SQL vs. MongoDB: What’s the Difference?

SQL and MongoDB may seem very similar and they do serve similar purposes. But they’re actually very different technologies under the hood. If you have questions on your mind like:

• What’s the difference between MongoDB and SQL databases? 
• When do I use SQL databases or MongoDB? 
• Will MongoDB support or MySQL support be better?

…you’ve come to the right place. In this article, we will compare SQL vs. MongoDB, a NoSQL database. We’ll take a look at which provides the most flexible data management, how SQL queries run, and how a dynamic schema can help you maintain and interact with your databases.

Introduction to terminologies

Before we get into a comparison, let’s discuss some of the terminologies used in this article. 

• Database: A database is a structured set of information. The computer program can access, manage & update this organized data.
• Database Management System (DBMS): It’s a program, which can interact with databases. The DBMS stores, updates & extracts data to/from databases. In other words, a DBMS is a program or system software for creating and managing databases.
• Relational Database Management System (RDBMS): A relational database follows a relational database model. Mr. E. F. Codd invented the relational database model. The RDBMS stores data in the form of related tables. Examples of RDBMS are MySQL, Oracle, SQL Server, PostgreSQL, etc.
• Structured Query Language (SQL): A language to communicate with the database management system. SQL is an American National Standards Institute (ANSI) standard. If you want to learn more about SQL, read the “What is SQL” article on Udemy.
• Database Schema: This refers to the way that data is organized within the database; the database’s schema design.
• MySQL: MySQL is a popular, free-to-use, and open-source relational database management system. It’s developed & managed by Oracle Corporation.
• NoSQL: NoSQL databases are also known as non-relational databases. These are non-tabular databases that store data differently compared to RDBMS.
• MongoDB: MongoDB is a free-to-use, open-source, most popular NoSQL database.

Now, you are familiar with the terminologies. We will compare MongoDB (NoSQL database) with MySQL, a well-known SQL database. You could also take a SQL/MySQL course to find out more.

The difference between MongoDB and SQL database (MySQL)

What’s the difference between MongoDB and MySQL? They’re entirely different database solutions. Let’s look at some of the major differences.

Development history

The MySQL database was developed in 1995. It’s an open-source relational database management system and stores data in tables within fixed rows and columns. Until the 2000s, SQL RDBMS was the only option to store data. It runs on the SQL language and data schema, which provides relational data models and data storage.

But the Internet & web 2.0 boom started to generate large amounts of unstructured data. This unstructured data could no longer be reliably analyzed or maintained through Structured Query Language SQL.

Unstructured data includes JSON documents, key-value pairs, text, graphs, nodes, and wide columns. Storing unstructured data in the table-like schema was challenging, especially when developers demanded high performance and consistent database support.

That’s when NoSQL databases came into the picture. The 10gen software organization developed MongoDB in 2009. In 2013, the company became MongoDB Inc. Since then, MongoDB has been the most popular NoSQL database.

Schemas

In SQL, the data must follow predefined schemas. For example, while creating a table, we must define data columns and data types. While storing the data, it should match the table structure. But, in MongoDB, we don’t need to define any schema. A collection can store any document type, which acts as a sort of dynamic schema rather than a rigid one. This has been frequently used in applications such as machine learning and AI.

Data storage

The SQL databases store data in tables. The table columns are data attributes and rows contain actual data records. In MongoDB, collections store the data. A collection stores JSON-like key-value pairs.

An example of a MongoDB document:

{
fullname: "John Doe",
gender: "Male",
age: 35,
contact: {
   phone: "1234567890",
   address: "Flat no. 123, MH"
   }
}

An example of a SQL database record/row:

fullname	gender	age	phone	address
John Doe	male	35	1234567890	Flat no. 123, MH

The SQL databases create relational tables using primary and foreign keys. For example, a “UserId” column is a primary key in the Users table. And in the Orders table, it would represent a foreign key. It connects the two tables with column references. This process creates a relationship between the two tables. This is why an SQL database like MySQL is known as a relational database. While, in the case of MongoDB, we can’t create such relationships. Hence, it’s called a non-relational database.

Almost every SQL database emphasizes ACID (Atomicity, Consistency, Isolation & Durability) property. While MongoDB focuses on the CAP theorem (Consistency, Availability, and Partition tolerance). The ACID property focuses on the reliability of transactions in the database. And CAP theorem ensures the high availability of data.

Reliability and availability

Reliability and availability are most important to understand if a database system is robust. Almost every DBMS works on standalone servers. To manage the risk of failures, SQL databases use distributed database architecture. The databases running on clusters of nodes increase resilience. In case of one node failure, the database will work on other nodes.

MongoDB runs on a cluster of commodity hardware. And for high reliability and availability, it replicates the data across the nodes. Hence, MongoDB fail-over is less complex and quicker.

Performance

As the number of queries grows, SQL takes more time to execute. But MongoDB performance is better in such scenarios. As NoSQL doesn’t use joins and indexing, it performs better compared to SQL databases.

Query language

Most popular relational databases use structured query language (SQL) to interact with databases. While MongoDB uses MongoDB Query Language (MQL).

Scaling

Most SQL databases need to scale up vertically. The vertical scaling means migrating to a larger server with more memory, disk, and computing.

MongoDB or NoSQL databases support horizontal scaling. It means we can add cheaper commodity servers. This process is known as Sharding.

MongoDB and SQL Database (MySQL) Comparison Table

	SQL Database (MySQL)	MongoDB Database
History	Developed in 1995	Developed in the late 2000s
Data Storage Model	Data stored in tables with rows & columns	Data stored in collections with key-value pair documents
Example Databases	Oracle, SQL Server, PostgreSQL, etc.	Other NoSQL databases are CouchDB, Redis, DynamoDB, etc.
Data Schemas	Predefined schemas	Dynamic schemas
Scalability	Vertical (scale-up with a large server)	Horizontal (scale-out across commodity servers)
Joins Support	Yes	No
Triggers Support	Yes	No
Foreign Keys Support	Yes	No
ACID Transactions	Supported	Not supported
Query Language	SQL	MQL
Use case	Ideal choice for structured data.	Ideal choice for unstructured data.

The pros and cons of SQL database

Here are some of the major pros and cons of using a SQL database such as MySQL:

Pros

• Easy to learn and ANSI-SQL2008 compatible.
• Joins and indexing helps in the fast retrieval of large data.
• The SQL DB has great support and user experience.

Cons

• Database structure change can be complex.
• It requires predefined schemas.
• It’s challenging to scale up.

The pros and cons of MongoDB database

Here are some of the major pros and cons of using a NoSQL database such as Mongo DB:

Pros

• Fast queries and performance compared to SQL DB.
• Supports dynamic schema, which is a flexible data model.
• Horizontal scale-out supported.

Cons

• There is no standard schema definition
• The joins, and indexing like SQL DB aren’t available.
• Features like stored procedures/functions aren’t available. So we can’t use any business logic on databases.

Conclusion

In this article, we compared MongoDB with SQL databases. We compared different aspects and studied the pros and cons. Let me summarize the important points below.

• SQL databases store structured data, while MongoDB stores unstructured data.
• MongoDB uses MQL, while SQL databases use structured query language.
• MongoDB is faster and scalable compared to SQL databases.
• SQL DB supports JOIN & transactions while MongoDB doesn’t.
• SQL database uses predefined schemas, while MongoDB uses dynamic schema.
• Check out the official website of MySQL database.
• Check out the official website of MongoDB database.

If you’re still unsure which technology you want to learn, consider looking deeper into SQL and its use cases. There are many SQL courses that you can take to learn more.