Data Partitioning for Highly Scalable Cloud Applications
Robert Neumann, Matthias Baumann, Reiner R. Dumke, Andreas Schmietendorf · IGI Global eBooks · 2012
Cloud computing has brought new challenges, but also exciting chances to developers. With the illusion of an infinite expanse of computing resources, even individual developers have been put into a position from which they can create applications that scale out all over the world, thus affecting millions of people. One difficulty with developing such mega-scale Cloud applications is to keep the storage backend scalable. In this chapter, we detail ways of partitioning non-relational data among thousands of physical storage nodes, thereby emphasizing the peculiarities of tabular Cloud storage. The authors give recommendations of how to establish a sustainable and scaling data management architecture that – while growing in terms of data volume – still provides the same high throughput. Finally, they underline their theoretical elaborations by featuring insights won from a real-world cloud project with which the authors have been involved.