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 <inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> -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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Publication Details

Journal
IEEE Transactions on Knowledge and Data Engineering
Published
2024-08-16
DOI
https://doi.org/10.1109/tkde.2024.3421232
Citations
2
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.98

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article

ZBTree: A Fast and Scalable B+-Tree for Persistent Memory

Wenkui Che, Daokun Hu, Jianhua Sun, Zhiwen Chen et al.
2 citations
IEEE Transactions on Knowledge and Data Engineering
Parallel Computing and Optimization Techniques
0.98
article

ZBTree: A Fast and Scalable B+-Tree for Persistent Memory

Wenkui Che, Daokun Hu, Jianhua Sun, Zhiwen Chen, Hao Chen
article en
2 citations

Abstract

In this paper, we present the design and implementation of ZBTree, a hotness-aware B <inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> -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.

IEEE Transactions on Knowledge and Data EngineeringVol. 36(12)
Hunan University (CN), Xiangtan University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hainan Province
Openalex Percentile: Top 26%
Parallel Computing and Optimization Techniques
0.98
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ZBTree: A Fast and Scalable B+-Tree for Persistent Memory — Wenkui Che, Daokun Hu, et al. · IEEE Transactions on Knowledge and Data Engineering (2024) | TGRS Research Map | TGRS