Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

COMPARISONAL ANALYSIS OF MYSQL AND MONGODB RESPONSE TIME QUERY PERFORMANCE

View through CrossRef
Query is the ability to display and or request data that is used to access data in the database system by using certain commands. Where a data obtained from one or more tables in the database according to what the user needs. The data can also interact with other data or tables with the aim of making it easier for the user to use it. The query response time is one of the considerations for testing databases. This journal will discuss two types of databases, namely MySQL (SQL) and MongoDB (NoSQL). Due to the increasing demand for data, data processors are competing to provide the right database with the best performance for applications or data processing. Then this journal provides a comparison analysis of MySQL and MongoDB databases in response time performance. This study uses the Query Runtime comparison execution test method between these databases. By using basic commands or DML queries, namely CRUD (Create, Read, Update, Delete). Several stages will be carried out, namely the determination of the dataset, implementation and testing, and analysis of the test results. In this study, trials were conducted on the insert, select, update, and delete processes with the data being tested were 100, 1000, 10000. After experimenting with MongoDB (NoSQL) and MySQL(SQL) databases. The MongoDB database has a faster query response time in the insert and delete process, for the query response time update and delete the MySQL database has a faster response time. Then it can be obtained that in query response time, the MongoDB database is superior in the update and delete processes with a runtime difference of 0.005 seconds and 0.017 seconds. MySQL database is superior in insert and select process with a difference of 4.137 seconds and 0.006 seconds.
Title: COMPARISONAL ANALYSIS OF MYSQL AND MONGODB RESPONSE TIME QUERY PERFORMANCE
Description:
Query is the ability to display and or request data that is used to access data in the database system by using certain commands.
Where a data obtained from one or more tables in the database according to what the user needs.
The data can also interact with other data or tables with the aim of making it easier for the user to use it.
The query response time is one of the considerations for testing databases.
This journal will discuss two types of databases, namely MySQL (SQL) and MongoDB (NoSQL).
Due to the increasing demand for data, data processors are competing to provide the right database with the best performance for applications or data processing.
Then this journal provides a comparison analysis of MySQL and MongoDB databases in response time performance.
This study uses the Query Runtime comparison execution test method between these databases.
By using basic commands or DML queries, namely CRUD (Create, Read, Update, Delete).
Several stages will be carried out, namely the determination of the dataset, implementation and testing, and analysis of the test results.
In this study, trials were conducted on the insert, select, update, and delete processes with the data being tested were 100, 1000, 10000.
After experimenting with MongoDB (NoSQL) and MySQL(SQL) databases.
The MongoDB database has a faster query response time in the insert and delete process, for the query response time update and delete the MySQL database has a faster response time.
Then it can be obtained that in query response time, the MongoDB database is superior in the update and delete processes with a runtime difference of 0.
005 seconds and 0.
017 seconds.
MySQL database is superior in insert and select process with a difference of 4.
137 seconds and 0.
006 seconds.

Related Results

Named Entity Recognition in Statistical Dataset Search Queries
Named Entity Recognition in Statistical Dataset Search Queries
Search engines must understand user queries to provide relevant search results. Search engines can enhance their understanding of user intent by employing named entity recognition ...
RaPID-Query for Fast Identity by Descent Search and Genealogical Analysis
RaPID-Query for Fast Identity by Descent Search and Genealogical Analysis
AbstractThe size of genetic databases has grown large enough such that, genetic genealogical search, a process of inferring familial relatedness by identifying DNA matches, has bec...
AOBO: A Fast-Switching Online Binary Optimizer on AArch64
AOBO: A Fast-Switching Online Binary Optimizer on AArch64
As the complexity of real-world server applications continues to grow, performance optimizations for large-scale applications are becoming increasingly challenging. The success of ...
EPD Electronic Pathogen Detection v1
EPD Electronic Pathogen Detection v1
Electronic pathogen detection (EPD) is a non - invasive, rapid, affordable, point- of- care test, for Covid 19 resulting from infection with SARS-CoV-2 virus. EPD scanning techno...
Some new fuzzy query processing methods based on similarity measurement and fuzzy data clustering
Some new fuzzy query processing methods based on similarity measurement and fuzzy data clustering
In relational and object-oriented database systems there is always data that is naturally fuzzy or uncertain. However, to deal with complex data types with fuzzy nature, these syst...
Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction
Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction
<p>Pre-retrieval Query Performance Prediction (QPP) methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the se...
Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction
Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction
<p>Pre-retrieval Query Performance Prediction (QPP) methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the se...
Database management systems for artificial intelligence: Comparative analysis of postgre SQL and MongoDB
Database management systems for artificial intelligence: Comparative analysis of postgre SQL and MongoDB
The rapid evolution of artificial intelligence (AI) has amplified the need for efficient database management systems (DBMS) to handle the growing volume, variety, and velocity of d...

Back to Top