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International Journal of Informative & Futuristic Research ISSN: 2347-1697

Volume 5 Issue 8 April 2018 www.ijifr.com

Abstract

Relational databases such as SQL Server, Oracle and MySQL have almost

forty five years of experience in real time production environment. For last

few decades, these databases have been successfully used by large banks

and other institutions/organizations throughout the world for transaction

processing, handling structured data, build and manage intelligent and

mission-critical applications. But today, there is remarkable growth in

heterogeneous and unstructured data due to availability, speed of internet

and connectivity of devices through IOT. So companies are progressively

considering alternatives to relational infrastructure to deal with Big Data.

NoSQL databases have coined themselves as alternative solutions. This

paper explains the need of transition from traditional databases to NoSQL

databases.

1. INTRODUCTION

Relational databases management systems follow relational data model. Database is

composed of relations or tables. Table is collection of rows and columns. Each row

represents a record and each column represents a field. Tables are linked with each other

based on some defined relationships such as foreign key. These relationships enable user

to retrieve and join data from one or several tables using a single query. Abstractly, tables

and relationship between tables represent some real time entities which are used in

designing the database schema. Relational databases such as SQL Server, Oracle and

MySQL have almost forty five years of experience in real time production environment.

For last few decades, these databases have been successfully used by large banks and

TRANSITION FROM TRADITIONAL

DATABASES TO NOSQL DATABASES Paper ID IJIFR/V5/ E8/ 010 Page No. 9215-9223 Subject Area

Computer

Engineering

Key Words Big Data, RDBMS, NoSQL, Cloud Computing

1 Subita Kumari

Research Scholar, Department of Computer Sci. & Engineering, University Institute of Engineering & Technology, Maharishi dayanand University, Rohtak-Haryana

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR)

Continuous 56th Edition, Volume - 5, Issue -8, April 2018 Page No. : 9215-9223

Subita Kumari :: Transition from Traditional Databases to NoSQL Databases

other institutions/organizations throughout the world for transaction processing, handling

structured data, build and manage intelligent and mission-critical applications [1]. Also,

there is easy availability of skilled and experienced programmers who can work on

relational systems. That's why most organizations are not going to transfer their

transactional systems from relational databases to NoSQL databases. But today, there is

remarkable growth in heterogeneous and unstructured data due to availability, speed of

internet and connectivity of devices through IOT. Under the explosive increase of global

data, the term of big data is mainly used to describe enormous datasets generated by

widely distributed data sources which require newer technologies and architectures to

store, process and manage these datasets. So companies are progressively considering

alternatives to relational infrastructure to deal with Big Data. These NoSQL databases

have coined themselves as alternative solutions [3].

2. RELATIONAL DATABASE MANAGEMENT SYSTEM

Relational databases management systems follow relational data model. Database is

composed of relations or tables. Table is collection of rows and columns. Each row

represents a record and each column represents a field. Tables are linked with each other

based on some defined relationships such as foreign key. These relationships enable user

to retrieve and join data from one or several tables using a single query. Abstractly, tables

and relationship between tables represent some real time entities which are used in

designing the database schema.

A. ACID properties of Relational Databases

A transaction is a set of logically related operations performed on database to perform unit

of work. The four main features of a relational database transaction that guarantee its

integrity is referred to as ACID (Atomicity, Consistency, Isolation, and Durability)

properties. Conventional RDBMS applications have focused on ACID transactions.

Atomicity

Atomicity refers to the execution of all operations of the transaction or none of

them. Recovery management component of RDBMS ensures atomicity.

Consistency

Consistency means database should remain in some consistent state before and

after execution of transaction. Concurrency control mechanism of RDBMS ensures

consistency.

Isolation

If two or more transactions are executing concurrently then isolation guarantees

that a running transaction is isolated from another transaction performing similar

task. In other words, transactions operating on the same data do not interfere with

each other. Concurrency control mechanism of RDBMS ensures isolation.

Durability

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Transactions should be able to recover under any case of failure. Recovery

management component of RDBMS ensures durability. This is very important

property in cases of failure of the systems.

B. Advantages of Relational Databases

After For last few decades, relational database management systems have presented fairly

robust information management tools to software developers and businesses. The

following are some of the advantages of relational database model:

Data Structure

The data of relational database is in tabular form, which is easy for users to

comprehend and use. The database structured queries can search for matching

entries in columns of the tables efficiently.

Data Independence

Various users of database can access data without physical details. Various levels of

database shown in figure 1 follow abstraction and lower level layer hide details

from lower level layer. This is called data independence.

Indexing

RDBMS allow various kinds of indexes to reduce I/O cost and to increase speed of

data access.

Multiple User Access

RDBMS allows multiple users to concurrently access the database. This is made

possible through concurrency control mechanism of RDBMS. It prevents users

operations from accessing partly updated records.

Authentications and Privileges

RDBMS provide authentication feature that allows database administrator to limit

database access to only authorized users. Also, RDBMS provide privilege control

feature that allows administrator to grant access on the basis of the task the user

needs to perform.

