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SAP HANA adoption of non-volatile memory

Published: 01 August 2017 Publication History
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  • Abstract

    Non-Volatile RAM (NVRAM) is a novel class of hardware technology which is an interesting blend of two storage paradigms: byte-addressable DRAM and block-addressable storage (e.g. HDD/SSD). Most of the existing enterprise relational data management systems such as SAP HANA have their internal architecture based on the inherent assumption that memory is volatile and base their persistence on explicit handling of block-oriented storage devices. In this paper, we present the early adoption of Non-Volatile Memory within the SAP HANA Database, from the architectural and technical angles. We discuss our architectural choices, dive deeper into a few challenges of the NVRAM integration and their solutions, and share our experimental results. As we present our solutions for the NVRAM integration, we also give, as a basis, a detailed description of the relevant HANA internals.

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      cover image Proceedings of the VLDB Endowment
      Proceedings of the VLDB Endowment  Volume 10, Issue 12
      August 2017
      427 pages
      ISSN:2150-8097
      Issue’s Table of Contents

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      VLDB Endowment

      Publication History

      Published: 01 August 2017
      Published in PVLDB Volume 10, Issue 12

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      • (2024)MTM: Rethinking Memory Profiling and Migration for Multi-Tiered Large MemoryProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650075(803-817)Online publication date: 22-Apr-2024
      • (2023)NVM: Is it Not Very Meaningful for Databases?Proceedings of the VLDB Endowment10.14778/3603581.360358616:10(2444-2457)Online publication date: 1-Jun-2023
      • (2023)WiscSort: External Sorting for Byte-Addressable StorageProceedings of the VLDB Endowment10.14778/3598581.359858516:9(2103-2116)Online publication date: 1-May-2023
      • (2023)Elastic Use of Far Memory for In-Memory Database Management SystemsProceedings of the 19th International Workshop on Data Management on New Hardware10.1145/3592980.3595311(35-43)Online publication date: 18-Jun-2023
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      • (2021)Optimizing in-memory database engine for AI-powered on-line decision augmentation using persistent memoryProceedings of the VLDB Endowment10.14778/3446095.344610214:5(799-812)Online publication date: 1-Jan-2021
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