MongoDB vs MySQl

 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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