research-article

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Design Tradeoffs of Data Access Methods

Authors:

Manos Athanassoulis,

Stratos IdreosAuthors Info & Claims

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Pages 2195 - 2200

https://doi.org/10.1145/2882903.2912569

Published: 26 June 2016 Publication History

Abstract

Database researchers and practitioners have been building methods to store, access, and update data for more than five decades. Designing access methods has been a constant effort to adapt to the ever changing underlying hardware and workload requirements. The recent explosion in data system designs - including, in addition to traditional SQL systems, NoSQL, NewSQL, and other relational and non-relational systems - makes understanding the tradeoffs of designing access methods more important than ever. Access methods are at the core of any new data system. In this tutorial we survey recent developments in access method design and we place them in the design space where each approach focuses primarily on one or a subset of read performance, update performance, and memory utilization. We discuss how to utilize designs and lessons-learned from past research. In addition, we discuss new ideas on how to build access methods that have tunable behavior, as well as, what is the scenery of open research problems.

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Index Terms

Design Tradeoffs of Data Access Methods
1. Information systems
  1. Data management systems
    1. Data structures
      1. Data access methods
        Data scans
        Multidimensional range search
        Point lookups
        Unidimensional range search

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cover image ACM Conferences

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

June 2016

2300 pages

ISBN:9781450335317

DOI:10.1145/2882903

General Chairs:
Fatma Özcan
IBM Research, USA
,
Georgia Koutrika
HP Labs, USA
,
Program Chair:
Sam Madden
Massachusetts Institute of Technology, USA

Copyright © 2016 ACM.

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Published: 26 June 2016

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SIGMOD/PODS'16: International Conference on Management of Data

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https://doi.org/10.1109/SoutheastCon52093.2024.10500105
Shaikhha AKelepeshis MGhorbani MDubach CBruening DHardekopf B(2023)Fine-Tuning Data Structures for Query ProcessingProceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization10.1145/3579990.3580016(149-161)Online publication date: 17-Feb-2023
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Jackowski AŚlusarczyk ŁLichota KWełnicki MWijata RKielar MKopeć TDubnicki CIwanicki K(2023)ObjDedup: High-Throughput Object Storage Layer for Backup Systems With Block-Level DeduplicationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.325050134:7(2180-2197)Online publication date: Jul-2023
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Sarkar SDayan NAthanassoulis M(2023)The LSM Design Space and its Read Optimizations2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00273(3578-3584)Online publication date: Apr-2023
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