Leanstore: in-memory data management beyond main memory

Leis V, Haubenschild M, Kemper A, Neumann T (2018)

Publication Type: Conference contribution

Publication year: 2018

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 185-196

Conference Proceedings Title: Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

Event location: Paris FR

ISBN: 9781538655207

DOI: 10.1109/ICDE.2018.00026

Abstract

Disk-based database systems use buffer managers in order to transparently manage data sets larger than main memory. This traditional approach is effective at minimizing the number of I/O operations, but is also the major source of overhead in comparison with in-memory systems. To avoid this overhead, in-memory database systems therefore abandon buffer management altogether, which makes handling data sets larger than main memory very difficult. In this work, we revisit this fundamental dichotomy and design a novel storage manager that is optimized for modern hardware. Our evaluation, which is based on TPC-C and micro benchmarks, shows that our approach has little overhead in comparison with a pure in-memory system when all data resides in main memory. At the same time, like a traditional buffer manager, it is fully transparent and can manage very large data sets effectively. Furthermore, due to low-overhead synchronization, our implementation is also highly scalable on multi-core CPUs.

Authors with CRIS profile

Viktor Leis

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Tableau Software, Inc. US United States (USA) (US)

How to cite

APA:

Leis, V., Haubenschild, M., Kemper, A., & Neumann, T. (2018). Leanstore: in-memory data management beyond main memory. In Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018 (pp. 185-196). Paris, FR: Institute of Electrical and Electronics Engineers Inc..

MLA:

Leis, Viktor, et al. "Leanstore: in-memory data management beyond main memory." Proceedings of the 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris Institute of Electrical and Electronics Engineers Inc., 2018. 185-196.

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