Comparison of SQL and NoSQL databases with different workloads: MongoDB vs MySQL evaluation

The relational database model is the main focus of the database for the previous time. It provides robust data storage and structure that supports transaction characteristics and data retrieval with structured query language- SQL. The emergence of web technologies and clustering technology in large servers called for the need to store unstructured data and move away from the pattern of tables and static fields and not topic to transaction conditions, especially in distributed systems. Instead, it used CAP theory. New data models are emergence including: Document Data model, Key-Value model, Column-Family model, and Graph model, And new programming languages that deal with these models. An asset inventory system is a model of systems that are not topic to a fixed structure. Each location in the organization has fairly different assets and assets in the same location have different specifications that are difficult to organize in the form of tables and columns. For this reason using of NOSQL CouchDB database system was adopted and the use of MAP-REDUCE technology to organize data display and Cloudant Query in Mango-view method to retrieve data from the inventory database.

Last decade records a huge amount of data transition going on Internet. Today's world is connected by Social Networking sites which have huge size of databases. Currently, there is no utility existing for transforming SQL databases into NoSQL databases. NoSQL is a new trend; there is no software or utilities that are designed for the desired conversion. Most of the utilities developed, were developed for converting one form of SQL database to another SQL database. To promote Hybrid Database architecture including a Relational Database and NoSQL Database, a Data Adapter system is proposed. It can deal with database transformations using a data synchronization mechanism. It focuses on enabling effective Data Analytics on Scaled-out Object Storage Systems. There is implementation of a Data Analytics Layer on an Object-based Storage Cluster to perform in-place Map-Reduce computation on existing data. It keeps the strengths of RDBMS as ACID properties and at the same time by providing the benefits of NoSQL through a middle layer which keeps track of all running transactions and manages with other layers with concurrent transactions. The Data Adapter uses a SQL layer accepting queries from application services, so that original application remains intact. The Data Adapter also controls query flow during database transformations. An approach that performs computation on existing large-scale data in an Object Storage system without moving data anywhere and outcomes of this approach are analyzed. This outline can deliver strict consistency without affecting scalability and availability of NoSQL Databases.

NoSQL and relational databases have been integrated in response to the increasing demand for scalable and efficient data management in hybrid cloud environments. The differences in data structures and query processing methods between these databases present both challenges and opportunities when designing an optimized hybrid system. This study explores the integration of NoSQL and relational databases to maximize data storage, retrieval, and processing efficiency across multiple cloud systems. NoSQL databases excel in handling unstructured and semi-structured data, offering flexibility and scalability, whereas relational databases provide robust consistency and structured query capabilities. Despite the wide availability of relational databases, certain applications require the dynamic adaptability of NoSQL systems, making integration a viable solution. The research evaluates key performance parameters, including query execution speed, scalability, data consistency, and resource utilization. Cloudsim is used for simulation, allowing for an in-depth comparison between standalone and hybrid database models. Experimental results indicate that the proposed hybrid model improves query performance by 30%, reduces latency by 20%, and enhances scalability by 40% compared to using relational or NoSQL databases alone. The novelty of this approach lies in its ability to overcome the limitations of each database type by their strengths in a dynamic cloud environment. The results highlight the effectiveness of hybrid database integration in optimizing cloud-based data management while ensuring seamless operation, adaptability, and resource efficiency.

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.

In recent times, practicality of web applications has become more reliant upon big-data orientated materials such 4K videos, hi-def. resolution images, lossless audios and massive texts. Structured Query Languages (SQL) faces compatibility issues with large scale databases. Because of this data storage problem, NoSQL databases are used for storing big-data. NoSQL databases have been recently gaining traction with many options such MongoDB, CouchDB, Redis and Apache Cassandra. One of the major restrictions companies, enterprises and developers encounter during developing an application is multiplicative cost of building a native programing across different platforms. Besides, network Function Virtualization (NFV) plays a vital role for providing services for utilizing such applications in larger and more effective scale. Hence, in this paper, we discussed our main motivation behind selecting Iconic Framework, a hybrid system for rapid development real-time application based on Firebase in the NFV environment cooperating with Mobile Edge Computing (MEC). As a result, this approach provides comparatively flexible features.

With the current emphasis on Big Data, NOSQL databases have surged in popularity. These databases are claimed to perform better than SQL databases. The traditional database is designed for the structured data and the complex query. In the environment of the cloud, the scale of data is very large, the data is non-structured, the request of the data is dynamic, these characteristics raise new challenges for the data storage and administration, in this context, the NOSQL database comes into picture. This paper discusses about some non-structured databases. It also shows how Cassandra is used to improve the scalability of the network compared to RDBMS.

