# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 2 Related Work  
Many studies have been conducted in recent years to compare the efficiency of different optimization techniques on both relational and non-relational databases [14; 15]. In [16], the authors presented a comparative study between relational and non-relational database models in a web-based application, by executing various operations on both relational and non-relational databases, thus highlighting the results obtained during performance comparison tests. The authors of [17] conducted an exhaustive analysis and comparison with regard to MySQL and CouchDB query performances as well as the configuration and structure of the data; for this purpose, specific similar applications were developed in Java in order to compare the time performance when working with large amounts of data.  
In [18], the authors presented a study that shows that multi-query optimization for shared data using predicate-based filters can open new opportunities to gain significant improvements by reducing redundant filtering tasks. [PERSON] et al. [3] described an approach to perform join operations at the application layer in the MongoDB database, which allows preserving data models.  
As NoSQL databases are growing in popularity, the integration of different NoSQL systems and the interoperability of NoSQL systems with SQL databases are becoming an increasingly important issue. In [19], the authors proposed a novel data integration methodology to query data individually from different relational and non-relational database systems. To show the applicability of the proposed methodology, a web-based application was developed, which confirms the usefulness of the method. The authors of [20] conducted a comparative analysis between NoSQL databases such as HBase, MongoDB, BigTable, and SimpleDB and relational databases such as MySQL, highlighting their limits. The authors specifically tested the above databases analyzing both simple and more complex queries.  
In [21], the authors presented a protocol to assess the computational complexity of querying relational and non-relational databases in a healthcare application. This protocol shows that SQL systems are not practical for single-patient queries since response times are slower. The query optimization problem was addressed in [22], where the authors addressed the issue of the efficient execution of JOIN queries in the Hadoop query language, Hive, over limited big data storages.  
Query optimization in relational and non-relational databases has become a promising research direction due to the increasing amount of data to be processed and queried as fast as possible. With this idea, the methods for optimizing the database structure and queries proposed in this paper address a particularly challenging and actual problem, being applied on two of the most popular open-source database management systems: MySQL as a relational DBMS, and document-based MySQL as a non-relational DBMS. The results obtained confirm the efficiency and impact that these optimization methods have on each specific DBMS when carrying out CRUD operations for different amounts of data.  
We chose to focus on these two databases because relational MySQL is the most used type of database at present, and document-based MySQL comes as an alternative to other non-relational databases, with MySQL thus trying to provide solutions to all application developers. Being a newer database about which there is not much documentation, by conducting this study, we can highlight important details about how to use it, integrate it into an application, and optimize it and its performance in certain operations.