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HB+tree: use hadoop and HBase even your data isn't that big

Published: 13 April 2015 Publication History

Abstract

Cloud providers store on databases for their users an increasing large number of data. These data are most of the time multi-dimensional and so applications using these data must have some type of indexing on them to perform queries on them efficiently. This indexing scheme must be scalable and also has low maintenance cost. The main task of this paper is a Comparison between a New Novel Hadoop-based distributed B+-tree, which index HBase nodes (the so-called HB+-tree:HBase B+-tree) and the concurrent local B+-tree in I/O model. We try to examine where each of these implementations are better and for which parameters we can achieve better performance. We conduct extensive experiments for both indexing methods, and the results demonstrate that for each studied use case the Cloud version outperforms the Centralized implementation, which is not always obvious according to [4].

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Cited By

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  • (2020)An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBaseISPRS International Journal of Geo-Information10.3390/ijgi91106259:11(625)Online publication date: 25-Oct-2020
  • (2017)On the analysis of big data indexing execution strategiesJournal of Intelligent & Fuzzy Systems10.3233/JIFS-16926932:5(3259-3271)Online publication date: 24-Apr-2017
  • (2017)In-Memory Distributed Indexing for Large-Scale Media Data Retrieval2017 IEEE International Symposium on Multimedia (ISM)10.1109/ISM.2017.38(232-239)Online publication date: Dec-2017
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    cover image ACM Conferences
    SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
    April 2015
    2418 pages
    ISBN:9781450331968
    DOI:10.1145/2695664
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 13 April 2015

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    Author Tags

    1. B+-trees
    2. HBase
    3. cloud databases
    4. data structures
    5. distributed systems
    6. hadoop
    7. multi-dimensional indexing
    8. spatial databases

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    • Research-article

    Funding Sources

    • Greek national funds
    • European Union (European Social Fund - ESF)

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    SAC 2015
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    SAC 2015: Symposium on Applied Computing
    April 13 - 17, 2015
    Salamanca, Spain

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    SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    Cited By

    View all
    • (2020)An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBaseISPRS International Journal of Geo-Information10.3390/ijgi91106259:11(625)Online publication date: 25-Oct-2020
    • (2017)On the analysis of big data indexing execution strategiesJournal of Intelligent & Fuzzy Systems10.3233/JIFS-16926932:5(3259-3271)Online publication date: 24-Apr-2017
    • (2017)In-Memory Distributed Indexing for Large-Scale Media Data Retrieval2017 IEEE International Symposium on Multimedia (ISM)10.1109/ISM.2017.38(232-239)Online publication date: Dec-2017
    • (2017)NSM-Tree: Efficient Indexing on Top of NoSQL DatabasesAlgorithmic Aspects of Cloud Computing10.1007/978-3-319-57045-7_1(3-14)Online publication date: 11-Apr-2017

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