Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database

Matallah, Houcine; Belalem, Ghalem; Bouamrane, Karim

doi:10.4018/ijssci.2021070104

Cited by 18 publications

(6 citation statements)

References 29 publications

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“…The majority of NoSQL systems are supported by this tool, which is crossplatform and easy to customize for these solutions. It has been used in several research studies (17)(18)(19)(20)(21) and is widely used to compare the performance of NoSQL database management systems in the cloud. A data generator and a group of performance tests for adding, updating, and deleting items make up YCSB.…”

Section: Methodsmentioning

confidence: 99%

A Study of Performance and Comparison of NoSQL Databases: MongoDB, Cassandra, and Redis Using YCSB

Kausar¹,

Nasar²,

Soosaimanickam³

2022

IJST

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Background/Objectives: Relational databases are a commonly utilized technology that allows for the storage, administration, and retrieval of various data schemas. However, for certain big databases, executing queries can become a time-consuming and inefficient procedure. Furthermore, storing enormous volumes of data necessitates servers with greater capacity and scalability. Relational databases have limits when it comes to dealing with scalability for big amounts of data. On the other hand, non-relational database systems, often known as NoSQL, were created to better fulfill the demands of key-value storing of enormous volumes of records. However, there are several NoSQL options, and the majority have not yet been extensively compared. The goal of this research is to examine different NoSQL databases and evaluate their performance in terms of typical data storage and retrieval. Methods: In this study, we use the YCSB tool to measure the performance of three NoSQL databases: MongoDB, Cassandra, and Redis. We test six different workloads with 100000, 250000, 500000, 750000, and 1000000 operations. Our test was designed with five different operations, i.e., 100000, 250000, 500000, 750000, and 1000000, with six different workloads to see which database is most suitable for applications which use a large amount of data to process. Findings: MongoDB is a superior performing NoSQL database among Cassandra and Redis. The numerous optimizations used by the designers of NoSQL solutions to improve performance, such as good cache memory operation, have a direct impact on the execution time. In all workloads except workload D, MongoDB has significantly reduced latency across all operation counts. Novelty: We also measure the average latency of different workload scenarios that include a mix of read, write, and update activities.

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Section: Methodsmentioning

confidence: 99%

A Study of Performance and Comparison of NoSQL Databases: MongoDB, Cassandra, and Redis Using YCSB

Kausar¹,

Nasar²,

Soosaimanickam³

2022

IJST

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“…3) It can provide good SQL query management tools to facilitate database operation and management [22]. ( 4) With high security, the database has a data backup and recovery function, ensuring data storage security [23].…”

Section: Monitoring Module Of Network Abnormalitymentioning

confidence: 99%

Application of Big Data Analysis and Cloud Computing Technology

Zhang

2022

Preprint

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With the swift growth of computer science, technologies such as big data and artificial intelligence are widely used in various fields of modern society. The types of network equipment and the scope of network coverage have also increased rapidly. While the network brings convenience to people, more attention must be paid to the security of the network platform. The purpose is to safely and effectively manage the current rapidly growing Internet data and improve the ability to detect abnormal network behaviors. Combining big data technology and machine learning (ML), the application of big data analysis and cloud computing technology for network security are studied. Firstly, the data collection technology of abnormal network behavior is introduced, and the Flume data collection component and Kafka distributed technology are discussed. Secondly, the data processing process and corresponding algorithm processing of abnormal network behavior are analyzed, including ML framework and stream processing technology. Finally, the model of network abnormal behavior detection based on big data is constructed, and compared with the related model based on the decision tree and random forest (RF) algorithm, and verified by experiments. The verification results reveal that among the 42 attack types against the dataset, the detection accuracy of network abnormal behavior by big data is 96.4%, and the false positive rate is 2.23%, which is higher than that of decision tree and RF algorithm. This experimental study denotes that the network abnormal behavior detection technology of big data based on the ML framework can effectively improve the type and efficiency of network abnormal behavior detection, and has certain reference significance for improving network security management and control capabilities.

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“…Its syntax is similar to an object-oriented retrieval language and it is dynamically retrievable and fully indexed by basically implementing most of the functionality in relational database forms retrieval. Moreover, it has an efficient way of storing data and has the rich features of a non-relational database [15]. The intelligent high-formula support monitoring system first stores the received data in MongoDB, then processes it with a Python script and dumps it in MySQL.…”

Section: The System Design and Implementationmentioning

confidence: 99%

An Intelligent High-formwork Support Monitoring System

Chen

Jian²,

Lin³

et al. 2022

Artificial Intelligence Evolution

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The high-formwork support is to form and maintain newly poured concrete so that there should be no deformation in the shape. Deflection and shrinkage should be minimum. The intelligent monitoring of high-formwork support based on big data has been dramatically promoted with the advancement of science and technology. The standard of living is constantly improving, and safety is frequently the top priority. The construction industry pays close attention to the safety of high-formwork support during the construction process. Traditional high-formwork support monitoring is based on manual observation and measurement, which is not only time-consuming and labor-intensive but also produces insignificant monitoring data. Because of the strict requirements for pouring quality and construction safety, the traditional high-formwork support has been unable to meet the demand. We must combine information technology to provide the best monitoring means for high-formwork support and effective guarantees for building construction safety. In this paper, we propose an intelligent high-formwork support monitoring system. Every two seconds, the system can obtain equipment monitoring data, which is automatically transmitted. It transmits monitoring data from the collector to the database, thereby avoiding high-formwork support accidents. Decision trees are also used in the analysis of monitoring data. As a result, three decision rules emerge. The main advantages of the intelligent high-formwork support monitoring system are that it is frequently monitored and allows for real-time monitoring. The proposed intelligent high-formwork support monitoring system ensures the safety of the high-formwork support during construction.

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Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database

Cited by 18 publications

References 29 publications

A Study of Performance and Comparison of NoSQL Databases: MongoDB, Cassandra, and Redis Using YCSB

A Study of Performance and Comparison of NoSQL Databases: MongoDB, Cassandra, and Redis Using YCSB

Application of Big Data Analysis and Cloud Computing Technology

An Intelligent High-formwork Support Monitoring System

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