Resiliency-aware Data Compression for In-memory Database Systems

Till Kolditz, Dirk Habich, Patrick Damme, Wolfgang Lehner, Dmitrii Kuvaiskii, Oleksii Oleksenko, Christof Fetzer


Nowadays, database systems pursuit a main memory-centric architecture, where the entire business-related data is stored and processed in a compressed form in main memory. In this case, the performance gain is massive because database operations can benefit from its higher bandwidth and lower latency. However, current main memory-centric database systems utilize general-purpose error detection and correction solutions to address the emerging problem of increasing dynamic error rate of main memory. The costs of these generalpurpose methods dramatically increases with increasing error rates. To reduce these costs, we have to exploit context knowledge of database systems for resiliency. Therefore, we introduce our vision of resiliency-aware data compression in this paper, where we want to exploit the benefits of both fields in an integrated approach with low performance and memory overhead. In detail, we present and evaluate a first approach using AN encoding and two different compression schemes to show the potentials and challenges of our vision.


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Paper Citation

in Harvard Style

Kolditz T., Habich D., Damme P., Lehner W., Kuvaiskii D., Oleksenko O. and Fetzer C. (2015). Resiliency-aware Data Compression for In-memory Database Systems . In Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-103-8, pages 326-331. DOI: 10.5220/0005557303260331

in Bibtex Style

author={Till Kolditz and Dirk Habich and Patrick Damme and Wolfgang Lehner and Dmitrii Kuvaiskii and Oleksii Oleksenko and Christof Fetzer},
title={Resiliency-aware Data Compression for In-memory Database Systems},
booktitle={Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA,},

in EndNote Style

JO - Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Resiliency-aware Data Compression for In-memory Database Systems
SN - 978-989-758-103-8
AU - Kolditz T.
AU - Habich D.
AU - Damme P.
AU - Lehner W.
AU - Kuvaiskii D.
AU - Oleksenko O.
AU - Fetzer C.
PY - 2015
SP - 326
EP - 331
DO - 10.5220/0005557303260331