Performance testing of NoSQL and RDBMS for storing big data in e-applications

Šeda, Pavel; Hošek, Jiří; Mašek, Pavel; Pokorny, Jiri

doi:10.1109/igbsg.2018.8393559

Cited by 5 publications

(7 citation statements)

References 2 publications

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“…Many studies have been conducted in recent years to compare the efficiency of different optimization techniques on both relational and non-relational databases [14,15]. In [16], the authors presented a comparative study between relational and non-relational database models in a web-based application, by executing various operations on both relational and non-relational databases, thus highlighting the results obtained during performance comparison tests.…”

Section: Related Workmentioning

confidence: 99%

Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases

et al. 2021

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Databases are an important part of today’s applications where large amounts of data need to be stored, processed, and accessed quickly. One of the important criteria when choosing to use a database technology is its data processing performance. In this paper, some methods for optimizing the database structure and queries were applied on two popular open-source database management systems: MySQL as a relational DBMS, and document-based MySQL as a non-relational DBMS. The main objective of this paper was to conduct a comparative analysis of the impact that the proposed optimization methods have on each specific DBMS when carrying out CRUD (CREATE, READ, UPDATE, DELETE) requests. To perform the analysis and performance evaluation of CRUD operations for different amounts of data, a case study testing architecture based on Java was developed and used to show how the databases’ proposed optimization methods can influence the performance of the application, and to highlight the differences in response time and complexity. The results obtained show the degree to which the proposed optimization methods contributed to the application’s performance improvement in the case of both databases; based on these, a detailed analysis and several conclusions are presented to support a decision for choosing a specific approach.

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Section: Related Workmentioning

confidence: 99%

Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases

et al. 2021

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“…The authors propose in [1], a comparison and testing of Elasticsearch and MySQL databases, using the Spring Data framework. The comparison is completed by searching values in larger key-value datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Elasticsearch is a near-real-time search platform that allows data search as soon as the document was indexed. Elasticsearch allows dividing the data between several clusters, which makes it possible to support high-performance operations [1]. Each cluster represents one or more servers that store information and provide the ability to search for data.…”

Section: Using Non-relational Elasticsearch and Mysql Document Storementioning

confidence: 99%

“…Nowadays, most applications are accessed by a large number of users and also have to process a large amount of data in a short time, an important aspect that improves the user experience and application performance being the use of an appropriate database. A large variety of applications use relational databases that perform well when processing a small amount of data, but when the volume of data increases, the relational model proves to have serious limitations mainly due to a quite rigid schema that may become an obstacle to change [1][2][3], and thus the need for a new approach has emerged, namely non-relational databases.…”

Section: Introductionmentioning

confidence: 99%

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An Analysis of the Performance and Configuration Features of MySQL Document Store and Elasticsearch as an Alternative Backend in a Data Replication Solution

et al. 2021

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In recent years, with the increase in the volume and complexity of data, choosing a suitable database for storing huge amounts of data is not easy, because it must consider aspects such as manageability, scalability, and extensibility. Nowadays, the NoSQL databases have gained immense popularity for their efficiency in managing such datasets compared to relational databases. However, relational databases also exhibit some advantages in certain circumstances, therefore many applications use a combined approach: relational and non-relational. This paper performs a comparative evaluation of two popular open-source DBMSs: MySQL Document Store and Elasticsearch as non-relational DBMSs; this comparison is based on a detailed analysis of CRUD operations for different amounts of data showing how the databases could be modeled and used in an application. A case-study application was developed for this purpose in Java programming language and Spring framework using for data storage both relational MySQL and non-relational Elasticsearch and MySQL Document Store. To model the real situation encountered in several developed applications that use both relational and non-relational databases, a data replication solution that imports data from the primary relational MySQL database into Elasticsearch and MySQL Document Store as possible alternatives for more efficient data search was proposed and implemented.

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“…The authors of [3] conduct a performance evaluation for CRUD operations for several non-relational and relational databases (MongoDB, CouchDB, Couchbase, Microsoft SQL Server, MySQL, and PostgreSQL); the performance was evaluated based on the CRUD operations in terms of the time of queries and the size of data with and without replication; however, in this case, the analysis was oriented only on simple queries over a simple database structure. In [14], a comparison between Elasticsearch and MySQL via searching values in larger key-value datasets was made.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage

et al. 2020

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In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

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Performance testing of NoSQL and RDBMS for storing big data in e-applications

Cited by 5 publications

References 2 publications

Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases

Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases

An Analysis of the Performance and Configuration Features of MySQL Document Store and Elasticsearch as an Alternative Backend in a Data Replication Solution

Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage

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