Abstract
Currently, cloud databases serve as mainstream data storage mechanism for unstructured data, primarily because of their high scalability and ease of availability. However, as yet, they lag behind RDBMs in terms of their support to developers for querying the data. The problem of developing frameworks to support flexible data queries is a very active area of research. In this work we consider HBase, a popular cloud database, inspired by Google’s BigTable. Relying on the recent Coprocessor feature of HBase, we have developed a framework that developers can use to implement aggregate functions like row count, max, min, etc. We further extended the existing Coprocessor framework to support a Cursor functionality, so that a client can incrementally consume the Coprocessor generated result. We demonstrate the effectiveness of our extension by comparatively evaluating it against the existing Scanner API with four queries on three different data sets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
(2011), http://www.odbms.org/download/dean-keynote-ladis2009.pdf
(2011), http://aws.amazon.com/
(2011), http://www.linkedin.com/pub/fabrice-veniard/4/389/153
Chang, F., Dean, J., Ghemawat, S., Hsieh, W., Wallach, D., Burrows, M., Chandra, T., Fikes, A., Gruber, R.: Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS) 26(2), 1–26 (2008)
Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)
Konstantinou, I., Angelou, E., Tsoumakos, D., Koziris, N.: Distributed indexing of web scale datasets for the cloud. In: Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud, pp. 1–6. ACM, New York (2010)
Li, N., Rao, J., Shekita, E., Tata, S.: Leveraging a scalable row store to build a distributed text index. In: Proceeding of the First International Workshop on Cloud Data Management, pp. 29–36. ACM, New York (2009)
Michel, J., Shen, Y., Aiden, A., Veres, A., Gray, M., Pickett, J., Hoiberg, D., Clancy, D., Norvig, P., Orwant, J., et al.: Quantitative analysis of culture using millions of digitized books. Science 331(6014), 176 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vashishtha, H., Stroulia, E. (2011). Enhancing Query Support in HBase via an Extended Coprocessors Framework. In: Abramowicz, W., Llorente, I.M., Surridge, M., Zisman, A., Vayssière, J. (eds) Towards a Service-Based Internet. ServiceWave 2011. Lecture Notes in Computer Science, vol 6994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24755-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-24755-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24754-5
Online ISBN: 978-3-642-24755-2
eBook Packages: Computer ScienceComputer Science (R0)