Skip to main content
SpringerLink
Log in
Book cover

Grid and Cloud Database Management pp 151–167Cite as

Virtualization and Column-Oriented Database Systems

  • Chapter
  • First Online:

  • 1141 Accesses

Abstract

Cloud and grid computing requires new data management paradigms, new data models, systems, and capabilities. Data-intensive systems such as column-oriented database systems will play an important role in cloud data management besides traditional databases. This chapter examines column-oriented databases in virtual environments and provides evidence that they can benefit from virtualization in cloud and grid computing scenarios. The major contributions involve: (1) the experimental results show that column-oriented databases are a good fit for cloud and grid computing; (2) it is demonstrated that they offer acceptable performance and response times, as well as better usage of virtual resources. Especially for high selectivity, CPU- and memory-intensive join queries virtual performance is better than nonvirtualized performance; (3) the performance data shows that in virtual environments they make good use of parallelism and have better support for clustering (parallel execution on multiple clustered VMs is faster than on a single VM with equal resources) due to data model, read-mostly data optimizations and hypervisor-level optimizations; and (4) analysis of the architectural and system underpinning contributing to these results.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Abadi, D.J., Madden, S.R., Hachem, N.: Column-stores vs. row-stores: How different are they really?. In: Proceedings of the 2008 ACM SIGMOD international Conference on Management of Data. SIGMOD. ACM, pp. 967–980 (2008)

    Google Scholar 

  2. Abadi, D.J., Marcus, A., Madden, S.R., Hollenbach, K.: Scalable semantic web data management using vertical partitioning. In: Proceedings of VLDB 2007, Vienna, Austria, 23–27 Sept 2007, pp. 411–422 (2007)

    Google Scholar 

  3. Apache Hadoop. http://hadoop.apache.org/core (2010)

  4. Apache HBase. http://hadoop.apache.org/hbase (2010)

  5. Apache Pig. http://incubator.apache.org/pig (2010)

  6. Architecture of MonetDB. http://monetdb.cwi.nl/projects/monetdb/MonetDB/Version4/Documentation/monet/index.html (2010)

  7. Barton Library Catalog Data. http://simile.mit.edu/rdf-test-data/barton/ (2010)

  8. Boncz, P.: Monet, a next-generation DBMS kernel for query-intensive applications, Doctoral Dissertation. CWI, 2002

    Google Scholar 

  9. Boncz, P.A., Zukowski, M., Nes, N.: MonetDB/X100: Hyper-Pipelining query execution. In: Proceedings of the Biennial Conference on Innovative Data Systems Research (CIDR), pp. 225–237, Asilomar, CA, USA, January 2005

    Google Scholar 

  10. Broekstra, J., A. Kampman, F. van Harmelen.: Sesame: A generic architecture for storing and querying RDF and RDF Schema. In: ISWC, pp. 54–68 (2002)

    Google Scholar 

  11. Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E.: Bigtable: a distributed storage system for structured data. In: Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation, vol. 7, Seattle, WA, 06–08 Nov 2006

    Google Scholar 

  12. Chong, E.I., Das, S., Eadon, G., Srinivasan, J.: An Efficient SQL-based RDF querying scheme. In: VLDB, pp. 1216–1227 (2005)

    Google Scholar 

  13. Copeland, G.P., Khoshafian, S.N.: A decomposition storage model. In: Proceedings of the 1985 ACM SIGMOD international Conference on Management of Data, Austin, Texas, USA. SIGMOD ‘85, pp. 268–279 (1985)

    Google Scholar 

  14. Dean, J., Ghemawat, S.: MapReduce: Simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation, vol. 6, San Francisco, CA, 06–08 Dec 2004

    Google Scholar 

  15. Harizopoulos, S., Liang, V., Abadi, D.J., Madden, S.: Performance tradeoffs in read-optimized databases. In: Dayal, U., Whang, K., Lomet, D., Alonso, G., Lohman, G., Kersten, M., Cha, S.K., Kim, Y. (eds.) Proceedings of the 32nd international Conference on Very Large Data Bases, Seoul, Korea, 12–15 Sept 2006. Very Large Data Bases, VLDB Endowment, pp. 487–498 (2006)

    Google Scholar 

  16. Harris, S., Gibbins, N.: 3store: Efficient bulk RDF storage. In: Proceedings of PSSS’03, pp. 1–15 (2003)

    Google Scholar 

  17. Holloway, A.L., DeWitt, D.J.: Read-optimized databases, in depth. In: Proceedings of VLDB Endow, vol. 1, issue 1, pp. 502–513. Aug 2008

