SQL Database with physical database tuning technique and NoSQL graph database comparisons

Khan, Wisal; Waqas, Ahmad; Luo, Bin; Ahmed, Ejaz

doi:10.1109/itnec.2019.8729264

Cited by 22 publications

(10 citation statements)

References 3 publications

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“…In previous studies, Neo4j typically outperformed SQL databases. In study [4] for example, Neo4j outperformed Oracle in various tests using count(*) queries. In the present study, aggregation queries were also used but the result was different.…”

Section: Discussionmentioning

confidence: 97%

“…SQL databases and Neo4j have been compared in several studies [4][5][6][7][8]. Khan et al compared tuned Oracle 11 g and Neo4j 3.03 Community Edition [4].…”

Section: Related Workmentioning

confidence: 99%

“…SQL databases and Neo4j have been compared in several studies [4][5][6][7][8]. Khan et al compared tuned Oracle 11 g and Neo4j 3.03 Community Edition [4]. They used healthcare data, including data of patients, medication, and medical staff.…”

Section: Related Workmentioning

confidence: 99%

“…Previous studies [4][5][6][7][8] where the performance of graph databases, especially Neo4j, was compared with the traditional SQL databases, indicate that graph databases possess better performance than relational databases. However, those studies mainly focused on quite simple queries.…”

Section: Introductionmentioning

confidence: 99%

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Performance of Graph and Relational Databases in Complex Queries

2022

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In developing NoSQL databases, a major motivation is to achieve better efficient query performance compared with relational databases. The graph database is a NoSQL paradigm where navigation is based on links instead of joining tables. Links can be implemented as pointers, and following a pointer is a constant time operation, whereas joining tables is more complicated and slower, even in the presence of foreign keys. Therefore, link-based navigation has been seen as a more efficient query approach than using join operations on tables. Existing studies strongly support this assumption. However, query complexity has received less attention. For example, in enterprise information systems, queries are usually complex so data need to be collected from several tables or by traversing paths of graph nodes of different types. In the present study, we compared the query performance of a graph-based database system (Neo4j) and relational database systems (MySQL and MariaDB). The effect of different efficiency issues (e.g., indexing and optimization) were included in the comparison in order to investigate the most efficient solutions for different query types. The outcome is that although Neo4j is more efficient for simple queries, MariaDB is essentially more efficient when the complexity of queries increases. The study also highlighted how dramatically the efficiency of relational database has grown during the last decade.

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Section: Discussionmentioning

confidence: 97%

“…SQL databases and Neo4j have been compared in several studies [4][5][6][7][8]. Khan et al compared tuned Oracle 11 g and Neo4j 3.03 Community Edition [4].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance of Graph and Relational Databases in Complex Queries

2022

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“…Terlebih lagi jika jumlah datanya ribuan atau disebut Big Data [14]. Pemilihan DBMS ini menjadi hal yang penting karena tidak hanya performa yang baik tetapi juga kecepatan DBMS dalam memproses data [15].…”

Section: Pendahuluanunclassified

Perbandingan Performa Respon Waktu Kueri Mysql, Postgresql, Dan Mongodb

Putra

Purwaningrum

Winata

2022

sibc

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Teknologi telah mempengaruhi digitalisasi pada fungsi pekerjaan pada berbagai instansi. Pekerjaan dalam setiap fungsi kerja pada instansi dapat secara mudah dilakukan dengan adanya teknologi. Penggunaan teknologi berupa aplikasi membutuhkan sistem database untuk menyimpan data-data dan informasi pada suatu instansi. DBMS pada dasarnya terbagi menjadi 2 jenis yaitu DBMS relasional dan non-relasional. Contoh dari DBMS relasional adalah MySQL dan PostgreSQL, sedangkan DBMS non-relasional adalah MongoDB. Penelitian ini mengukur waktu yang diperlukan setiap DBMS dalam mengeksekusi query. Penelitian ini melakukan eksekusi pada berbagai skenario query, yaitu INSERT (create), SELECT (read), UPDATE (update), DELETE (delete), SUM, dan COUNT. Dataset yang digunakan dalam penelitian ini berasal dari open data website kaggle yaitu data tentang permainan catur yang terdiri dari 16 kolom dan 20.058 baris data. Hasil dari penelitian ini yaitu PostgreSQL yang paling cepat waktu respon eksekusi query dibanding DBMS lain.

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Look at, Look into and Look Around: A New Approach to Visualizing Network Data and Increasing the Quality of Decisions

Beltrán

Geng

2022

Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2

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SQL Database with physical database tuning technique and NoSQL graph database comparisons

Cited by 22 publications

References 3 publications

Performance of Graph and Relational Databases in Complex Queries

Performance of Graph and Relational Databases in Complex Queries

Perbandingan Performa Respon Waktu Kueri Mysql, Postgresql, Dan Mongodb

Look at, Look into and Look Around: A New Approach to Visualizing Network Data and Increasing the Quality of Decisions

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