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Proteus: Autonomous Adaptive Storage for Mixed Workloads

Authors:

Khuzaima DaudjeeAuthors Info & Claims

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

Pages 700 - 714

https://doi.org/10.1145/3514221.3517834

Published: 11 June 2022 Publication History

Abstract

Enterprises use distributed database systems to meet the demands of mixed or hybrid transaction/analytical processing (HTAP) workloads that contain both transactional (OLTP) and analytical (OLAP) requests. Distributed HTAP systems typically maintain a complete copy of data in row-oriented storage format that is well-suited for OLTP workloads and a second complete copy in column-oriented storage format optimized for OLAP workloads. Maintaining these data copies consumes significant storage space and system resources. Conversely, if a system stores data in a single format, OLTP or OLAP workload performance suffers. This paper presents Proteus, a distributed HTAP database system that adaptively and autonomously selects and changes its storage layout to optimize for mixed workloads. Proteus generates physical execution plans that utilize storage-aware operators for efficient transaction execution. Using comprehensive HTAP workloads and state-of-the-art comparison systems, we demonstrate that Proteus delivers superior HTAP performance while providing OLTP and OLAP performance on par with designs specialized for either type of workload.

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Costa CChrysanthis PHerodotou HCosta MStavrakis ENicolaou N(2025)A Multiple Compression Approach using Attribute-based SignaturesOpen Research Europe10.12688/openreseurope.19247.15(49)Online publication date: 10-Feb-2025
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Index Terms

Proteus: Autonomous Adaptive Storage for Mixed Workloads
1. Information systems
  1. Data management systems
    1. Database administration
      1. Autonomous database administration
    2. Database management system engines
      1. Parallel and distributed DBMSs
  2. Information storage systems
    1. Record storage systems
      1. Relational storage
        Hybrid storage layouts

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cover image ACM Conferences

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

June 2022

2597 pages

ISBN:9781450392495

DOI:10.1145/3514221

General Chair:
Zachary Ives
University of Pennsylvania (USA)
,
Program Chairs:
Angela Bonifati
Lyon 1 University (France)
,
Amr El Abbadi
University of California, Santa Barbara (USA)

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Published: 11 June 2022

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https://doi.org/10.12688/openreseurope.19247.1
Yu GWu ZKossmann FLi TMarkakis MNgom AMadden SKraska T(2024)Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRADProceedings of the VLDB Endowment10.14778/3681954.368202617:11(3629-3643)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3682026
Hansert PMichel S(2024)Partition, Don't Sort! Compression Boosters for Cloud Data Ingestion PipelinesProceedings of the VLDB Endowment10.14778/3681954.368201317:11(3456-3469)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3682013
ZHANG LZHU YAN YZI YWANG J(2024)The optimization problem of system load balancing and its solution for industrial Internet-of-Things data managementSCIENTIA SINICA Informationis10.1360/SSI-2023-021154:10(2343)Online publication date: 30-Sep-2024
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Zhang CLi GZhang JZhang XFeng J(2024)HTAP Databases: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338969336:11(6410-6429)Online publication date: Nov-2024
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