fast comparisons and fast sorting, in particular offset-value coding. In the process, we happened upon its mutually beneficial relationship with prefix truncation in run files as well as the duality of compression techniques in row- and column-format storage structures, namely prefix truncation and run-length encoding of leading key columns. We also found a beneficial relationship with consumers of sorted streams, e.g., merging parallel streams, in-stream aggregation, and merge join. We report on our implementation in the context of Google’s Napa and F1 Query systems as well as an experimental evaluation of performance and scalability.

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来源期刊
ACM Transactions on Database Systems
ACM Transactions on Database Systems 工程技术-计算机:软件工程
CiteScore
5.60
自引率
0.00%
发文量
15
审稿时长
>12 weeks
期刊介绍: Heavily used in both academic and corporate R&D settings, ACM Transactions on Database Systems (TODS) is a key publication for computer scientists working in data abstraction, data modeling, and designing data management systems. Topics include storage and retrieval, transaction management, distributed and federated databases, semantics of data, intelligent databases, and operations and algorithms relating to these areas. In this rapidly changing field, TODS provides insights into the thoughts of the best minds in database R&D.
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