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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:

…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.

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Introduction to terminologies

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

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.

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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:

fullnamegenderagephoneaddress
John Doemale351234567890Flat 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
HistoryDeveloped in 1995Developed in the late 2000s
Data Storage ModelData stored in tables with rows & columnsData stored in collections with key-value pair documents
Example DatabasesOracle, SQL Server, PostgreSQL, etc.Other NoSQL databases are CouchDB, Redis, DynamoDB, etc.
Data SchemasPredefined schemasDynamic schemas
ScalabilityVertical (scale-up with a large server)Horizontal (scale-out across commodity servers)
Joins SupportYesNo
Triggers SupportYesNo
Foreign Keys SupportYesNo
ACID TransactionsSupportedNot supported
Query LanguageSQLMQL
Use caseIdeal 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

Cons

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

Cons

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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.

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.

Page Last Updated: March 2022