Abstract
Optimal index configurations for in-memory databases differ significantly from configurations for their traditional disk-based counterparts. Operations like full column scans that have previously been prohibitively expensive in disk-based and row-oriented databases are now computationally feasible with columnar main memory-resident data structures and even outperform index-based accesses in many cases. Furthermore, index selection criteria are different for in-memory databases since maintenance costs are often lower while memory footprint considerations have become increasingly important.
In this paper, we introduce a workload-based and cost-aware index advisor tailored for columnar in-memory databases in mixed workload environments. We apply a memory traffic-driven model to estimate the efficiency of each index and to give a system-wide overview of the indices that are cost-ineffective with respect to their size and performance improvement. We also present our Index Advisor Cockpit applied to a real-world live production enterprise system of a Global 2000 company.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
HYRISE on Github: https://github.com/hyrise/hyrise.
- 2.
The Plan Cache of SAP HANA contains frequently executed query plans (including prepared SQL statements) as well as a number of monitoring statistics per plan, such as the aggregated execution count or the minimal/average/maximal run times.
- 3.
Global 2000: http://www.forbes.com/global2000/.
References
Chaudhuri, S., Narasayya, V.R.: Autoadmin ‘what-if’ index analysis utility. In: Proceedings ACM SIGMOD International Conference on Management of Data, SIGMOD 1998, Seattle, Washington, USA, pp. 367–378 (1998)
Dulloor, S., Roy, A., Zhao, Z., Sundaram, N., Satish, N., Sankaran, R., Jackson, J., Schwan, K.: Data tiering in heterogeneous memory systems. In: Proceedings of the Eleventh European Conference on Computer Systems, EuroSys 2016, London, United Kingdom, pp. 15: 1–15: 16, 18–21 April 2016
Färber, F., May, N., Lehner, W., Große, P., Müller, I., Rauhe, H., Dees, J.: The SAP HANA database - an architecture overview. IEEE Data Eng. Bull. 35(1), 28–33 (2012)
Faust, M., Schwalb, D., Krüger, J., Plattner, H.: Fast lookups for in-memory column stores: group-key indices, lookup and maintenance. In: International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures - ADMS 2012, pp. 13–22 (2012)
Finkelstein, S.J., Schkolnick, M., Tiberio, P.: Physical database design for relational databases. ACM Trans. Database Syst. 13(1), 91–128 (1988)
Funke, F., Kemper, A., Neumann, T.: Compacting transactional data in hybrid OLTP & OLAP databases. PVLDB 5(11), 1424–1435 (2012)
Grund, M., Krüger, J., Plattner, H., Zeier, A., Cudré-Mauroux, P., Madden, S.: HYRISE - a main memory hybrid storage engine. PVLDB 4(2), 105–116 (2010)
Kissinger, T., Kiefer, T., Schlegel, B., Habich, D., Molka, D., Lehner, W.: ERIS: a NUMA-aware in-memory storage engine for analytical workload. In: International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures - ADMS 2014, pp. 74–85 (2014)
Lang, H., Mühlbauer, T., Funke, F., Boncz, P., Neumann, T., Kemper, A.: Data blocks: hybrid OLTP and OLAP on compressed storage using both vectorization and compilation. In: International Conference on Management of Data, SIGMOD 2016, San Francisco, CA, USA (2016)
Manegold, S., Boncz, P.A., Kersten, M.L.: Optimizing database architecture for the new bottleneck: memory access. VLDB J. 9(3), 231–246 (2000)
Manegold, S., Boncz, P.A., Kersten, M.L.: Generic database cost models for hierarchical memory systems. In: Proceedings of 28th International Conference on Very Large Data Bases, VLDB 2002, pp. 191–202 (2002)
Papadomanolakis, S., Ailamaki, A.: An integer linear programming approach to database design. In: ICDE 2007, Istanbul, Turkey, pp. 442–449, 15–20 April 2007
Plattner, H.: The impact of columnar in-memory databases on enterprise systems. PVLDB 7(13), 1722–1729 (2014)
Plattner, H., Zeier, A.: In-Memory Data Management: An Inflection Point for Enterprise Applications, 1st edn. Springer, Heidelberg (2011)
Schwalb, D., Faust, M., Krueger, J., Plattner, H.: Physical column organization in in-memory column stores. In: Gao, H., Kim, J., Sakurai, Y. (eds.) DASFAA 2016. LNCS, vol. 9645, pp. 48–63. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37450-0_4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Boissier, M., Djürken, T., Schlosser, R., Faust, M. (2016). A Cost-Aware and Workload-Based Index Advisor for Columnar In-Memory Databases. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2016. Communications in Computer and Information Science, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-46254-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-46254-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46253-0
Online ISBN: 978-3-319-46254-7
eBook Packages: Computer ScienceComputer Science (R0)