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NVLSM: A Persistent Memory Key-Value Store Using Log-Structured Merge Tree with Accumulative Compaction

Published:16 August 2021Publication History
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Abstract

Computer systems utilizing byte-addressable Non-Volatile Memory (NVM) as memory/storage can provide low-latency data persistence. The widely used key-value stores using Log-Structured Merge Tree (LSM-Tree) are still beneficial for NVM systems in aspects of the space and write efficiency. However, the significant write amplification introduced by the leveled compaction of LSM-Tree degrades the write performance of the key-value store and shortens the lifetime of the NVM devices. The existing studies propose new compaction methods to reduce write amplification. Unfortunately, they result in a relatively large read amplification. In this article, we propose NVLSM, a key-value store for NVM systems using LSM-Tree with new accumulative compaction. By fully utilizing the byte-addressability of NVM, accumulative compaction uses pointers to accumulate data into multiple floors in a logically sorted run to reduce the number of compactions required. We have also proposed a cascading searching scheme for reads among the multiple floors to reduce read amplification. Therefore, NVLSM reduces write amplification with small increases in read amplification. We compare NVLSM with key-value stores using LSM-Tree with two other compaction methods: leveled compaction and fragmented compaction. Our evaluations show that NVLSM reduces write amplification by up to 67% compared with LSM-Tree using leveled compaction without significantly increasing the read amplification. In write-intensive workloads, NVLSM reduces the average latency by 15.73%–41.2% compared to other key-value stores.

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      • Published in

        cover image ACM Transactions on Storage
        ACM Transactions on Storage  Volume 17, Issue 3
        August 2021
        227 pages
        ISSN:1553-3077
        EISSN:1553-3093
        DOI:10.1145/3477268
        • Editor:
        • Sam H. Noh
        Issue’s Table of Contents

        Copyright © 2021 Association for Computing Machinery.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 16 August 2021
        • Accepted: 1 March 2021
        • Revised: 1 February 2021
        • Received: 1 May 2020
        Published in tos Volume 17, Issue 3

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