, Volume 17, Issue 3, pp 223–232 | Cite as

Energy Efficiency in Main-Memory Databases

  • Stefan NollEmail author
  • Henning Funke
  • Jens Teubner


As the operating costs of today’s data centres continue to increase and processor manufacturers are forced to meet thermal design power constraints when designing new hardware, the energy efficiency of a main-memory database management system becomes more and more important. Plus, lots of database workloads are more memory-intensive than compute-intensive, which results in computing power being unused and wasted. This can become a problem because wasting computing also means wasting electrical power.

In this paper, we experimentally study the impact of reducing the clock frequency of the processor and the impact of using fewer processor cores on the energy efficiency of common database algorithms such as scans, simple aggregations, simple hash joins, and state-of-the-art join algorithms. We stress the fundamental trade-off between peak performance and energy efficiency, as opposed to the established race-to-idle strategy. Ultimately, we show that reducing unused computing power significantly improves the energy efficiency of memory-bound database algorithms.



This work was supported by the DFG, Collaborative Research Center SFB 876, A2.


  1. 1.
    Balkesen C, Alonso G, Teubner J, Özsu MT (2013) Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited. Proc Vldb Endow 7(1):85–96CrossRefGoogle Scholar
  2. 2.
    Barber R, Lohman G, Pandis I, Raman V, Sidle R, Attaluri G, Chainani N, Lightstone S, Sharpe D (2014) Memory-Efficient Hash Joins. Proc Vldb Endow 8(4):353–364CrossRefGoogle Scholar
  3. 3.
    Barroso LA, Hölzle U (2007) The Case for Energy-Proportional Computing. Computer (Long Beach Calif) 40(12):33–37Google Scholar
  4. 4.
    Esmaeilzadeh H, Blem E, Amant Sankaralingam Burger StRKD (2011) Dark Silicon and the End of Multicore Scaling. Sigarch Comput Archit News 39(3):365–376CrossRefGoogle Scholar
  5. 5.
    Harizopoulos S, Shah MA, Meza J, Ranganathan P (2009) Energy Efficiency: The New Holy Grail of Data Management Systems Research. Biennial Conference on Innovative Data Systems Research (CIDR) January 4-7, 2009. Asilomar, California (arXiv preprint arXiv:0909.1784)Google Scholar
  6. 6.
    Intel Corporation (2016) Intel 64 and IA-32 Architectures Software Developer’s Manual – Volume 3BGoogle Scholar
  7. 7.
    Lang W, Harizopoulos S, Patel JM, Shah MA, Tsirogiannis D (2012) Towards Energy-Efficient Database Cluster Design. Proc Vldb Endow 5(11):1684–1695CrossRefGoogle Scholar
  8. 8.
    Poess M, Nambiar RO (2008) Energy Cost, the Key Challenge of Today’s Data Centers: A Power Consumption Analysis of TPC-C Results. Proc Vldb Endow 1(2):1229–1240CrossRefGoogle Scholar
  9. 9.
    Psaroudakis I, Kissinger T, Porobic D, Ilsche T, Liarou E, Tözün P, Ailamaki A, Lehner W (2014) Dynamic Fine-grained Scheduling for Energy-Efficient Main-memory Queries. Proc Damon Acm Pp 1(7):1–1Google Scholar
  10. 10.
    Schall D, Härder T (2013) Energy-proportional Query Execution Using a Cluster of Wimpy Nodes. Proceedings of the International Workshop on Data Management on New Hardware. ACM, DaMoN ’13, pp 1:1–1:6Google Scholar
  11. 11.
    Tsirogiannis D, Harizopoulos S, Shah MA (2010) Analyzing the Energy Efficiency of a Database Server. Proceedings of the SIGMOD International Conference on Management of Data. ACM, SIGMOD ’10, pp 231–242Google Scholar
  12. 12.
    Tu Y, Wang X, Zeng B, Xu Z (2014) A System for Energy-Efficient Data Management. Sigmod Rec 43(1):21–26CrossRefGoogle Scholar
  13. 13.
    Unified EFI, Inc (2016) Advanced Configuration and Power Interface Specification 6.1. Google Scholar
  14. 14.
    Willhalm T, Dementiev R, Fay P (2016) Intel Performance Counter Monitor - A better way to measure CPU utilization. Google Scholar
  15. 15.
    Xu Z, Tu Y, Wang X (2010) Exploring Power-Performance Tradeoffs in Database Systems. Proceedings of the International Conference on Data Engineering (ICDE)., pp 485–496Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2017

Authors and Affiliations

  1. 1.Databases and Information Systems GroupTU Dortmund UniversityDortmundGermany

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