RB+-Tree and Its Implementation in Column-Stored Data Warehouse

Li Sun, Yule Hu
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Abstract

The index technology is one of the key issues to improve the efficiency for massive data queries. Traditional index technologies such as B+-tree index has achieved good performance in the update transaction environment. However, the performance remains dissatisfactory by directly applying it to column- oriented analytical data warehouse. In this paper, we propose a novel tree-based index: RB+-tree (Reduced B+-tree) by analyzing the characteristics of column-oriented data warehouse query environment. The RB+-tree, with the improved structure and the bottom-up index creation approach, greatly improves the performance of create and search operations as well as the space usage. Further more, we apply RB+-tree to column-oriented data warehouse, and construct the rowid-index and value-index. Especially, we propose the join-index for multi-table join based on the star schema, which improves the tuple reconstruction and multi-table join performance effectively in column-oriented data warehouse system. The experimental results on the data warehouse benchmark data set SSB verify the effectiveness of the proposed method.
RB+-Tree及其在列存储数据仓库中的实现
索引技术是提高海量数据查询效率的关键问题之一。传统的索引技术如B+树索引在更新事务环境中取得了良好的性能。然而,直接应用于面向列的分析数据仓库,其性能仍不理想。本文通过分析面向列的数据仓库查询环境的特点,提出了一种新的基于树的索引:RB+-tree (Reduced B+-tree)。通过改进的结构和自下而上的索引创建方法,RB+-tree极大地提高了创建和搜索操作的性能以及空间使用。在此基础上,将RB+树应用于面向列的数据仓库,构造了列索引和值索引。特别提出了基于星型模式的多表连接的连接索引,有效地提高了面向列数据仓库系统中元组重构和多表连接的性能。在数据仓库基准数据集SSB上的实验结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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