No SQL in Practice: A Write-Heavy Enterprise Application

Lourenço, João Ricardo; Abramova, Veronika; Cabral, Bruno; Bernardino, Jorge; Carreiro, Paulo; Vieira, Marco

doi:10.1109/bigdatacongress.2015.90

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…They observed that there is no difference between these two databases in performance, and concluded NoSQL databases still should be improved. Lourenco et al [28] evaluated Cassandra, CouchDB and MongoDB for a write-intensive application. The results revealed that Cassandra is better than the other NoSQL databases for a fournode setup, while a MS SQL Server running on a single node outperformed all NoSQL contenders for these specific settings.…”

Section: Related Workmentioning

confidence: 99%

The Impact of Distance on Performance and Scalability of Distributed Database Systems in Hybrid Clouds

Mansouri¹,

Babar²

2020

Preprint

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The increasing need for managing big data has led the emergence of advanced database management systems, e.g., NoSQL or SQL Clusters, which are usually deployed and operated on a cloud computing infrastructure. These database management systems are also called "big data management solutions", whose performance and scalability play a critical role in the operations of "big data applications". Recently, there has been increased efforts aimed at evaluating the performance and scalability of "big data management solutions" hosted by either private or public cloud datacenters. However, there has been little work on evaluating the performance and scalability of "big data management solutions" in hybrid cloud arrangement, where an enterprise leverages a public cloud along with it's own private cloud for it's processing or storage needs. In the hybrid cloud model, the distance between private and public cloud datacenters can be one of the key factors that can affect the throughput performance (simply throughput) of "big data management solutions." Hence, any evaluation of the performance and scalability of "big data management solutions" in a hybrid cloud arrangement needs to consider the impact of distance between a private cloud and a public cloud.In this article, we present a detailed evaluation of throughput performance, scalability, and VMs size vs. VMs number for six modern databases (MongoDB, Cassandra, Riak, CouchDB, Redis, and MySQL Cluster) in a hybrid cloud arrangement, consisting of a private cloud in Adelaide and Azure based public cloud datacenters located in Sydeny, Mumbai and Virginia regions. Based systematic and thorough evaluation, we make the following important observations. First, as the distance between private and public clouds increases, the throughput performance of most databases reduces. Second, MongoDB obtains the best throughput performance, followed by MySQL Cluster under the default setting in terms of replicas number and consistency mechanism. Whilst Cassandra exposes the most fluctuation in through performance, the throughput of other databases initially reduces and then increases as the number of nodes burst into the public cloud increases. Third, vertical scalability improves the throughput of databases more than the horizontal scalability. Forth, exploiting bigger VMs (i.e., a VM with more cores) rather than more VMs with less cores can increase throughput performance for Cassandra by a factor of at most 800%, for Riak by 30%, and for Redis by 10%.

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Section: Related Workmentioning

confidence: 99%

The Impact of Distance on Performance and Scalability of Distributed Database Systems in Hybrid Clouds

Mansouri¹,

Babar²

2020

Preprint

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“…They includes processing methods such as data collection [33], data mining [34] and data aggregation [35]. The processed source data is stored on cloud in relational or NoSQL databases [36]. Lastly, service layer answers information requests submitted by consumers by integrating information stored in cloud.…”

Section: A Cloud Storage For Big Datamentioning

confidence: 99%

A Data Sharing Protocol to Minimize Security and Privacy Risks of Cloud Storage in Big Data Era

Han

Zhang

2019

IEEE Access

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A cloud-based big data sharing system utilizes a storage facility from a cloud service provider to share data with legitimate users. In contrast to traditional solutions, cloud provider stores the shared data in the large data centers outside the trust domain of the data owner, which may trigger the problem of data confidentiality. This paper proposes a secret sharing group key management protocol (SSGK) to protect the communication process and shared data from unauthorized access. Different from the prior works, a group key is used to encrypt the shared data and a secret sharing scheme is used to distribute the group key in SSGK. The extensive security and performance analyses indicate that our protocol highly minimizes the security and privacy risks of sharing data in cloud storage and saves about 12% of storage space.INDEX TERMS Big data, security and privacy, cloud storage, data sharing.

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“…It is not replacement of SQL instead it is compliment to SQL. It provides an easy way to store unstructured data from various sources [4,16]. There are various issues that relational model cannot address, so in that case nonrelational databases are used to provide more scalability and superior performance.…”

Section: IIImentioning

confidence: 99%

Real-Time Applications of Big Data- A Survey

Rubal¹

2016

IJERT

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With the rapid growth of technologies, large amount of data is produced from different sources that can either be structured or unstructured. Such type of data is very difficult to process and manage that contains millions records of information that includes social media, web sales, audios, videos, images etc. Timely analytics of this data is a key factor in success in many business and service domains. Examples of these domains are intelligent transport, healthcare, security, finance and military. To improve quality of information and decision making it is important to effectively analyze this large volume of data to answer new challenges.

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No SQL in Practice: A Write-Heavy Enterprise Application

Cited by 9 publications

References 25 publications

The Impact of Distance on Performance and Scalability of Distributed Database Systems in Hybrid Clouds

The Impact of Distance on Performance and Scalability of Distributed Database Systems in Hybrid Clouds

A Data Sharing Protocol to Minimize Security and Privacy Risks of Cloud Storage in Big Data Era

Real-Time Applications of Big Data- A Survey

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