Skip to content

Measuring elasticity for cloud databases

The rise of the Internet and the multiplication of data sources have multiplied the number of “Bigdata” storage problems. These data sets are not only very big but also tend to grow very fast, sometimes in a short period. Distributed databases that work well for such data sets need to be not only scalable but also elastic to ensure a fast response to growth in demand of computing power or storage. The goal of this article is to present measurement results that characterize the elasticity of three databases. We have chosen Cassandra, HBase, and mongoDB as three representative popular horizontally scalable NoSQL databases that are in production use. We have made measurements under realistic loads up to 48 nodes, using the Wikipedia database to create our dataset and using the Rackspace cloud infrastructure. We define precisely our methodology and we introduce a new dimensionless measure for elasticity to allow uniform comparisons of different databases at different scales. Our results show clearly that the technical choices taken by the databases have a strong impact on the way they react when new nodes are added to the clusters.

Thibault Dory, Boris Mejías, Peter Van Roy, and Nam-Luc Tran, Measuring Elasticity for Cloud Databases, proceedings of the Cloud Computing 2011 (Second International Conference on Cloud Computing, GRIDs, and Virtualization), Rome, Italy, September 2011.

Click here to access the paper.

Releated Posts

2022 Wrap Up

We got a deep dive into some of the most memorable moments of 2022.
Read More

IEEE Big Data 2022: the key takeaways

In December 2022, our research director Sabri Skhiri travelled to Osaka to attend IEEE Big Data 2022. He sums up the main trends, and shares his favourite talks and papers.
Read More