Language

RDBMS have build in maintenance tools that allow database administrators to

easily test, repair and maintain database.

Network Access

In RDBMS users can access and use the database without logging into the physical

computer system. RDBMS use server daemon programs that listen for requests on a

network and connect clients to the database.

Relational database management systems such as Microsoft SQL Server, Oracle, MySQL,

and Sybase are the key database management systems which have been widely used for

last few decades by individuals and organizations for managing structured data. However,

horizontal scaling is a big challenge in the contemporary era of web technologies.

Recently, with an increase in web application and diversity in data, there is need to

explore non-relational options which can provide a schema-less data structure, horizontal

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scaling, high availability and simple replication. These newly explored options are called

NoSQL databases.

3. NOSQL DATABASES

NoSQL is the term used to express data stores that do not follow the relational model and

do not use SQL (Structured Query Language) as the data query language. NoSQL is a

class of databases which allows better application development through the use of flexible

schema. These databases scale horizontally and dynamically to support a large number of

users and a big amount of data. These databases allow complex and distributed processing

of data, so they provide improved performance for highly responsive applications. [2]

They are categorized into various classes based on how they store data.

Key-Value (KV) Store

Key-value databases are based on the concept of the distributed hash table and Amazon's

Dynamo [4]. Amazon uses its Dynamo key-value store for its shopping carts. They store

data as values and pair each value with an alpha-numeric identifier (key) in simple

standalone tables called as hash tables [5]. Examples of various Key-Value databases are -

Dynamo, Tokyo Cabinet, Redis, Riak, Voldemort, and MemcacheDB.

Column-Oriented Databases

Column-Oriented data stores utilize a column-oriented data structure that accommodates

multiple attributes per key [5]. They are also more scalable because the user can add new

columns in the database in future. There is no need to supply values for already existing

rows for the new columns. Some examples of the various Column-Oriented database are -

Hypertable, HBase, and Cassandra.

Graph Store

Graph databases handle highly interconnected data called nodes. These are useful when

relationships between data sets are more important than the data itself. They replace

relational tables with structured relational graphs of interconnected key-value pairs. They

are almost similar to object-oriented databases as the graphs are represented as an object-

oriented network of nodes, edges, and properties [5]. Examples of the various Graph Store

database are - Neo4J, InfiniteGraph, Sones GraphDB, InfoGrid, AllegroGraph, and

FlockDB.

Document Oriented Databases

Document-oriented databases store data in the form of object like documents. They are

good for storing and managing big data-size collections of documents like text documents,

email messages, product or customer details [5]. They use JSON (Java Script Object

Notation), BSON (Binary Serialized dOcument Notation) or XML (Extensible Markup

Language) as data exchange formats. MongoDB and CouchDB are famous open source

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document-oriented databases. SimpleDB is a proprietary document-oriented database of

Amazon.

4.1 The Benefits of NoSQL

NoSQL databases are more scalable than relational databases. They carry out agile development

and quick iteration. They work on large volumes of structured, semi-structured and unstructured

data. They use object-oriented programming that is easy to use and flexible. They are efficient and

scale-out architecture instead of expensive monolithic relational architecture. Some of the benefits

of NoSQL databases are explained as below-

Dynamic Schema

Relational databases need defined schema or structure before adding data to the

database. NoSQL databases are designed to permit the adding of data without a

predefined schema.

Auto-sharding

Relational databases scale vertically means a single server has to take care of the

entire database to ensure availability and consistency of data. This single server

becomes expensive and places restrictions on scalability. The solution to this

problem is to scale horizontally means adding more servers instead of adding

additional capability in a single server. NoSQL databases support auto-sharding

mechanism means it automatically spread data across an arbitrary number of

servers.

Replication

Most NoSQL databases support automatic replication, means they are highly

available databases and manage to recover from disasters without involving

separate applications.

Integrated Caching

Many NoSQL databases have excellent integrated caching capabilities, keeping

frequently used data in system memory as much as possible without needing

separate caching layer.

4.2 What's causing transition from traditional databases to NoSQL databases?

There is no single motive or technology that is causing the move to NoSQL technologies.

There are four interrelated megatrends that are driving the embracing of NoSQL

technologies. These are Big Users, Big Data, Cloud Computing and Internet of Things.

A. Big Users

The easy availability, low cost and high speed of internet, throughout the world, has

created big users. Today, Almost 5+ billions global online population using computers,

laptops, and smartphones spend 40+ billion hours online daily. A recently launched app

can go viral, growing from zero to a million users overnight. The numbers of users and

hours spent swing on festivals like Christmas or Diwali. So the technologies dealing with

varying population have to be scalable and flexible.

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B. Big Data

Big Data is defined as the huge amount of heterogeneous type of data that is being

generated with high speed and analysis of this data requires new technologies and

architectures. Figure 4.2.1shows various dimensions of big data.