    Google Scholar 

  18. Husain, F.M., Doshi, P., Khan, L., Thuraisingham, B.: Storage and retrieval of large RDF graph using Hadoop and MapReduce. In: Proceedings of International Conference on Cloud Computing, Beijing, China, 01–04 Dec 2009. LNCS, vol. 5931, pp. 680–686. Springer, Berlin (2009)

    Google Scholar 

  19. Ivanova, M., Kersten, M., Nes, N.: Self-organizing strategies for a column-store database. In: Proceedings of the 11th International Conference on Extending Database Technology (EDBT 2008), Nantes, France, 25–30 Mar 2008

    Google Scholar 

  20. Liu, J.F.: Distributed storage and query of large RDF graphs. Technical Report. The University of Texas at Austin, Austin, TX, USA (2010). http://userweb.cs.utexas.edu/ jayliu/reports/Queryof_Large_RDF_Graphs.pdf

  21. Longwell website. http://simile.mit.edu/longwell/

  22. Mika, P., Tummarello, G.: Web semantics in the clouds. IEEE Intell. Syst. 23(5), 82–87 (2008)

    Article  Google Scholar 

  23. MonetDB. http://monetdb.cwi.nl/

  24. OWL Web Ontology Language. Overview.W3C Recommendation. http://www.w3.org/TR/owl-features/ (2004)

  25. RDF Primer.W3C Recommendation. http://www.w3.org/TR/rdf-primer (2004)

  26. RDF Schema.W3C Specification Candidate. http://www.w3.org/TR/2000/CR-rdf-schema-20000327/ (2000)

  27. RDQL – A Query Language for RDF.W3C Member Submission 9 January 2004. http://www.w3.org/Submission/RDQL/ (2004)

  28. Scalable Storage Performance. VMWare Corp. White Paper. http://www.vmware.com/files/pdf/scalablestorage_performance.pdf (2008)

  29. Sidirourgos, L., Goncalves, R., Kersten, M., Nes, N., Manegold, S.: Column-store support for RDF data management: not all swans are white. In: Proceedings of VLDB Endowment, vol. 1, issue 2, Aug 2008, pp. 1553–1563 (2008)

    Google Scholar 

  30. SPARQL Query Language for RDF. W3C Working Draft 4 October 2006. http://www.w3.org/TR/rdf-sparql-query/ (2006)

  31. Stonebraker, M., Abadi, D.J., Batkin, A., Chen, X., Cherniack, M., Ferreira, M., Lau, E., Lin, A., Madden, S., ONeil, E., ONeil, P., Rasin, A., Tran, N., Zdonik S.: C-store: A column-oriented DBMS. In: Proceedings of the 31st VLDB Conference (2005)

    Google Scholar 

  32. Swoogle. http://swoogle.umbc.edu/

  33. VMware vSphere 4 Performance with Extreme I/O Workloads. VMWare Corp. White Paper. http://www.vmware.com/pdf/vsp4_extreme_io.pdf (2010)

  34. Wilkinson, K., Sayers, C., Kuno, H., Reynolds, D.: Efficient RDF storage and retrieval in Jena2. In: SWDB, pp. 131–150 (2003)

    Google Scholar 

  35. Wordnet rdf dataset. http://www.cogsci.princeton.edu/wn/

  36. World Wide Web Consortium (W3C). http://www.w3.org/

Download references

Acknowledgements

The authors thank Martin Karsten and Romulo Gonzalez for providing us with the semantic benchmark and for their kind assistance with setting it up and configuring MonetDB.

Author information

Authors and Affiliations

  1. Databases and Distributed Systems Group, TU-Darmstadt, Darmstadt, Germany

    Ilia Petrov, Vyacheslav Polonskyy & Alejandro Buchmann

Authors
  1. Ilia Petrov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vyacheslav Polonskyy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alejandro Buchmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ilia Petrov .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Dept. of Innovation Engineering, University of Salento, Via per Monteroni, 73100, Lecce, Italy

    Sandro Fiore Ph.D.

  2. Faculty of Engineering, Dept. of Innovation Engineering, University of Salento, Via per Monteroni, 73100, Lecce, Italy

    Giovanni Aloisio

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Petrov, I., Polonskyy, V., Buchmann, A. (2011). Virtualization and Column-Oriented Database Systems. In: Fiore, S., Aloisio, G. (eds) Grid and Cloud Database Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20045-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20045-8_8

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20044-1

  • Online ISBN: 978-3-642-20045-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation