PLIS: Persistent Learned Index for Strings
Authors: 
Yu Zhang, Shiyu Yang, Wenlei Zhong, Guojie Ma, Jianye Yang, Weihong ZhouAuthors Info & Claims
Yu Zhang, Shiyu Yang, Wenlei Zhong, Guojie Ma, Jianye Yang, Weihong ZhouAuthors Info & ClaimsPages 251 - 263
Abstract
Persistent memory, emerging as a potential replacement for the next generation of main memory, is gradually gaining prominence. Presently, index structures based on persistent memory primarily focus on B+ trees, hash tables, and indexes, with significant performance improvements observed in recent research on learned indexes. However, most efforts concentrate on reducing the update costs of learned indexes, leaving inadequate support for string key types. Therefore, this paper aims to create a persistent memory string learned index structure capable of handling variable-length string keys, reducing write amplification, and ensuring crash consistency. We evaluate SLIP cost effectiveness ratio using real and synthetic datasets, results show outperforming state-of-the-art string learned indexes SIndex and SLIPP across various workloads.
References
[1]
Huang K and Wang T Indexing on Non-volatile Memory 2023 1 Cham Springer
[2]
Huang, K., Imai, D., Wang, T., Xie, D.: SSDs striking back: the storage jungle and its implications to persistent indexes. In: CIDR, vol. 22, pp. 1–8 (2022)
[3]
Raoux, S., Burr, G.W., Breitwisch, M.J., et al.: Phase-change random access memory: a scalable technology. IBM J. Res. Dev. 52(4.5), 465–479 (2008)
[4]
Strukov DB, Snider GS, Stewart DR, et al. The missing memristor found Nature 2008 453 7191 80-83
[5]
Hong XL, Loy DJJ, Dananjaya PA, et al. Oxide-based RRAM materials for neuromorphic computing J. Mater. Sci. 2018 53 8720-8746
[6]
Hady FT, Foong A, Veal B, et al. Platform storage performance with 3D XPoint technology Proc. IEEE 2017 105 9 1822-1833
[7]
Arulraj J, Levandoski J, Minhas UF, et al. BzTree: a high-performance latch-free range index for non-volatile memory Proc. VLDB Endow. 2018 11 5 553-565
[8]
Yang, J., Wei, Q., Chen, C., et al.: NV-tree: reducing consistency cost for NVM-based single level systems. In: 13th USENIX Conference on File and Storage Technologies (FAST 2015), pp. 167–181 (2015)
[9]
Wang, C., Hu, J., Yang, T.Y., et al.: SEPH: scalable, efficient, and predictable hashing on persistent memory. In: 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2023), pp. 479–495 (2023)
[10]
Hu, D., Chen, Z., Che, W., et al.: Halo: a hybrid PMem-DRAM persistent hash index with fast recovery. In: Proceedings of the 2022 International Conference on Management of Data, pp. 1049–1063 (2022)
[11]
Lu B, Ding J, Lo E, et al. APEX: a high-performance learned index on persistent memory Proc. VLDB Endow. 2021 15 3 597-610
[12]
Zhang Z, Chu Z, Jin P, et al. PLIN: a persistent learned index for non-volatile memory with high performance and instant recovery Proc. VLDB Endow. 2022 16 2 243-255
[13]
Wang Z, Ding C, Song F, et al. WIPE: a write-optimized learned index for persistent memory ACM Trans. Archit. Code Optim. 2024 21 2 1-25
[14]
Kraska, T., Beutel, A., Chi, E.H., et al.: The case for learned index structures. In: Proceedings of the 2018 International Conference on Management of Data, pp. 489–504 (2018)
[15]
Wang, Y., Tang, C., Wang, Z., et al.: SIndex: a scalable learned index for string keys. In: Proceedings of the 11th ACM SIGOPS Asia-Pacific Workshop on Systems, pp. 17–24 (2020)
[16]
Handy, J., Coughlin, T.: Persistent memories without optane, where would we be. In: Storage Developer Conference (SDC). SNIA (2022)
[17]
Ding, J., Minhas, U.F., et al.: ALEX: an updatable adaptive learned index. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 969–984 (2020)
[18]
Wu, J., Zhang, Y., Chen, S., et al.: Updatable learned index with precise positions. arXiv preprint arXiv:2104.05520 (2021)
[19]
Tang, C., Wang, Y., Dong, Z., et al.: XIndex: a scalable learned index for multicore data storage. In: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 308–320 (2020)
[20]
Gugnani, S., et al.: Understanding the idiosyncrasies of real persistent memory. In: Proceedings of the VLDB Endowment, pp. 626–639 (2020)
[21]
Chen, Z., Hua, Y., Ding, B., et al.: Lock-free concurrent level hashing for persistent memory. In: Proceedings of the 2020 USENIX Annual Technical Conference, pp.799–812 (2020)
[22]
Alguliev RM et al. CDDS: constraint-driven document summarization models Expert Syst. Appl. 2013 40 2 458-65
[23]
Yang, J., Kim, J., Hoseinzadeh, M., et al.: An empirical guide to the behavior and use of scalable persistent memory. In: File and Storage Technologies (2020)
[24]
Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 497–506 (2009)
[25]
Manegold, S., Boncz, P., Kersten, M.L.: Generic database cost models for hierarchical memory systems. In: Proceedings of the 28th International Conference on Very Large Databases VLDB, pp. 191–202 (2022)
[26]
Venkataraman, S., Tolia, N., Ranganathan, P., et al.: Consistent and durable data structures for non-volatile byte-addressable memory. In: Proceedings of the 9th USENIX Conference on File and Storage Technologies. FAST (2011)
[27]
Zhou, W., Yang, S.: SLIPP: a space-efficient learned index for string keys. In: Proceedings of the 2024 6th International Conference on Big-data Service and Intelligent Computation (2024)
[28]
Qi X, Wang M, Wen Y, Zhang H, and Yuan X Zhao X, Yang S, Wang X, and Li J Weighted cost model for optimized query processing WISA 2022 2022 Cham Springer 473-484
[29]
Mandelman JA, Dennard RH, Bronner GB, et al. Challenges and future directions for the scaling of dynamic random-access memory (DRAM) IBM J. Res. Dev. 2002 2 3 187-212
Index Terms
- PLIS: Persistent Learned Index for Strings
Index terms have been assigned to the content through auto-classification.
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Information & Contributors
Information
Published In
Aug 2024
623 pages
ISBN:978-981-97-7706-8
DOI:10.1007/978-981-97-7707-5
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 11 September 2024
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