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A quantitative evaluation of persistent memory hash indexes

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Abstract

Persistent memory (PMem) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery. Especially with the release of Intel Optane DC Persistent Memory Modules, we have witnessed a flourish in (re)designing persistent hash indexes. However, most of them are focus on the evaluation of specific metrics with important properties sidestepped. Thus, it is essential to understand how the proposed hash indexes perform under a unified testing framework and how they differentiate from each other if a wider range of performance metrics are considered. To this end, this paper provides a comprehensive evaluation of persistent hash tables. In particular, we focus on the evaluation of several state-of-the-art hash tables including CCEH, Dash, PCLHT, Clevel, Viper, Halo, SOFT, and Plush, with the second-generation PMem hardware. Our evaluation was conducted using a unified benchmarking framework and representative workloads. Besides characterizing common performance properties, we also explore how hardware configurations (such as PMem bandwidth, CPU instructions, and NUMA) affect the performance of PMem-based hash tables. With our in-depth analysis, we identify design trade-offs and good paradigms in prior arts and suggest desirable optimizations and directions for the future development of PMem-based hash tables.

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Acknowledgements

This research was supported by the National Science Foundation of China under Grants 62302424, 61972137 and 61772183, Provincial Natural Science Foundation of Hunan under Grants 2022JJ40088, and Changsha Municipal Natural Science Foundation under Grants kq2202135. The authors would like to thank the anonymous reviewers for their valuable suggestions.

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  1. Zhiwen Chen and Daokun Hu have contributed equally to this work.

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  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

    Zhiwen Chen, Daokun Hu, Wenkui Che, Jianhua Sun & Hao Chen

  2. School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan, China

    Zhiwen Chen

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Chen, Z., Hu, D., Che, W. et al. A quantitative evaluation of persistent memory hash indexes. The VLDB Journal 33, 375–397 (2024). https://doi.org/10.1007/s00778-023-00812-1

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