Distributed NoSQL Data Stores: Performance Analysis and a Case Study

Abstract

NoSQL data-stores are commonly used to provide flexibility and availability for big data handling. However, there is a lack of comprehensive studies about which NoSQL data-store performs the best from the two scalability aspects, (scale-up, and scale-out), in a distributed and parallel processing environment. This paper compares the popular NoSQL data-stores (Cassandra, HBase, and MongoDB) and analyzes the resulting performance. Our experiments measure throughput, latency, and run-time of the evaluated data-stores on a big data set that consist of standard benchmarking workloads. Our results provide that the performance of each NoSQL data-store varies according to two main factors, (a) the type of executed operation, (read, scan, update, write, and insert), and (b) the level of distribution.