NoSQL Overview and Performance Testing of HBase Over Multiple Nodes with MySQL

Das, Nabanita; Paul, Swagata; Sarkar, Bidyut Biman; Chakrabarti, Satyajit

doi:10.1007/978-981-13-1498-8_24

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…However, this point must be clearly investigated before a decision in favor of such a solution is taken. For some queries, the time may be same or higher than that offered by conventional RDBMS solutions [62]. Typical use cases are recapitulated in Table 1.…”

Section: Wide-column Data Modelmentioning

confidence: 99%

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

Khan¹,

Liu²,

Ali³

et al. 2023

Advances in Computers

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Section: Wide-column Data Modelmentioning

confidence: 99%

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

Khan¹,

Liu²,

Ali³

et al. 2023

Advances in Computers

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“…This is because Flink's scalability is limited for such queries. The proposed system runs on top of Flink without any changes in the structure of Flink (Das et al, 2019). It translates advanced SQL query translations into Flink codes to execute advanced SQL queries in Flink (Gouda et al, 2016).…”

Section: Translatormentioning

confidence: 99%

Complex SQL-NoSQL Query Translation for Data Lake Management

Nurhadi¹,

Kadir²,

Surin³

2022

Journal of Computer Science

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A data lake refers to an extremely large data resource or repository. Data lakes store large amounts of data and use advanced analytics to pair data from multiple sources with different types of structured, semistructured, and unstructured information. NoSQL databases such as Mongodb, Redis, Neo4j, and Cassandra are nontabular and they store data differently rather than use relational tables. NoSQL databases come in many forms, mostly documents, key values, wide columns, and graphs based on their data model. NoSQL gives less complicated scalability and higher overall performance as compared with traditional relational databases. NoSQL databases can store different types of data, but they cannot fully support Automation, Consistency, Isolation, and endurance (ACID) features, i.e., trigger functions in multi-transaction management, because a NoSQL database uses a non-relational database system. Thus, an interpreter is necessary for SQL-to-NoSQL queries. We used ANTLR (ANother Tool for Language Recognition), which has five main stages: The input SQL query, the tokenizer, the parser, the parser tree, and lastly the generation of the query results in NoSQL. The tool gave users to write a flexible multi-pass language parser that is expected to solve problems in querying complex ACID functions and other problems in complex queries in NoSQL databases. In the measurement, analysis, and evaluation of the translation results through the comparison of each NoSQL criterion against a Relational Database Management System (MySQL), the scores obtained were as follows. The performance criterion achieved the highest score (98.40%) by the MongoDB database, followed by scalability (97.40%) and accuracy (97.00%). The criterion with the lowest score was complexity (91.65%).

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“…The authors reported that Mongodb execution time is lower than MySQL for all types of operations. In [Das et al 2019], loading and data scanning metrics have been used to compare MySQL and NoSQL HBase platforms. The author reported that HBase performs better in these metrics than MySQL.…”

Section: Related Workmentioning

confidence: 99%

A Comparison Between Nosql and Rdbms: Storage and Retrieval

Fadhel¹,

Jameel²

2022

MINAR Journal

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Relational database management systems (RDBMS) emerged as the solution for data storage in the past decades. All data storage systems and applications utilize a RDBMS in the heart of the system to store and retrieve the data. In the past few years a new data storage model, named Not Only Structured Query Language (NOSQL), has emerged to produce less complex data storage systems and to tackle the data's massive volume performance degradation of used systems. In this work, a comparison study is conducted between MySQL as an example of RDBMS and Monogodb as an example of NOSQL systems using threading and machine resources to show the differences for developers to select one of these models for their applications. The results show that the performance of NoSQL is less than MySQL for small datasets and few database operations, such as few thousands of records and hundreds of operations per day. However, with the introduction of threads and volumes of data, the performance of Mongodb overcomes My Structured Query Language (MySQL). In addition, the results have shown that Mongodb requires more memory usage and CPU resources than MySQL to complete their tasks. Finally, the images were saved as byte data inside both platforms. The storing process for this data inside Mongodb was faster than the MySQL platform

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NoSQL Overview and Performance Testing of HBase Over Multiple Nodes with MySQL

Cited by 9 publications

References 14 publications

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

Complex SQL-NoSQL Query Translation for Data Lake Management

A Comparison Between Nosql and Rdbms: Storage and Retrieval

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