MySQL vs MongoDB

 A Database Management System (DBMS) is responsible for managing and retrieving all required information from well-organized fragments of data. MySQL and MongoDB are such databases and the most in-demand database services for web applications. Both allow you to extract data and make reports from a site or app, but they are designed differently.  MySQL is a legacy table-structured system, whereas MongoDB is a document-based system. In this article, we shall have an interesting battle of MySQL vs MongoDB, and see how both the DBMS differ.

 

 

MySQL vs  MongoDB: Introduction 

 

 

MySQL 

 

 

MySQL is a famous, free-to-use, and open-source Relational Database Management system (RDBMS) made by Oracle. As with other relational systems, MySQL stores data with the help of tables and rows executes referential integrity, and utilizes SQL i.e. structured query language for accessing the data. When users need to recover data from a MySQL database, they must make an SQL query that merges multiple tables together to make the view of the data they require. It makes optimum usage of SQL for querying and operating database systems.

 

 

Database schemas and data models must be defined early, and data must correspond to this schema to be stored in the database. This strict approach to storing data presents some degree of safety but trades this for flexibility. If a new type or format of data requires to be stored in the database, schema migration should occur, which can become complex and costly as the size of the database grows.

 

 

MongoDB

 

 

Similar to MySQL, MongoDB is also free to use and open source, regardless, its design principles vary from traditional relational systems. In general, it is styled as a non-relational system (NoSQL), MongoDB adopts an extremely different technique for storing data, conveying information as a series of JSON-like documents as opposed to the table and row structure of relational systems.

 

 

MongoDB documents include a series of key/value pairs of irregular types, including arrays and nested documents, however, the immediate difference is that the structure of the key/value pairs in a shared collection can vary from document to document. This more relaxed approach is feasible as documents are self-describing.

 

 

 

We have general information about MongoDB and MYSQL. Let’s kickstart the comparison using significant parameters.

 

Parameters  of  Comparison	MongoDB	MySQL
Brief Intro	A non-relational database system giving improved flexibility and horizontal scalability	A strong relational database system, with a common database environment for skilled IT experts
Year Released	2009	1995
Organization	MongoDB Inc.	Oracle
Performance	Follows a hierarchical data model and maintains data together, reducing the need for joins, optimized for write performance	Optimized for high-performance joins with numerous tables that are indexed, optimized for high performance across many tables
Managing Data	Large chunks of data are easy to manage	Difficult when large chunks of data are there
System Type	Non-relational or NoSQL system	Legacy system designed with SQL
Applications	Real-time analytics, content management systems, Legacy business sites, IoT, mobile apps, analytical sites, and much more	High-security sites, eCommerce sites, structured data with clear schema, social media sites, etc.
Data Representation	Shows data as JSON documents	Shows the data in tables and rows
Programming Languages Support	C, C++	C, C++, JavaScript
Supports	Inbuilt replication, sharding, and auto elections	Master slave and master replication
Schema Definition	No need to define the schema, simply drop documents	Must define tables, and columns before storing
Query Language	JavaScript as a query language	SQL as a query language
JOIN Support	Does not support JOIN operations	Supports JOIN operations
Suitable For	Projects where there is structured or unstructured data for growth	Projects where there is structured data and for a traditional RDBMS
Risks	There is no schema definition necessary so there is minimal risk of attack	Higher risk of SQL injection attack
Foreign Key	Doesn’t allow the use of foreign keys	Allows usage of foreign keys
Scalability	Is scaled horizontally and vertically	Only Scaled Vertically
Terminologies	Table, Row, Columns, Joins	Collection, Document, Field, Embedded Document
Community Support	Roughly. 213 repositories on GitHub	Around. 23 repositories on GitHub
Application Security	Uses a role-based access control (RBAC) for security	Has a privilege-based security model (PBSM)
User Friendliness	Attractive and Simple UI for developers	Managing Tables, schemas, normalization, etc is confusing at times
Architecture	Has Nexus architecture which comes with more flexibility	Contains Client-server architecture with more storage
Distributed Architecture	Yes	No
Transaction Model	Follows the BASE model with more accessibility	Follows the ACID model with more consistency
Developer Productivity	The development cycle is fast and is a developer’s delight	Development in MySQL is slow as it has strict table structures
Integration Support	Integrates well with many storage engines and uses JSON language MongoDB query language	Uses SQL for database management supports programming languages but is less flexible
Query Language	Uses MongoDB Query Language (MQL)	Uses SQL like any other RDBMS
Associated Indexes	In case, the index is not found, the database engine looks for documents collection	Here, when the index is not found, the database engine looks for the whole table for the rows
Flexibility in Schema Design	Dynamic schema and design can be changed	Once defined, the schema design cannot be modified
Atomic Transactions	Multi-document transactions	Atomic transactions

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