Performance analysis of NoSQL and relational databases with MongoDB and MySQL

Jose, Benymol; Abraham, Sajimon

doi:10.1016/j.matpr.2020.03.634

Cited by 61 publications

(30 citation statements)

References 2 publications

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“…However, few NoSQL Databases are supported only by these frameworks, so the programmer has to make designs for data models of an application and choose a proper strategy for data mapping. The authors in [ 35 ] presented a model to study the performance of querying in NoSQL and relational databases with MongoDB and MySQL. Santana LH et al [ 36 ] presented an analysis of mapping strategies for storing RDF data into NoSQL databases.…”

Section: Related Workmentioning

confidence: 99%

An adaptive spark-based framework for querying large-scale NoSQL and relational databases

Khashan

El-Desouky

Elghamrawy³

2021

PLoS ONE

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The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others’ efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios’ benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.

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Section: Related Workmentioning

confidence: 99%

An adaptive spark-based framework for querying large-scale NoSQL and relational databases

Khashan

El-Desouky

Elghamrawy³

2021

PLoS ONE

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“…MongoDB adalah sistem manajemen basis data (DBMS) berbasis sumber terbuka yang menggunakan model basis data berorientasi dokumen yang mendukung berbagai jenis data (Jose & Abraham, 2019). MongoDB tidak menggunakan tabel dan baris seperti dalam basis data relasional, arsitektur MongoDB dibentuk dari Collection dan Document, (Chaudhary et al, 2018).…”

Section: Mongodbunclassified

Evaluasi Penggunaan Prometheus dan Grafana Untuk Monitoring Database Mongodb

Ramadoni

Amirudin²,

Fahmi³

et al. 2021

JIP

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Sebuah basis data dituntut untuk selalu dalam keadaan optimal, karena hal tersebut merupakan faktor penting dalam sebuah Sistem Informasi Manajemen. Untuk menjaga sebuah sistem basis data agar selalu optimal tentu saja dibutuhkan mekanisme monitoring yang dapat memberikan informasi kepada pihak yang berkepentingan sehingga setiap kejadian dapat terpantau, sehingga apabila diperlukan sebuah tindakan preventif maupun korektif dapat segera dilakukan. Pemilihan perangkat monitoring perlu mempertimbangkan beberapa faktor seperti fleksibilitas, kemampuan untuk diintegrasikan dengan sistem yang lain. Prometheus dan Grafana adalah kombinasi yang bagus untuk melakukan monitoring pada sistem basis data, tidak hanya terbatas pada sistem sebuah basis data MongoDB, kemampuan Prometheus untuk menyimpan basis data dalam bentuk time series memungkinkan Prometheus untuk menyimpan data dalam jumlah besar. Sedangkan tampilan visualisasi Grafana yang bisa dikustomisasi membuka peluang untuk kita membuat visualisasi dan mendapatkan visibilitas sesuai dengan kebutuhan. Penggunaan Prometheus dan Grafana yang bersifat opensource juga menjadi pilihan untuk mengurangi biaya karena tidak membutuhkan lisensi untuk penggunaannya bahkan di lingkungan komersial sekalipun.

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“…MongoDB benchmark results are promising even with enormous amounts of data, performing even better than MySQL [65]. Traffic is normally managed through load balancers, such as Nginx [66], which allow to distribute the requests between different servers, then scaling the solution.…”

Section: Performance Validationmentioning

confidence: 99%

Enabling Identity for the IoT-as-a-Service Business Model

Diego¹,

Regueiro

Maciá‐Fernández

2021

IEEE Access

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The IoT-as-a-Service (IoTaaS) business model has already been identified by some people from both industry and academia, but has not been formally defined. IoTaaS offers IoT devices on demand, with considerable cost savings and resource optimization. In addition, it enables different applications to reuse the existing devices. However, this business model is associated with different technological challenges that need to be addressed, one of which is the identity problem. Focusing on this, self-sovereign identity (SSI) schemes have proven to provide better privacy and scalability than traditional identity paradigms, which is especially important in the IoT owing to its characteristics. In this paper, we formally analyze an IoTaaS business model, identifying and detailing its main technological challenges. In addition, we tackle the identity problem of this business model and propose an SSI-based identity management system, which is compliant with the existing standards from the W3C, and include a performance evaluation.

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Performance analysis of NoSQL and relational databases with MongoDB and MySQL

Cited by 61 publications

References 2 publications

An adaptive spark-based framework for querying large-scale NoSQL and relational databases

An adaptive spark-based framework for querying large-scale NoSQL and relational databases

Evaluasi Penggunaan Prometheus dan Grafana Untuk Monitoring Database Mongodb

Enabling Identity for the IoT-as-a-Service Business Model

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