NoSQL Databases: A Software Engineering Perspective

For over forty years, relational databases have been the leading model for data storage, retrieval and management. However, due to increasing needs for scalability and performance, alternative systems have emerged, namely NoSQL technology. The rising interest in NoSQL technology, as well as the growth in the number of use case scenarios, over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies. While most research work mostly focuses on performance evaluation using standard benchmarks, it is important to notice that the architecture of real world systems is not only driven by performance requirements, but has to comprehensively include many other quality attribute requirements. Software quality attributes form the basis from which software engineers and architects develop software and make design decisions. Yet, there has been no quality attribute focused survey or classification of NoSQL databases where databases are compared with regards to their suitability for quality attributes common on the design of enterprise systems. To fill this gap, and aid software engineers and architects, in this article, we survey and create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use case scenarios from the software engineer point of view and the quality attributes that each of them is most suited to.

show abstract

“…This article is a revised and extended version of our WorldCIST 2015 paper [13]. It improves and complements the former in the following aspects:…”

Section: Introductionmentioning

confidence: 93%

Choosing the right NoSQL database for the job: a quality attribute evaluation

Lourenço

Cabral

Carreiro

et al. 2015

Journal of Big Data

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this regard, different types of solutions have been proposed and implemented. As Costa et al [25] and Costa and Santos [26] suggest, some considered implementing DWs in NoSQL databases, albeit these solutions only scale the operational systems (see [27] for a comparison of NoSQL engines). Eventually, SQL on-Hadoop emerged as a more efficient solution for Big Data contexts [25], [26], [28], [29].…”

Section: Big Data Warehousingmentioning

confidence: 99%

On the use of simulation as a Big Data semantic validator for supply chain management

Vieira

Dias

Santos

et al. 2020

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

Simulation stands out as an appropriate method for the Supply Chain Management (SCM) field. Nevertheless, to produce accurate simulations of Supply Chains (SCs), several business processes must be considered. Thus, when using real data in these simulation models, Big Data concepts and technologies become necessary, as the involved data sources generate data at increasing volume, velocity and variety, in what is known as a Big Data context. While developing such solution, several data issues were found, with simulation proving to be more efficient than traditional data profiling techniques in identifying them. Thus, this paper proposes the use of simulation as a semantic validator of the data, proposed a classification for such issues and quantified their impact in the volume of data used in the final achieved solution. This paper concluded that, while SC simulations using Big Data concepts and technologies are within the grasp of organizations, their data models still require considerable improvements, in order to produce perfect mimics of their SCs. In fact, it was also found that simulation can help in identifying and bypassing some of these issues.

show abstract

“…With the increasing growth in popularity of NoSQL databases over the last ten years, research work in performance evaluation has been increasingly conducted also on these emerging database systems. Prior work has focused on comparing NoSQL systems in terms of read/write performance, scalability and flexibility [6,22,2,17]. To the best of our knowledge only a limited number of works have been conducted on performance modelling of NoSQL databases, and available models specific to Cassandra are few [13,25].…”

Section: Related Workmentioning

confidence: 99%

A Queueing Network Model for Performance Prediction of Apache Cassandra

Dipietro¹,

Casale²,

Serazzi³

2017

Proceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools

View full text Add to dashboard Cite

NoSQL databases such as Apache Cassandra have attracted large interest in recent years thanks to their high availability, scalability, flexibility and low latency. Still there is limited research work on performance engineering methods for NoSQL databases, which yet are needed since these systems are highly distributed and thus can incur significant cost/performance trade-offs. To address this need, we propose a novel queueing network model for the Cassandra NoSQL database aimed at supporting resource provisioning. The model defines explicitly key configuration parameters of Cassandra such as consistency levels and replication factor, allowing engineers to compare alternative system setups.Experimental results based on the YCSB benchmark indicate that, with a small amount of training for the estimation of its input parameters, the proposed model achieves good predictive accuracy across different loads and consistency levels. The average performance errors of the model compared to the real results are between 6% and 10%. We also demonstrate the applicability of our model to other NoSQL databases and other possible utilisation of it.

show abstract

NoSQL Databases: A Software Engineering Perspective

Cited by 27 publications

References 17 publications

Choosing the right NoSQL database for the job: a quality attribute evaluation

Choosing the right NoSQL database for the job: a quality attribute evaluation

On the use of simulation as a Big Data semantic validator for supply chain management

A Queueing Network Model for Performance Prediction of Apache Cassandra

Contact Info

Product

Resources

About