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Disaggregated Database Systems

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Qizhen ZhangAuthors Info & Claims

SIGMOD '23: Companion of the 2023 International Conference on Management of Data

Pages 37 - 44

https://doi.org/10.1145/3555041.3589403

Published: 05 June 2023 Publication History

Abstract

Disaggregated database systems achieve unprecedented excellence in elasticity and resource utilization at the cloud scale and have gained great momentum from both industry and academia recently. Such systems are developed in response to the emerging trend of disaggregated data centers where resources are physically separated and connected through fast data center networks. Database management systems have been traditionally built based on monolithic architectures, so disaggregation fundamentally challenges the designs. On the other hand, disaggregation offers benefits like independent scaling of compute, memory, and storage. Nonetheless, there is a lack of systematic investigation into new research challenges and opportunities in recent disaggregated database systems.

To provide database researchers and practitioners with insights into different forms of resource disaggregation, we take a snapshot of state-of-the-art disaggregated database systems and related techniques and present an in-depth tutorial. The primary goal is to better understand the enabling techniques and characteristics of resource disaggregation and its implications for next-generation database systems. To that end, we survey recent work on storage disaggregation, which separates secondary storage devices (e.g., SSDs) from compute servers and is widely deployed in current cloud data centers, and memory disaggregation, which further splits compute and memory with Remote Direct Memory Access (RDMA) and is driving the transformation of clouds. In addition, we mention two techniques that bring novel perspectives to the above two paradigms: persistent memory and Compute Express Link (CXL). Finally, we identify several directions that shed light on the future development of disaggregated database systems.

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Cited By

Ordonez CMacyna WBellatreche L(2024)Energy-Aware Analytics in the CloudProceedings of the International Workshop on Big Data in Emergent Distributed Environments10.1145/3663741.3664789(1-6)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3663741.3664789
Pang XWang J(2024)Understanding the Performance Implications of the Design Principles in Storage-Disaggregated DatabasesProceedings of the ACM on Management of Data10.1145/36549832:3(1-26)Online publication date: 30-May-2024
https://doi.org/10.1145/3654983
Habich DPietrzyk JBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)SIMDified Data Processing - Foundations, Abstraction, and Advanced TechniquesCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654694(613-621)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654694
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Index Terms

Disaggregated Database Systems
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. DBMS engine architectures
      2. Parallel and distributed DBMSs
        Relational parallel and distributed DBMSs

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cover image ACM Conferences

SIGMOD '23: Companion of the 2023 International Conference on Management of Data

June 2023

330 pages

ISBN:9781450395076

DOI:10.1145/3555041

General Chairs:
Sudipto Das
Amazon Web Services, USA
,
Ippokratis Pandis
Amazon Web Services, USA
,
Program Chairs:
K. Selçuk Candan
Arizona State University, USA
,
Sihem Amer-Yahia
CNRS, Université Grenoble Alpes, France

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 05 June 2023

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https://dl.acm.org/doi/10.1145/3663741.3664789
Pang XWang J(2024)Understanding the Performance Implications of the Design Principles in Storage-Disaggregated DatabasesProceedings of the ACM on Management of Data10.1145/36549832:3(1-26)Online publication date: 30-May-2024
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https://dl.acm.org/doi/10.1145/3626246.3654694
Pan JWang JLi GBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Vector Database Management Techniques and SystemsCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654691(597-604)Online publication date: 9-Jun-2024
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Bi YLiu JZhao CZhao JRen KXiong L(2024)Share: Stackelberg-Nash based Data Markets2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00275(3573-3586)Online publication date: 13-May-2024
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Wang RGao CWang JKadam PTamerÖzsu MAref W(2024)Optimizing LSM-based indexes for disaggregated memoryThe VLDB Journal10.1007/s00778-024-00863-yOnline publication date: 19-Jun-2024
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