research-article

A storage advisor for hybrid-store databases

Authors:

Philipp Rösch,

Lars Dannecker,

Gregor HackenbroichAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 5, Issue 12

Pages 1748 - 1758

https://doi.org/10.14778/2367502.2367514

Published: 01 August 2012 Publication History

Abstract

With the SAP HANA database, SAP offers a high-performance in-memory hybrid-store database. Hybrid-store databases---that is, databases supporting row- and column-oriented data management---are getting more and more prominent. While the columnar management offers high-performance capabilities for analyzing large quantities of data, the row-oriented store can handle transactional point queries as well as inserts and updates more efficiently. To effectively take advantage of both stores at the same time the novel question whether to store the given data row- or column-oriented arises. We tackle this problem with a storage advisor tool that supports database administrators at this decision. Our proposed storage advisor recommends the optimal store based on data and query characteristics; its core is a cost model to estimate and compare query execution times for the different stores. Besides a per-table decision, our tool also considers to horizontally and vertically partition the data and manage the partitions on different stores. We evaluated the storage advisor for the use in the SAP HANA database; we show the recommendation quality as well as the benefit of having the data in the optimal store with respect to increased query performance.

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Cited By

Hamirwasia VKarlapalem KValluri S(2019)FASTProceedings of Real-Time Business Intelligence and Analytics10.1145/3350489.3350490(1-10)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1145/3350489.3350490
Durand GPinnecke MPiriyev RMohsen MBroneske DSaake GSekeran MRodriguez FBalami L(2018)GridFormationProceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management10.1145/3211954.3211956(1-7)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1145/3211954.3211956
Kester MAthanassoulis MIdreos SChirkova RYang JSuciu D(2017)Access Path Selection in Main-Memory Optimized Data SystemsProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064049(715-730)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3064049
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Index Terms

A storage advisor for hybrid-store databases
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 5, Issue 12

August 2012

340 pages

ISSN:2150-8097

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VLDB Endowment

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Published: 01 August 2012

Published in PVLDB Volume 5, Issue 12

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Cited By

Hamirwasia VKarlapalem KValluri S(2019)FASTProceedings of Real-Time Business Intelligence and Analytics10.1145/3350489.3350490(1-10)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1145/3350489.3350490
Durand GPinnecke MPiriyev RMohsen MBroneske DSaake GSekeran MRodriguez FBalami L(2018)GridFormationProceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management10.1145/3211954.3211956(1-7)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1145/3211954.3211956
Kester MAthanassoulis MIdreos SChirkova RYang JSuciu D(2017)Access Path Selection in Main-Memory Optimized Data SystemsProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064049(715-730)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3064049
Lu WWang YJiang JLiu JShen YWei B(2017)Hybrid storage architecture and efficient MapReduce processing for unstructured dataParallel Computing10.1016/j.parco.2017.08.00869:C(63-77)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.parco.2017.08.008
Basu DLin QChen WVo HYuan ZSenellart PBressan S(2016)Regularized Cost-Model Oblivious Database Tuning with Reinforcement LearningTransactions on Large-Scale Data- and Knowledge-Centered Systems XXVIII - Volume 994010.1007/978-3-662-53455-7_5(96-132)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/978-3-662-53455-7_5
Nguyen-Cong DD'Orazio LTran NHacid M(2016)Storing and Querying DICOM Data with HYTORMOProceedings of the Second International Workshop on Data Management and Analytics for Medicine and Healthcare - Volume 1018610.1007/978-3-319-57741-8_4(43-61)Online publication date: 9-Sep-2016
https://dl.acm.org/doi/10.1007/978-3-319-57741-8_4
Alagiannis IIdreos SAilamaki ADyreson CLi FÖzsu M(2014)H2OProceedings of the 2014 ACM SIGMOD International Conference on Management of Data10.1145/2588555.2610502(1103-1114)Online publication date: 18-Jun-2014
https://dl.acm.org/doi/10.1145/2588555.2610502
Chabane Yd'Orazio LGruenwald LMohamad BRey C(2013)Medical data management in the SYSEO projectACM SIGMOD Record10.1145/2536669.253667842:3(48-53)Online publication date: 17-Oct-2013
https://dl.acm.org/doi/10.1145/2536669.2536678

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