Author:
Schmeißer Josef,Schüle Maximilian E.,Leis Viktor,Neumann Thomas,Kemper Alfons
Abstract
AbstractRecently proposed index structures, that combine trie-based and comparison-based search mechanisms, considerably improve retrieval throughput for in-memory database systems. However, most of these index structures allocate small memory chunks when required. This stands in contrast to block-based index structures, that are necessary for disk-accesses of beyond main-memory database systems such as Umbra. We therefore present the B2-tree. The outer structure is identical to that of an ordinary B+-tree. It still stores elements in a dense array in sorted order, enabling efficient range scan operations. However, B2-tree is composed of multiple trees, each page integrates another trie-based search tree, which is used to determine a small memory region where a sought entry may be found. An embedded tree thereby consists of decision nodes, which operate on a single byte at a time, and span nodes, which are used to store common prefixes. This architecture usually accesses fewer cache lines than a vanilla B+-tree as shown in our performance evaluation. As a result, the B2-tree answers point queries considerably faster. Concurrent access to B2-tree pages are managed by an optimistic locking protocol which results in high utilization of the available hardware resources. Our evaluation of read-write workloads attests more than competitive performance for the B2-tree compared to a traditional B+-tree.
Funder
Technische Universität München
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. An Empirical Evaluation of Variable-length Record B+Trees on a Modern Graph Database System;2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW);2024-05-13
2. Recursive SQL and GPU-support for in-database machine learning;Distributed and Parallel Databases;2022-07-09
3. Recursive SQL for Data Mining;34th International Conference on Scientific and Statistical Database Management;2022-07-06