ZBTree:用于持久内存的快速可扩展 B$^+$ 树

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-16 DOI:10.1109/TKDE.2024.3421232
Wenkui Che;Zhiwen Chen;Daokun Hu;Jianhua Sun;Hao Chen
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引用次数: 0

摘要

在本文中,我们介绍了 ZBTree 的设计与实现,这是一种用于持久内存(PMem)的热感知 B$^+$ 树。ZBTree 利用 PMem+DRAM 架构,该架构具有易失性操作层和保序持久层,易失性操作层用于加速数据访问,保序持久层用于实现快速恢复以及低开销的一致性和持久性保证。操作层包含用于索引的内部节点和保存元数据的压缩叶节点(DLeaves)。在叶节点压缩的基础上,我们提出了一种数据寄存方法,它支持将热数据动态加载到快速 DRAM 中,从而避免了后续读取热数据时对 PMem 的访问,并在不占用额外 DRAM 的情况下提高了读取性能。此外,我们还提出了一种轻量级节点拆分机制,该机制具有恒定的持久性开销,不会随节点大小而变化。我们的广泛评估表明,在各种工作负载下,ZBTree 的吞吐量比最先进的树索引高出 1.4-6.3 倍。同时,与现有设计相比,ZBTree 的恢复速度相当或更快。
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ZBTree: A Fast and Scalable B$^+$+-Tree for Persistent Memory
In this paper, we present the design and implementation of ZBTree, a hotness-aware B $^+$ -Tree for persistent memory (PMem). ZBTree leverages the PMem+DRAM architecture, which is featured with a volatile operation layer to accelerate data access and an order-preserving persistent layer to achieve fast recovery and low-overhead consistency and persistence guarantees. The operation layer contains inner nodes for indexing and compacted leaf nodes (DLeaves) that hold metadata. Based on leaf node compaction, we present a data lodging method, which supports to load hot data into fast DRAM dynamically, avoiding PMem accesses for subsequent reads of hot data and achieving improved read performance without incurring extra DRAM usage. In addition, we present a lightweight node splitting mechanism with constant persistence overhead that does not vary with node size. Our extensive evaluations show that ZBTree achieves higher throughput by a factor of 1.4x-6.3x compared to state-of-the-art tree indexes under a wide range of workloads. Meanwhile, ZBTree achieves comparable or faster recovery speed compared to existing designs.
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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