International Journal of Informative & Futuristic Research ISSN:

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1 Volume 5 Issue 8 April 2018 International Journal of Informative & Futuristic Research ISSN: TRANSITION FROM TRADITIONAL DATABASES TO NOSQL DATABASES Paper ID IJIFR/V5/ E8/ 010 Page No Subject Area Key Words Big Data, RDBMS, NoSQL, Cloud Computing Computer Engineering 1 Subita Kumari Research Scholar, Department of Computer Sci. & Engineering, University Institute of Engineering & Technology, Maharishi dayanand University, Rohtak-Haryana 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 This work is published under Attribution-NonCommercial-ShareAlike 4.0 International License 9215

2 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 9216

3 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 9217

4 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 objectoriented 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, 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 9218

5 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. 9219

6 Data in Zetta Bytes (Trillions of Gigabytes) B. Big Data ISSN: 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 shows the trend of growth of data in last two decades. Trend of Growth of Big Data Un/Semi Structured Data Structured Data Year Figure 4.2.2: Trend of Growth of Big Data 9220

7 Variety Today, Data being generated is not of single category. It may be raw, structured, semi-structured (web pages, web log files, s, social media sites etc.) and even unstructured (audio files, video files etc.). Figure 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 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 9221

8 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 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. 9222

9 6. REFERENCES [1] Kumari, S., & Gupta, P. (2015). Document store. 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), [3] Kumari, S., & Gupta, P. (2018). Implementation of CouchDBViews. In Big Data Analytics (pp ). 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), [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 ). IEEE. [8] Gulia, P. & Hemlata (2017). Novel Algorithm for PPDM of Vertically Partitioned Data. International Journal of Applied Engineering Research, 12(12), [9] Hemlata, Gulia, P. (2018). DCI3 Model for Privacy Preserving in Big Data. In Big Data Analytics (pp ). Springer, Singapore. TO CITE THIS PAPER Kumari, S. :: Transition from Traditional Databases to International Journal of Informative & Futuristic Research (ISSN: ), Vol. (5) No. (8), April 2018, pp , Paper ID: IJIFR/V5/E8/010. Available online through