An Analysis of the Performance and Configuration Features of MySQL Document Store and Elasticsearch as an Alternative Backend in a Data Replication Solution

In recent years, with the increase in the volume and complexity of data, choosing a suitable database for storing huge amounts of data is not easy, because it must consider aspects such as manageability, scalability, and extensibility. Nowadays, the NoSQL databases have gained immense popularity for their efficiency in managing such datasets compared to relational databases. However, relational databases also exhibit some advantages in certain circumstances, therefore many applications use a combined approach: relational and non-relational. This paper performs a comparative evaluation of two popular open-source DBMSs: MySQL Document Store and Elasticsearch as non-relational DBMSs; this comparison is based on a detailed analysis of CRUD operations for different amounts of data showing how the databases could be modeled and used in an application. A case-study application was developed for this purpose in Java programming language and Spring framework using for data storage both relational MySQL and non-relational Elasticsearch and MySQL Document Store. To model the real situation encountered in several developed applications that use both relational and non-relational databases, a data replication solution that imports data from the primary relational MySQL database into Elasticsearch and MySQL Document Store as possible alternatives for more efficient data search was proposed and implemented.

Promising directions for the development of modern databases

In this article, we will compare the SQL and NoSQL models, their advantages and disadvantages, clarify the variations of using both approaches to database management and explain in which cases it is better to use one or the other model. Three main characteristics of the system were tested: parallel data processing, working with information stores, and data replication. The possibility of parallel data processing was evaluated by analyzing the mechanisms of blocking, multi-level parallel access control and ACID. Storage testing covered both physical media and RAM usage. Replication was tested in synchronous and asynchronous modes. During the tests, we found out that SQL databases with the possibility of clustering showed promising performance results per node and also have scalability.

Comparative Analysis of Different Data Replication Strategies in Cloud Environment

In this paper, we performed a comparative study of the different data replication strategies such as Adaptive Data Replication Strategy (ADRS), Dynamic Cost Aware Re-Replication and Rebalancing Strategy (DCR2S) and Efficient Placement Algorithm (EPA) in the cloud environment. The implementation of these three techniques is done in JAVA and the performance analysis is conducted to study the performance of those replication techniques by various parameters. The parameters used for the performance analysis of these three techniques are Load Variance, Response Time, Probability of File Availability, System Byte Effective Rate (SBER), Latency, and Fault Ratio. From the analysis, it is evaluated that by varying the number of file replicas, it shows deviations in the outcomes of these parameters. The comparative results were also analyzed.

Ontological Data Replication in a Distributed Real-Time Database System

The massive use of ontologies generates a large amount of semantic data. To facilitate their management, persistent solutions for storing and querying these semantic data loads have been proposed. This gave rise to a new type of databases, called ontology-based databases (OBDB). In recent years, the need for data and real-time services has increased significantly in a large number of applications. However, the OBDB does not implement any mechanism to address real-time applications which are characterized, not only by handling large amounts of data, but also by temporal constraints, to which can be submitted data and treatments. As well, geographically extended applications, requiring using real-time databases that manage data and distributed processing are increasingly needed.These applications are managed by Distributed Real-Time DataBase Management System (DRTDBMS). Like any system, the DRTDBMS, often go through overload phases, due to the unpredictable arrival of transactions submitted by users. In order to better manage Quality of Service (QoS) in these systems by facing instability periods, approaches based on Distributed Feedback Control Scheduling (DFCS) were proposed. These approaches does not address the use of ontological data. In this paper, we propose an approach aiming to enhance QoS in DRTDBMS based on data replication. It consists in extending the DFCS architecture by the manipulation of ontological data as well as handling the execution of accessing transactions. In the extension we propose, we study the applicability of different data replication policies. The proposed architecture is then called Replication-Based-Distributed Feedback Control Scheduling Architecture for Real-Time Ontology (Replication-Based-DFCS-RTO). We also show the contribution provided by our approach through simulation results.