Figure 4.2.1: Various Dimensions of Big Data

Volume

The big word in Big Data itself defines the volume. The volume of data has grown

from gigabytes to zettabytes. Figure 4.2.2 shows the trend of growth of data in last

two decades.

Figure 4.2.2: Trend of Growth of Big Data

Da

ta i

n Z

ett

a B

yte

s

(Tri

llio

ns

of

Gig

ab

yte

s)

Year

Trend of Growth of Big Data

Un/Semi Structured Data

Structured Data

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Variety

Today, Data being generated is not of single category. It may be raw, structured,

semi-structured (web pages, web log files, e-mails, social media sites etc.) and

even unstructured (audio files, video files etc.). Figure 4.2.2 shows that growth of

structured data is linear but the growth of unstructured data is highly exponential

over a period of last 15 years.

Velocity

Velocity in context of big data means the speed of the data coming from various

sources. [6] It also means that data collection and analysis must be rapidly and

timely conducted so as to maximize the commercial value of big data.

Variability

Variability considers uneven data flow. [7] In his era of internet, data loads

become challenging to maintain during peak hours of specific events.

4.3 Cloud Computing

Cloud computing is defined as the delivery of computing services such as storage, servers,

software, databases, networking and analytics over the Internet. Applications today are

cloud-based and developed using a three-tier internet architecture as shown in figure 4.3.1.

They need to support the combined needs of millions of customers. Also, there have been

tons of changes in database management system since the instigation of cloud computing.

The need for scalable databases has been increased and these needs are being satisfied by

the NoSQL databases with their high availability, scalability and easily programmable

models [8].

Figure 4.3.1: Conventional 2-tier versus New 3-tier Cloud-Based architecture

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4.4 Internet of Things (IoT)

The Internet of Things is a world where all physical smart devices and things are

connected to each other through the internet and share information as shown in figure

4.4.1. All these devices come with a variety of new sensors. New sensors create new data

and there arise the need for new functionality. Relational databases make it hard to

incorporate new data. 40 billion sensors generate huge volumes of data. Relational

databases were not designed for handling that volume of data. In IoT one need to analyze

rapidly changing and multi-structured data in real time. Lengthy ETL (Extract, Transform,

Load) processes of relational databases to cleanse data for reporting won't work [9].

Figure 4.4.1: Internet of Things

5. CONCLUSION

Under the explosive increase of global data, the term of big data is mainly used to describe

enormous datasets generated by widely distributed data sources which require newer technologies

and architectures to store, process and manage these datasets. So companies are progressively

considering alternatives to relational infrastructure to deal with Big Data. NoSQL databases have

coined themselves as alternative solutions. There are following motivations to consider

alternatives - First is technical, because there is need to scale or perform ahead of the capabilities

of the existing systems. Secondly, there is desire to identify possible alternatives to expensive

proprietary software. A third motivation is agility or speed of development as today market

embrace agile development methodologies more quickly. So, above reasons suggest the need of

transition from traditional relational databases to NoSQL databases as they can ship new

functionality without redesigning the existing database and they can also scale out as the sensors

data grow.

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6. REFERENCES

[1] Kumari, S., & Gupta, P. (2015). Document store NoSQL Databases. International Journal of

Artificial Intelligence and Knowledge Discovery 5(3).

[2] Kumari, S., & Gupta, P. (2017). Proposed Architecture of MongoDB-Hive

Integration. International Journal of Applied Engineering Research, 12(15), 5000-5004.

[3] Kumari, S., & Gupta, P. (2018). Implementation of CouchDBViews. In Big Data

Analytics (pp. 241-251). Springer, Singapore.

[4] Burtica, R., Mocanu, E. M., Andreica, M. I., & Ţăpuş, N. (2012, March). Practical application

and evaluation of no-SQL databases in Cloud Computing. In Systems Conference (SysCon),

2012 IEEE International (pp. 1-6). IEEE.

[5] Moniruzzaman, A. B. M., & Hossain, S. A. (2013). Nosql database: New era of databases for

big data analytics-classification, characteristics and comparison. International Journal of

Database Theory and Application Vol. 6, No. 4.

[6] Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and

Applications, 19(2), 171-209.

[7] Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: issues, challenges, tools and

good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference

on (pp. 404-409). IEEE.

[8] Gulia, P. & Hemlata (2017). Novel Algorithm for PPDM of Vertically Partitioned

Data. International Journal of Applied Engineering Research, 12(12), 3090-3096

[9] Hemlata, Gulia, P. (2018). DCI3 Model for Privacy Preserving in Big Data. In Big Data

Analytics (pp. 351-362). Springer, Singapore.

TO CITE THIS PAPER

Kumari, S. :: “Transition from Traditional Databases to NoSQL Databases”

International Journal of Informative & Futuristic Research (ISSN: 2347-1697), Vol. (5)

No. (8), April 2018, pp. 9215-9223, Paper ID: IJIFR/V5/E8/010. Available online

through- http://www.ijifr.com/searchjournal.aspx

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