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Main Memory Adaptive Denormalization

Authors:

Zezhou Liu,

Stratos IdreosAuthors Info & Claims

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

Pages 2253 - 2254

https://doi.org/10.1145/2882903.2914835

Published: 26 June 2016 Publication History

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Abstract

Joins have traditionally been the most expensive database operator, but they are required to query normalized schemas. In turn, normalized schemas are necessary to minimize update costs and space usage. Joins can be avoided altogether by using a denormalized schema instead of a normalized schema; this improves analytical query processing times at the tradeof increased update overhead, loading cost, and storage requirements.

In our work, we show that we can achieve the best of both worlds by leveraging partial, incremental, and dynamic denormalized tables to avoid join operators, resulting in fast query performance while retaining the minimized loading, update, and storage costs of a normalized schema.

We introduce adaptive denormalization for modern main memory systems. We replace the traditional join operations with efficient scans over the relevant partial universal tables without incurring the prohibitive cost of full denormalization.

References

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C. Balkesen, J. Teubner, G. Alonso, and M. T. Özsu. Main-memory hash joins on multi-core cpus: Tuning to the underlying hardware. In ICDE, pages 362--373, 2013.

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Cited By

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Idreos SCallaghan MMaier DPottinger RDoan ATan WAlawini ANgo H(2020)Key-Value Storage EnginesProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3383133(2667-2672)Online publication date: 11-Jun-2020
https://dl.acm.org/doi/10.1145/3318464.3383133
Cao YFan WYuan T(2019)Block as a value for SQL over NoSQLProceedings of the VLDB Endowment10.14778/3339490.333949812:10(1153-1166)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.14778/3339490.3339498
Idreos SKraska TBoncz PManegold SAilamaki ADeshpande AKraska T(2019)From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive SystemsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3314034(2054-2059)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3314034
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Index Terms

Main Memory Adaptive Denormalization
1. Information systems
  1. Data management systems
    1. Data structures
      1. Data access methods
      2. Data layout
    2. Database management system engines
      1. Database query processing
      2. Main memory engines

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Information

Published In

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

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2016

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Author Tag

adaptive denormalization

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Poster

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National Science Foundation

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SIGMOD/PODS'16

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SIGMOD

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

June 26 - July 1, 2016

California, San Francisco, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Idreos SCallaghan MMaier DPottinger RDoan ATan WAlawini ANgo H(2020)Key-Value Storage EnginesProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3383133(2667-2672)Online publication date: 11-Jun-2020
https://dl.acm.org/doi/10.1145/3318464.3383133
Cao YFan WYuan T(2019)Block as a value for SQL over NoSQLProceedings of the VLDB Endowment10.14778/3339490.333949812:10(1153-1166)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.14778/3339490.3339498
Idreos SKraska TBoncz PManegold SAilamaki ADeshpande AKraska T(2019)From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive SystemsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3314034(2054-2059)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3314034
Idreos SAthanassoulis MDayan NGuo DKester MMaas LZoumpatianos K(2019)Past and Future Steps for Adaptive Storage Data Systems: From Shallow to Deep AdaptivityReal-Time Business Intelligence and Analytics10.1007/978-3-030-24124-7_6(85-94)Online publication date: 11-Oct-2019
https://doi.org/10.1007/978-3-030-24124-7_6

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Query processing in main memory database management systems

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Query processing in main memory database management systems

Query processing in main memory database management systems

Redesign the Memory Allocator for Non-Volatile Main Memory

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