Performance of Graph and Relational Databases in Complex Queries
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6490 |
| _version_ | 1797480931040165888 |
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| author | Petri Kotiranta Marko Junkkari Jyrki Nummenmaa |
| author_facet | Petri Kotiranta Marko Junkkari Jyrki Nummenmaa |
| author_sort | Petri Kotiranta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:07:16Z |
| format | Article |
| id | doaj.art-38ef865179294e9c9a5176fc7ce289ce |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:07:16Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-38ef865179294e9c9a5176fc7ce289ce2023-11-23T19:37:37ZengMDPI AGApplied Sciences2076-34172022-06-011213649010.3390/app12136490Performance of Graph and Relational Databases in Complex QueriesPetri Kotiranta0Marko Junkkari1Jyrki Nummenmaa2Faculty of Information Technology and Communication Sciences, Computing Sciences, Tampere University, 33100 Tampere, FinlandFaculty of Information Technology and Communication Sciences, Computing Sciences, Tampere University, 33100 Tampere, FinlandFaculty of Information Technology and Communication Sciences, Computing Sciences, Tampere University, 33100 Tampere, FinlandIn 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.https://www.mdpi.com/2076-3417/12/13/6490graph databaserelational databaseperformancecomplex queriesNeo4JMariaDB |
| spellingShingle | Petri Kotiranta Marko Junkkari Jyrki Nummenmaa Performance of Graph and Relational Databases in Complex Queries Applied Sciences graph database relational database performance complex queries Neo4J MariaDB |
| title | Performance of Graph and Relational Databases in Complex Queries |
| title_full | Performance of Graph and Relational Databases in Complex Queries |
| title_fullStr | Performance of Graph and Relational Databases in Complex Queries |
| title_full_unstemmed | Performance of Graph and Relational Databases in Complex Queries |
| title_short | Performance of Graph and Relational Databases in Complex Queries |
| title_sort | performance of graph and relational databases in complex queries |
| topic | graph database relational database performance complex queries Neo4J MariaDB |
| url | https://www.mdpi.com/2076-3417/12/13/6490 |
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