Towards Data-Based Cache Optimization of B+-Trees

Roland Kühn, Daniel Biebert, Christian Hakert, Jian-Jia Chen, J. Teubner
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Abstract

The rise of in-memory databases and systems with considerably large memories and cache sizes requires the rethinking of the proper implementation of index structures like B+-trees in such systems. While disk block-sized nodes and binary search were considered as good in the past, smaller node sizes and cache-friendly linear search within nodes can be noticeably more performant nowadays. Considering the probabilistic distribution of lookup values to the B+-tree as part of a memory-friendly and cache-aware layout is a consequent next step, which is studied in this paper. Favoring frequently visited nodes and paths in the regard of cache hits can improve the overall performance of the tree and, thus, of the entire database system. We provide such an optimized B+-tree layout, which takes the probabilistic distribution of the lookup values as a basis. Experimental evaluation shows that choosing rather small node sizes in combination with our optimization algorithm can improve the performance by up to in comparison to a default baseline.
基于数据的B+树缓存优化研究
内存数据库和具有相当大内存和缓存大小的系统的兴起需要重新考虑在这些系统中如何正确实现索引结构(如B+树)。虽然磁盘块大小的节点和二进制搜索在过去被认为是好的,但现在更小的节点大小和节点内的缓存友好型线性搜索可以显着提高性能。考虑查找值在B+树中的概率分布,作为内存友好和缓存感知布局的一部分,是本文研究的下一步。在缓存命中方面,选择频繁访问的节点和路径可以提高树的整体性能,从而提高整个数据库系统的性能。我们提供了这样一个优化的B+树布局,它以查找值的概率分布为基础。实验评估表明,与默认基线相比,选择较小的节点大小与我们的优化算法相结合可以提高性能。
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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