Data aggregation is one of the key features used in databases, especially for Business Intelligence (e.g., ETL, OLAP) and analytics/data mining. When considering SQL databases, aggregation is used to prepare and visualize data for deeper analyses. However, these operations are often impossible on very large volumes of data regarding memory-and-time-consumption. In this paper, we show how NoSQL databases such as MongoDB and its key-value stores, thanks to the native MapReduce algorithm, can provide an efficient framework to aggregate large volumes of data. We provide basic material about the MapReduce algorithm, the different NoSQL databases (read intensive vs. write intensive). We investigate how to efficiently modelize the data framework for BI and analytics. For this purpose, we focus on read intensive NoSQL databases using MongoDB and we show how NoSQL and MapReduce can help handling large volumes of data.

Chapter 2 - Querying

This paper outlines the main features of Biblisa, a tool that offers various possibilities of enhancing queries submitted to large collections of aligned parallel text residing in bilingual digital library. Biblisa supports keyword queries as an intuitive way of specifying information needs. The keyword queries initiated, in Serbian or English, can be expanded, both semantically, morphologically and in other language, using different supporting monolingual and bilingual resources. Terminological and lexical resources are of various types, such as wordnets, electronic dictionaries, SQL and NoSQL databases, which are distributed in different servers accessed in various ways. The web application has been tested on a collection of texts from 3 journals and 2 projects, comprising 299 documents generated from TMX, stored in a NoSQL database. The tool allows the full-text and metadata search, with extraction of concordance sentence pairs for translation and terminology work support.

Databases play an important role in Information and Communication Technologies and data has no significance unless it is converted into meaningful information. Relational or SQL disk databases where data is structured in tables have served many applications over the years. The increasing need for faster data access in real-time systems has changed the traditional Disk-based database into an In-memory database where data is manipulated and stored directly in the main memory thereby reducing disk input/output operations. With the emergence of IoT and Big Data, NoSQL databases now represent an alternative to traditional relational databases in terms of simpler design and faster operations on large data volumes. This work compares three types of databases namely Relational On-disk, Relational In-memory and NoSQL On-disk/In-memory in terms of Insert, Select, Update and Delete operations together with Indexes, Stored procedures and Triggers in an IoT environment. A plant environment has been monitored in real-time using sensor data such as Temperature/Humidy, Moisture and Water level. Data was stored in different databases and the time taken to execute various operations were recorded. Experiments using the water level parameter demonstrate there is no specific database that provides the best performance for all data operations.

The need for practical, scalable and efficient data analysis methods is crucial in today's fast-changing data environment.This paper explores the synergy between MongoDB, a NoSQL database is recognized for its flexibility and scalability.SQL, which is the standardized language for relational database querying and management and vector databases, which are essential for AI-driven applications.By leveraging the strengths of these technologies organizations can unlock powerful insights, optimize performance and address diverse analytical needs.This paper outlines best practices, industry use cases and innovative strategies for combining MongoDB, SQL and vector databases to advance data analysis techniques.

ABSTRACTNowadays the dominant databases for work with structured data are based on a relational model (also called SQL databases), while NoSQL databases have been the increasingly popular solution for unstructured data. An important requirement which contemporary enterprise information systems should address is the use of data, regardless of the level of their structuredness. That has led to the need for design a unique logical database, which includes SQL and NoSQL components, and takes advantage of both technologies, so-called a hybrid SQL/NoSQL database. In this paper, the justification of introducing a hybrid database, depending on the criteria of data structuredness in use, was analysed and a new approach for design hybrid SQL/NoSQL database was presented, with its necessary activities. Experimental results confirmed, in certain conditions, the better performances of the hybrid database compared to SQL database and opened the space for further research.

A proposal of architecture for integration and uniform use of hybrid SQL/NoSQL database components

Relational database is widely used in most of the application to store and retrieve data. They work best when they handle a limited set of data. Handling real time huge volume of data like internet was inefficient in relation database systems. To overcome this problem the NO-SQL or Not Only SQL Database came into existence. This paper discusses about problems with relation databases and how different types of NOSQL Databases are used to efficiently handle the real world problems.

Nowadays, there are many options for corpus linguistic analysis that make use of different approaches for corpus storage. There are tools based on SQL databases, dedicated implementations such as CQP/CWB and others that employ plain-text corpora. NoSQL databases have been widely used for big data, data mining and even sentiment analysis. However, as far as we can see, there is a lack of a widespread concordancer or consolidated framework that makes use of MongoDB architecture for the purposes of corpus linguistics. This paper aims to describe the architecture of a software that allows users to analyse monolingual and bilingual parallel corpora with grammatical annotation using MongoDB technology. Our premises are that MongoDB is ideal for non-structured data and provides high flexibility and scalability, so it may be also useful for corpus linguistic research. We analyse functionalities of MongoDB such as text search indexes and query format in order to examine its suitability.