## 2 Related Work  
Currently, there are many studies that have been conducted to compare relational and non-relational databases based on different metrics. There are a lot of databases, especially the NoSQL ones such as MongoDB, Elasticsearch, Redis, Neo4j, and Cassandra [5; 8; 9] for storing large volumes of data, and choosing the most suitable databases for an application can be difficult. In [9] the authors presented a performance analysis of Elasticsearch and CouchDB on image data sets using the LINUX platform. The analysis is based on the results carried out by operations on both document-oriented databases and shows that CouchDB is more efficient than Elasticsearch for insert, update, and delete operations but for the select operation, Elasticsearch performs much better than CouchDB.  
The authors propose in [1], a comparison and testing of Elasticsearch and MySQL databases, using the Spring Data framework. The comparison is completed by searching values in larger key-value datasets.  
Elasticsearch was initially developed as a system for full-text search in large volumes of unstructured data. At present, Elasticsearch is a full-fledged analytical system with various capabilities [10]. In [10], possible sources of Big Data and problems related to its processing are analyzed. A system based on the Elasticsearch engine and MapReduce model is proposed as the solution to the user verification problem.  
In [11] [PERSON] and al. compare the performance of Elasticsearch, OpenTSDB (based on HBase), and InfluxDB NoSQL databases, using the same set of machines and the same data. Using the LHCb Workload Management System (WMS), based on DIRAC [11] as a use case they set up a new monitoring system, and in parallel with the current MySQL system, they stored the same data into the databases under test. [PERSON] et al. presented in [12] a solution to effectively address the challenges of real-time analysis using a configurable Elasticsearch search engine.  
Inserting and querying JSONs for Big Data for databases such as Cassandra, Mongo, PostgreSQL, CouchDB, MariaDB, and Elasticsearch concluded with the recommendation for Cassandra and Elasticsearch usage for searching and analytics, as described in [13].  
The way that Cassandra, another NoSQL database open-source with a wide-column store based on BigTable and DynamoDB concepts, benefit from the combination of BigTable and DynamoDB systems and exhibits optimizations for write access is described in [14]. In [15], the authors perform an analysis implementation process of column-oriented data stores of the NoSQL databases: BigTable and Cassandra, with respect to various issues such as features, integrity, indexing, distributions, and design.  
As highlighted above, choosing an appropriate database for storing huge amounts of data is not trivial, as one must take into account different aspects such as manageability, scalability, and extensibility. Moreover, even if several comparison studies exist in the literature, we cannot identify studies that are involved in the comparison to the MySQL Document Store database.  
In this idea, this paper performs a comparative evaluation of two popular open-source DBMSs: MySQL Document Store and Elasticsearch as a non-relational DBMS.