TY - GEN
T1 - SQL and NoSQL database comparison
T2 - Future of Information and Communication Conference, FICC 2018
AU - Chang, Ming Li Emily
AU - Chua, Hui Na
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - In this digital era, social media web applications have churned out huge amount of unstructured data each day. These social media data may be processed into meaningful data through text analytics. With the rapid growth of the volume of unstructured data produced daily, NoSQL database is increasingly popular that it has become the chosen database to store data. However, little research is done on the comparison of SQL and NoSQL in terms of indexing, performance tuning, and amount of records supported. This paper aims to provide a thorough comparative evaluation of MongoDB and MySQL, a tool for SQL and NoSQL databases, respectively, in terms of their performance in populating and retrieving big data after performance tuning. The findings presented in this paper give a new insight from the aspect of how these databases support semi-structured social media data by considering the options of performance tuning. The methodology for this research consists of four performance measurements, namely, insert, select, update, and delete up to 1 million Twitter data stored, to evaluate SQL and NoSQL databases. Our result findings indicate that MongoDB does perform faster for all the four operations. However, there are more performance tuning options provided by MySQL for more flexible performance optimization.
AB - In this digital era, social media web applications have churned out huge amount of unstructured data each day. These social media data may be processed into meaningful data through text analytics. With the rapid growth of the volume of unstructured data produced daily, NoSQL database is increasingly popular that it has become the chosen database to store data. However, little research is done on the comparison of SQL and NoSQL in terms of indexing, performance tuning, and amount of records supported. This paper aims to provide a thorough comparative evaluation of MongoDB and MySQL, a tool for SQL and NoSQL databases, respectively, in terms of their performance in populating and retrieving big data after performance tuning. The findings presented in this paper give a new insight from the aspect of how these databases support semi-structured social media data by considering the options of performance tuning. The methodology for this research consists of four performance measurements, namely, insert, select, update, and delete up to 1 million Twitter data stored, to evaluate SQL and NoSQL databases. Our result findings indicate that MongoDB does perform faster for all the four operations. However, there are more performance tuning options provided by MySQL for more flexible performance optimization.
KW - Big data
KW - Database management systems
KW - NoSQL
KW - Semi-structured data model
KW - Twitter data streaming
UR - https://www.scopus.com/pages/publications/85058542681
U2 - 10.1007/978-3-030-03402-3_20
DO - 10.1007/978-3-030-03402-3_20
M3 - Conference contribution
AN - SCOPUS:85058542681
SN - 9783030034016
T3 - Advances in Intelligent Systems and Computing
SP - 294
EP - 310
BT - Advances in Information and Communication Networks - Proceedings of the 2018 Future of Information and Communication Conference FICC, Vol. 1
A2 - Bhatia, Rahul
A2 - Arai, Kohei
A2 - Kapoor, Supriya
PB - Springer Verlag
Y2 - 5 April 2018 through 6 April 2018
ER -