Abstract
As discussed in Chap. 5, SanssouciDB is a database designed to run transactional and analytical workloads in enterprise computing. The underlying data set can easily reach a size of several terabytes in large companies. Although memory capacities of commodity servers are growing, it is still expensive to process those huge data sets entirely in main memory. Therefore, SanssouciDB and several other modern in-memory storage engines use compression techniques on top of the initial dictionary encoding to decrease the total memory requirements. Columnar storage of data is well suited for compression, as data of the same type and domain is stored consecutively and can thus be processed efficiently.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
D. Abadi, S. Madden, M. Ferreira, Integrating compression and execution in column-oriented database systems, in Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, SIGMOD ’06 (ACM, New York 2006), pp. 671–682
C. Lemke, K.-U. Sattler, F. Faerber, A. Zeier, Speeding up queries in column stores, in Data Warehousing and Knowledge Discovery. Lecture Notes in Computer Science, vol. 6263 (Springer, Heidelberg, 2010), pp. 117–129
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Plattner, H. (2014). Compression. In: A Course in In-Memory Data Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55270-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-55270-0_7
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-55269-4
Online ISBN: 978-3-642-55270-0
eBook Packages: Business and EconomicsBusiness and Management (R0)