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International Workshop on In-Memory Data Management and Analytics

International Workshop on In-Memory Data Management and Analytics

IMDM 2013, IMDM 2014: In Memory Data Management and Analysis pp 112–125Cite as

Efficient Transaction Processing for Hyrise in Mixed Workload Environments

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8921))

Abstract

Hyrise is an in-memory storage engine designed for mixed enterprise workloads that originally started as a research prototype for hybrid table layouts and basic transaction processing capabilities. This paper presents our incremental improvements and learnings to better support transactional consistency in mixed workloads.

In particular, the paper addresses a multi-version concurrency control mechanism with lock-free commit steps, tree-based multi-column indices, in-memory optimized logging and recovery mechanisms. Additionally, a mixed workload scheduling mechanism is presented, addressing partition-able transactional workloads in combination with analytical queries.

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Notes

  1. 1.

    Source code available at https://github.com/hyrise/hyrise.

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Author information

Authors and Affiliations

  1. Hasso Plattner Institute, Potsdam, Germany

    David Schwalb, Martin Faust, Johannes Wust & Hasso Plattner

  2. eXascale Infolab, University of Fribourg, Fribourg, Switzerland

    Martin Grund

Authors
  1. David Schwalb
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  2. Martin Faust
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  3. Johannes Wust
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  4. Martin Grund
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  5. Hasso Plattner
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Corresponding author

Correspondence to David Schwalb .

Editor information

Editors and Affiliations

  1. Samsung Corporation, San Jose, California, USA

    Arun Jagatheesan

  2. Microsoft Corporation, Redmond, Washington, USA

    Justin Levandoski

  3. Technische Universität München, Garching, Germany

    Thomas Neumann

  4. Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Andrew Pavlo

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Schwalb, D., Faust, M., Wust, J., Grund, M., Plattner, H. (2015). Efficient Transaction Processing for Hyrise in Mixed Workload Environments. In: Jagatheesan, A., Levandoski, J., Neumann, T., Pavlo, A. (eds) In Memory Data Management and Analysis. IMDM IMDM 2013 2014. Lecture Notes in Computer Science(), vol 8921. Springer, Cham. https://doi.org/10.1007/978-3-319-13960-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-13960-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13959-3

  • Online ISBN: 978-3-319-13960-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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