Revisiting Secondary Indexing in LSM-based Storage Systems with Persistent Memory


Jing Wang, Youyou Lu, Qing Wang, Yuhao Zhang, and Jiwu Shu, Department of Computer Science and Technology, Tsinghua University and Beijing National Research Center for Information Science and Technology (BNRist)


LSM-based storage systems are widely used for superior write performance on block devices. However, they currently fail to efficiently support secondary indexing, since a secondary index query operation usually needs to retrieve multiple small values, which scatter in multiple LSM components. In this work, we revisit secondary indexing in LSM-based storage systems with byte-addressable persistent memory (PM). Existing PM-based indexes are not directly competent for efficient secondary indexing. We propose PERSEID, an efficient PMbased secondary indexing mechanism for LSM-based storage systems, which takes into account both characteristics of PM and secondary indexing. PERSEID consists of (1) a specifically designed secondary index structure that achieves highperformance insertion and query, (2) a lightweight hybrid PM-DRAM and hash-based validation approach to filter out obsolete values with subtle overhead, and (3) two adapted optimizations on primary table searching issued from secondary indexes to accelerate non-index-only queries. Our evaluation shows that PERSEID outperforms existing PM-based indexes by 3-7× and achieves about two orders of magnitude performance of state-of-the-art LSM-based secondary indexing techniques even if on PM instead of disks.

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This content is available to:

@inproceedings {288711,
author = {Jing Wang and Youyou Lu and Qing Wang and Yuhao Zhang and Jiwu Shu},
title = {Revisiting Secondary Indexing in {LSM-based} Storage Systems with Persistent Memory},
booktitle = {2023 USENIX Annual Technical Conference (USENIX ATC 23)},
year = {2023},
isbn = {978-1-939133-35-9},
address = {Boston, MA},
pages = {817--832},
url = {},
publisher = {USENIX Association},
month = jul