Conclusion
We propose HeterMM, a versatile framework that leverages in-DRAM indexes in KV stores on heterogeneous memory. HeterMM incorporates a plug-in programming model, allowing for the integration of various types of indexes. By prioritizing the maintenance of both index and hot data in DRAM, HeterMM maximizes the utilization of the superior performance of DRAM. Our evaluation demonstrates that HeterMM outperforms existing state-of-the-art frameworks that convert in-DRAM indexes to persistent ones. Furthermore, HeterMM can surpass NVM-specific KV stores by carefully selecting the appropriate index for specific scenarios.
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References
Huang W, Ji Y, Zhou X, He B, Tan K. A design space exploration and evaluation for main-memory hash joins in storage class memory. Proceedings of the VLDB Endowment, 2023, 16(6): 1249–1263
Ramanathan M K, Kim W H, Fu X, Monga S K, Lee H W, Jang M, Mathew A, Min C. TIPS: making volatile index structures persistent with DRAM-NVMM tiering. In: Proceedings of 2021 USENIX Annual Technical Conference. 2021, 773–787
Cooper B F, Silberstein A, Tam E, Ramakrishnan R, Sears R. Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM Symposium on Cloud Computing. 2010, 143–154
Hu D, Chen Z, Che W, Sun J, Chen H. Halo: a hybrid PMem-DRAM persistent hash index with fast recovery. In: Proceedings of 2022 International Conference on Management of Data. 2022, 1049–1063
Chen Y, Lu Y, Fang K, Wang Q, Shu J. uTree: a persistent B+-tree with low tail latency. Proceedings of the VLDB Endowment, 2020, 13(12): 2634–2648
Acknowledgement
This project was partially supported by a grant funded by the Ministry of Education (Singapore) (Title: inPMdb: An in-Persistent Memory Database System; WBS NO: A8000082-00-00) and Shanghai Engineering Research Center of Big Data Management.
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Ji, Y., Huang, W. & Zhou, X. HeterMM: applying in-DRAM index to heterogeneous memory-based key-value stores. Front. Comput. Sci. 18, 184612 (2024). https://doi.org/10.1007/s11704-024-3713-0
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DOI: https://doi.org/10.1007/s11704-024-3713-0