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Workshop on Big Data Benchmarks

Workshop on Big Data Benchmarks

WBDB 2015, WBDB 2015: Big Data Benchmarking pp 40–67Cite as

Towards a General Array Database Benchmark: Measuring Storage Access

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10044))

Abstract

Array databases have set out to close an important gap in data management, as multi-dimensional arrays play a key role in science and engineering data and beyond. Even more, arrays regularly contribute to the “Big Data” deluge, such as satellite images, climate simulation output, medical image modalities, cosmological simulation data, and datacubes in statistics. Array databases have proven advantageous in flexible access to massive arrays, and an increasing number of research prototypes is emerging. With the advent of more implementations a systematic comparison becomes a worthwhile endeavor.

In this paper, we present a systematic benchmark of the storage access component of an Array DBMS. It is designed in a way that comparable results are produced regardless of any specific architecture and tuning. We apply this benchmark, which is available in the public domain, to three main proponents: rasdaman, SciQL, and SciDB. We present the benchmark and its design rationales, show the benchmark results, and comment on them.

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Notes

  1. 1.

    “Spatial” in this context includes any axis, be it spatial, temporal, or of some other semantics in an application context.

  2. 2.

    Except in SciQL which does not support partitioning.

  3. 3.

    Standard deviation.

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Author information

Authors and Affiliations

  1. Jacobs University Bremen, Campus Ring 1, 28759, Bremen, Germany

    George Merticariu & Peter Baumann

  2. rasdaman GmbH, Hans-Hermann-Sieling-Str. 17, 28759, Bremen, Germany

    Dimitar Misev

Authors
  1. George Merticariu
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  2. Dimitar Misev
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  3. Peter Baumann
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Corresponding author

Correspondence to Peter Baumann .

Editor information

Editors and Affiliations

  1. Technical University of Berlin, Berlin, Germany

    Tilmann Rabl

  2. Cisco Systems, Inc., San Jose, California, USA

    Raghunath Nambiar

  3. University of California at San Diego, La Jolla, California, USA

    Chaitanya Baru

  4. Ampool, Inc., Santa Clara, California, USA

    Milind Bhandarkar

  5. Oracle Corporation, Redwood Shores, California, USA

    Meikel Poess

  6. Indian Institute of Public Health, Hyderabad, India

    Saumyadipta Pyne

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Merticariu, G., Misev, D., Baumann, P. (2016). Towards a General Array Database Benchmark: Measuring Storage Access. In: Rabl, T., Nambiar, R., Baru, C., Bhandarkar, M., Poess, M., Pyne, S. (eds) Big Data Benchmarking. WBDB WBDB 2015 2015. Lecture Notes in Computer Science(), vol 10044. Springer, Cham. https://doi.org/10.1007/978-3-319-49748-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-49748-8_3

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