Closing the B+-tree vs. LSM-tree Write Amplification Gap on Modern Storage Hardware with Built-in Transparent Compression
Yifan Qiao, Xubin Chen, Ning Zheng, Jiangpeng Li, Yang Liu, Tong Zhang
摘要
This paper studies the design of B-tree that can take full advantage of modern storage hardware with built-in transparent compression. Recent years have witnessed significant interest in applying log-structured merge tree (LSM-tree) as an alternative to B-tree. The current consensus is that, compared with B-tree, LSM-tree has distinct advantages in terms of storage space efficiency and write amplification. This paper argues that one should revisit this belief upon the arrival of storage hardware with built-in transparent compression. Advanced storage appliances (e.g., all-flash array) and emerging computational storage drives perform hardware-based lossless data compression, transparent to OS and user applications. Beyond straightforwardly reducing the physical storage cost difference between B-tree and LSM-tree, such modern storage hardware brings new opportunities to innovate B-tree implementation in order to largely reduce its write amplification. As the first step to explore the potential, this paper presents three simple design techniques (i.e., deterministic page shadowing, localized page modification logging, and sparse redo logging) that can leverage such modern storage hardware to significantly reduce the B-tree write amplification. We implemented these design techniques and carried out experiments on a commercial storage drive with built-in transparent compression. The results show that the proposed design techniques can reduce the B-tree write amplification by over 10×. Compared with RocksDB (a popular key-value store built upon LSM-tree), the implemented B-tree can achieve similar or even smaller write amplification and physical storage space usage.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper8
- Bf-Tree: A Modern Read-Write-Optimized Concurrent Larger-Than-Memory Range IndexXiangpeng Hao, Badrish ChandramouliVLDB 2024 · 被引用 14 次
- Reviving In-Storage Hardware Compression on ZNS SSDs through Host-SSD CollaborationYingjia Wang, Tao Lu, Yuhong Liang, Xiang Chen 等HPCA 2025 · 被引用 7 次
- ScalaCache: Scalable User-Space Page Cache Management with Software-Hardware CoordinationLi Peng, Yuda An, You Zhou, Chenxi Wang 等USENIX ATC 2024 · 被引用 6 次
- PolarStore: High-Performance Data Compression for Large-Scale Cloud-Native DatabasesQingda Hu, Xinjun Yang, Feifei Li, Junru Li 等FAST 2026 · 被引用 4 次
- Sorting on Byte-Addressable Storage: The Resurgence of Tree StructureYing Zheng, Kian-Lee TanVLDB 2024 · 被引用 2 次
它引用的顶会 Paper1
相关 Paper
- Reducing Write Amplification of LSM-Tree with Block-Grained CompactionXiaoliang Wang, Peiquan Jin, Bei Hua, Hai Long 等ICDE 2022 · 被引用 26 次
- BL-Tree: The Best of Both Worlds by Combining B+- Tree on Top and LSM - Tree on BottomSuzhen Wu, Zuocheng Wang, Shengzhe Wang, Jiahong Chen 等ICDE 2025 · 被引用 2 次
- ListDB: Union of Write-Ahead Logs and Persistent SkipLists for Incremental Checkpointing on Persistent MemoryWonbae Kim, Chanyeol Park, Dongui Kim, Hyeongjun Park 等OSDI 2022 · 被引用 47 次
- PartitionKV: Redesigning LSM-tree KV Stores on NVMs with Adaptive Partitioning for Reducing Write Stalls and AmplificationXingye Huang, Jinyu Wu, Xiaofang Xia, Jiangtao Cui 等SIGMOD 2026
- Less is More: De-amplifying I/Os for Key-value Stores with a Log-assisted LSM-treeKecheng Huang, Zhiping Jia, Zhaoyan Shen, Zili Shao 等ICDE 2021 · 被引用 27 次
