Skip to main content

Working with Cassandra Database

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 701))

Abstract

Traditional databases cannot handle a huge amount of data. NoSQL database like Cassandra can handle large data with easier management and lower cost compared to SQL databases like Oracle and other relational databases. Cassandra is an open source technology. This paper explains how Cassandra helps in handling large data efficiently with almost 30–35% more efficient than the relational database like Oracle when data size increases to ten thousand and beyond. For this, the paper explains an experiment which was carried out by varying the size of number of data records and comparing the performance of Cassandra and Oracle. As the data size was continuously increased, most of the Cassandra queries took almost 30–35% less time as compared to Oracle. Though Oracle was more efficient when data size was less (up to 40 × 103 records), the performance dropped thereafter continuously for every 10 × 103 increase in data size thereafter.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Hewitt, E.: The Definitive Guide, O’Reilly Media, 2nd edn. Cassandra (2016)

    Google Scholar 

  2. Abdi, D.: Problems with CAP and Yahoo’s little known NoSQL system http://dbmsmusings.blogspot.com/2010/04/problems-with-cap-and-yahoos-little.html, Yale University (2010)

  3. Harrison, G.: Next Generation Databases, NoSQL, New SQL (2015)

    Chapter  Google Scholar 

  4. McMurtry, D., Oakley, A., Sharp, J., Subramanian, M., Zhang, H.: Data Access for Highly Scalable Solutions: Using SQL, NoSQL and Polyglot persistence (2013)

    Google Scholar 

  5. Rabl, T., Gómez-Villamor, S., Sadoghi, M., Muntés-Mulero, V., Jacobsen, H.A., Mankovskii, S.: Solving big data challenges for enterprise application performance management. Proc. VLDB Endow. 5(12), 1724–1735 (2012)

    Article  Google Scholar 

  6. Eure, I.: Looking to the future with Cassandra http://about.digg.com/blog/looking-future-cassandra, Sept (2009)

  7. Date, C.J.: The New Relational Database Dictionary (2015)

    Google Scholar 

  8. Ellis, J., et. al.: Cassandra Gossiper Architecture http://wiki.apache.org/cassandra/ArchitectureGossip (2010)

  9. Coulouris, G., Dollimore, J., Kindberg, T.: Distributed Systems: Concepts and Design Addison Wesley (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saurabh Anand .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Anand, S., Singh, P., Sagar, B.M. (2018). Working with Cassandra Database. In: Satapathy, S., Tavares, J., Bhateja, V., Mohanty, J. (eds) Information and Decision Sciences. Advances in Intelligent Systems and Computing, vol 701. Springer, Singapore. https://doi.org/10.1007/978-981-10-7563-6_55

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7563-6_55

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7562-9

  • Online ISBN: 978-981-10-7563-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics