MongoDB vs SQL – What are the differences?

Data management has been a very crucial task from its beginning. Over the years we have made several improvements in managing data. We must be able to use and protect our information in a mannered way. There are primarily two ways to store our data, using DBMS and file system. A file system is software that manages and organizes the files in a storage medium whereas DBMS maintains a database to define, alter and manipulate data. 

DBMS i.e Database Management System came as a solution to solve our problems like redundancy, security, and inconsistency which existed while using the File System. DBMS is further classified into Relational and Non-Relational Databases. In this article, we’ll be comparing examples of our relational and non-relational databases i.e SQL and MongoDB.

Introduction

Let’s take a look at each of them one by one. 

SQL

SQL, also known as Structured Query Language, is the standard programming language used for storing and retrieving information from the database. It is an example of a relational database which means the data will be stored in a structured format. Relational databases store their data in the form of tables and describe relationships between the data. 

MongoDB

MongoDB is a non-relational open-source, cross-platform database. It is currently the most popular NoSQL database. A NoSQL database signifies that the data is stored in an unstructured manner. It is a document-oriented database in which the data is stored in BSON(binary JSON) format.

Differences

License

The SQL server is owned and developed by Microsoft using a commercial license. Whereas, MongoDB is an open-source licensed database that has two versions, a free community version, and a paid licensed version. 

Operating System

SQL servers only run on Windows whereas MongoDB runs on Linux, Windows, and macOS operating systems.

Data Storing

In SQL, the data are stored in the form of a table where each column denotes an attribute while each row denotes a particular record. These tables are present inside the databases. For example, a table containing student details might have details like name, date of birth, parent’s contact number, grades, marks, etc. Whereas, in MongoDB, data is stored as collections. These collections of data are stored as documents(BSON).

SQL generates relations between two or more tables using foreign and primary keys. Primary keys are those columns that contain unique values and can represent other corresponding columns. A primary key becomes a foreign key when it is used by other tables to map unique relationships between each other.

In MongoDB, we cannot define relations between the unstructured data of the collection. Collections contain data/documents in a key-value pair structure.

Architecture

The architecture of SQL works on the ACID property which stands for Atomicity, Consistency, Isolation, and Durability. These properties focus on the consistency and reliability of transactions performed in the database.

SQL databases safeguard the reliability of transactions whereas MongoDB works on the principle of the CAP Theorem. The CAP theorem focuses on Consistency, Availability, and Partition which ensures high data availability.

 

Scalability

SQL databases are scaled vertically by increasing the memory size, disk size, or computing power of the server. The costs of managing rapid growth in large databases will be highly costly due to high querying for data. This happens due to SQL’s vertical scaling nature. 

On the other hand, MongoDB supports horizontal scaling. Here instead of increasing the server configuration, a new server is added for scalability. This method is less expensive because this method uses large volumes of clusters of low-cost commodity hardware which together meet high query requirements.

Querying Complexity

Querying a database is an important factor for a database management system. MongoDB being a NoSQL database faces difficulty while working on complex queries whereas SQL is the best solution for complex querying.

Schema 

SQL databases have a static or predefined schema that cannot be altered. Whereas on the other hand, MongoDB has a dynamic schema which makes it more flexible.

Performance

Data operations on MongoDB are fast and easy because of their NoSQL nature. Data can be quickly stored, manipulated, and retrieved without any compromise on data integrity while SQL Is a bit slow and becomes costly while using large databases.

Conclusion

In conclusion, we can say that businesses that deal with structured and relational data are better suited for a SQL database. It supports high-performing complex querying. On the other hand, if we want to scale out massive volumes of traffic and data we can utilize MongoDB. MongoDB can provide tremendous performance and flexibility with the help of its non-relational architecture. 

Finally, it’s up to the user who wants to decide what type of database one is comfortable in and what kind of data one is dealing with to manage the database. I hope you were able to gain some insights into the differences between SQL and MongoDB with this article. You can learn more about SQL here and also learn how to implement MongoDB with NodeJS backend here. Apart from these, you always have official documentations to look into 🙂