A scalable generic transaction model scenario for distributed NoSQL databases

Ramesh, Dharavath; Kumar, Chiranjeev

doi:10.1016/j.jss.2014.11.037

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…However, this method depends on the precision of the client clock. In literature [25] [26], the authors implemented a transaction support function of multi-row data for HBase using two-phase commit protocol. In literature [27], the author implemented a middle layer of transaction support for MongoDB database using four-phase commit protocol.…”

Section: Snapshot Isolation Technology Aims To Improve Concurrency Anmentioning

confidence: 99%

A Surfing Concurrence Transaction Model for Key-Value NoSQL Databases

Li¹,

Gu²

2018

JSEA

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As more and more application systems related to big data were developed, NoSQL (Not Only SQL) database systems are becoming more and more popular. In order to add transaction features for some NoSQL database systems, many scholars have tried different techniques. Unfortunately, there is a lack of research on Redis's transaction in the existing literatures. This paper proposes a transaction model for key-value NoSQL databases including Redis to make possible allowing users to access data in the ACID (Atomicity, Consistency, Isolation and Durability) way, and this model is vividly called the surfing concurrence transaction model. The architecture, important features and implementation principle are described in detail. The key algorithms also were given in the form of pseudo program code, and the performance also was evaluated. With the proposed model, the transactions of Key-Value NoSQL databases can be performed in a lock free and MVCC (Multi-Version Concurrency Control) free manner. This is the result of further research on the related topic, which fills the gap ignored by relevant scholars in this field to make a little contribution to the further development of NoSQL technology.

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Section: Snapshot Isolation Technology Aims To Improve Concurrency Anmentioning

confidence: 99%

A Surfing Concurrence Transaction Model for Key-Value NoSQL Databases

Li¹,

Gu²

2018

JSEA

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“…Column-oriented databases are highly scalable and eventually consistent. They provide support to applications for reliable and highly available storage (Dharavath and Kumar, 2015). The application areas of column-oriented databases are customer record analysis, data warehousing, patient data management, library systems, and wherever analysis is required to aggregate similar data items.…”

Section: Fig 4 Examples Of Data Models: (A) Key-value; (B) Column-ormentioning

confidence: 99%

Big data storage technologies: a survey

Siddiqa

Karim

Gani

2017

Frontiers Inf Technol Electronic Eng

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Abstract:There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed 'big data'. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mechanism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the existing approaches using Brewer's CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.

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PHash: A memory-efficient, high-performance key-value store for large-scale data-intensive applications

Shim

2017

Journal of Systems and Software

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A scalable generic transaction model scenario for distributed NoSQL databases

Cited by 9 publications

References 29 publications

A Surfing Concurrence Transaction Model for Key-Value NoSQL Databases

A Surfing Concurrence Transaction Model for Key-Value NoSQL Databases

Big data storage technologies: a survey

PHash: A memory-efficient, high-performance key-value store for large-scale data-intensive applications

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