MongoDB vs. MySQL: 7 Key Differences to Make the Best Decision for Your Business

Data-driven businesses use a variety of solutions to manage their growing data. MongoDB and MySQL are two of the most common database management systems (DBMS) today. Both are open-source and can handle large volumes of data for complex reporting and analysis. However, they function differently and vary in design and usage. In this article, we break down what each system offers, their differences, and how they’re used so you can choose which fits your business needs best.

What is MySQL?

MySQL is a relational database management system (RDBMS) that uses Structured Query Language (SQL) for database access and management. Developed in 1995, it organizes data into tables composed of rows and columns, which is ideal for structured data and complex queries. Known for its reliability, performance, and user-friendly interface, MySQL is a popular choice for applications requiring ACID (Atomicity, Consistency, Isolation, Durability) compliance and complex joins.

What is MongoDB?

MongoDB is a NoSQL database launched in 2007 that stores data in flexible, JSON-like documents. It is designed for scalability and high performance, especially for applications handling large volumes of unstructured or semi-structured data. MongoDB's schema-less design allows for rapid iteration and scalability, suitable for modern web applications, big data, and real-time analytics.

What’s the difference between MongoDB and MySQL?

MySQL stores data in a relational format, organizing data into tables composed of rows and columns, making it easy to use for query execution and analysis. It’s widely used in applications that need structured data and complex joins for financial, enterprise resource planning (ERP), and customer relationship management (CRM) systems. MySQL provides full ACID (Atomicity, Consistency, Isolation, and Durability) compliance, ensuring the data remains consistent and accurate.

MongoDB, on the other hand, stores data in flexible, JSON-like documents instead of tables. This non-relational approach allows for more dynamic data models, which is useful for applications that handle large volumes of unstructured or semi-structured data. MongoDB's schema-less design enables faster development cycles and easier scaling, making it ideal for modern web applications, big data projects, and real-time analytics. While MongoDB provides some ACID-like features, it traditionally follows a BASE (Basically Available, Soft state, Eventually consistent) model, prioritizing availability and partition tolerance over strict consistency.

7 differences: MongoDB vs. MySQL

Your choice between MongoDB and MySQL might differ depending on your organization’s specific needs, such as performance requirements, scalability, security, and flexibility. Here, we’ll explore some key differences between these two database systems to help you determine which one is the best fit for your organization.

Performance

MongoDB is designed to efficiently handle large volumes of unstructured or semi-structured data. Its document-oriented model provides fast read-and-write operations without complex joins or transactions, which is critical for big data applications and real-time analytics. MongoDB also supports horizontal scaling, meaning it can distribute data across multiple servers to boost performance and reliability as your data grows.

MySQL, on the other hand, specializes in handling structured data and complex queries. Its use of defined schemas and indexing makes for effective query performance. MySQL supports various storage engines, including InnoDB, ensuring ACID-compliant transactions and data integrity. For read-heavy workloads, read replicas are used to distribute the load, maintaining smooth performance even with increased demand.

Scalability

MongoDB's scalability is one of its most important features. It uses sharding, a form of horizontal scaling, to split large tables into smaller bits and store them across multiple machines, making it easier to manage large datasets and handle high-traffic applications. This allows MongoDB to scale alongside your application without major changes to your database infrastructure.

MySQL typically scales vertically by adding more resources to a single server. While it does support replication to help with read scalability, it doesn't naturally support horizontal scaling as easily as MongoDB. However, with the right configurations and third-party tools, MySQL can achieve horizontal scaling, although this setup tends to be more complex and requires ongoing management.

Security

MongoDB provides advanced security features, including authentication, authorization, encryption, and auditing. It supports role-based access control (RBAC), allowing administrators to define specific roles and permissions. MongoDB also ensures data encryption both at rest and in transit, which helps protect sensitive information and comply with industry standards.

MySQL also offers comprehensive security measures, like RBAC, data encryption at rest (using the InnoDB storage engine), and secure connections via SSL/TLS (Secure Socket Layer/Transport Layer Security). These features help safeguard data and meet regulatory requirements, making MySQL a reliable option for secure data management.

Flexibility

MongoDB's schema-less design offers impressive flexibility in handling diverse data types and structures. This allows developers to make changes to the data model quickly without extensive schema modifications, making it ideal for applications that require frequent updates and iterations. MongoDB supports rich queries, indexing, and aggregation, providing versatile options for data storage and access.

MySQL, while more rigid in schema design, offers flexibility through its support for various storage engines, plugins, and configuration options. The structured nature of MySQL helps enforce data integrity and relationships, which is valuable for complex data models and transactional applications. MySQL’s versatility makes it suitable for a wide range of applications, from simple web apps to complex enterprise systems.

Data modeling

MongoDB uses a flexible data model, allowing documents within a collection to have different structures. This is beneficial for applications where data requirements evolve over time or are not strictly defined. Developers can store complex data types and hierarchical relationships directly within documents, reducing the need for complex joins.

Being a relational database, MySQL uses a fixed schema model where the structure of the data is defined upfront. This ensures consistency and integrity of the data but can make it more difficult to adapt to changing requirements. Relationships between different entities are managed through foreign keys and joins.

Query language

MongoDB uses a query language based on JavaScript and supports rich queries, including geospatial queries, text search, and aggregation framework. Its query language allows for powerful data manipulation directly within the database, making it suitable for applications with complex querying needs.

MySQL uses SQL, a powerful and widely adopted language for managing and querying relational databases. It provides commands for data manipulation, aggregation, and reporting, perfect for applications that require complex data interactions and analysis.

Licensing and cost

MongoDB offers a free, open-source version and a commercial version with additional features and support. Its licensing model has changed over time, and it's important to review the current terms to ensure compliance.

MySQL is available under the GNU General Public License (GPL) for the community edition, while Oracle offers commercial licenses for MySQL Enterprise Edition, which includes additional features, support, and services.

Similarities between MongoDB and MySQL

MongoDB and MySQL share some similarities that make them both valuable in different contexts:

Programming language support

Both MongoDB and MySQL offer extensive support for a wide range of programming languages. This includes Python, Java, C#, PHP, Ruby, and Node.js. The broad language support allows either database to integrate seamlessly into a wide range of development environments and applications, providing flexibility for developers working on diverse projects.

Open-source licenses

MongoDB and MySQL are both open-source databases, which means their source code is available for free and can be modified and distributed by anyone. They are supported by a large community of users that contribute to regular updates, improvements, and a wealth of resources for troubleshooting and learning. MongoDB is licensed under the Server Side Public License (SSPL), while MySQL is licensed under the GNU General Public License (GPL).

Cloud-native application support

Both databases work well with cloud-native applications and are available on various cloud platforms, including AWS, Google Cloud, and Microsoft Azure, as well as managed services like Amazon RDS for MySQL and MongoDB Atlas. These services boost the databases’ scalability, provide automated backups, and support high availability, making it easier to deploy and manage databases in the cloud.

When to use MongoDB vs. MySQL

Depending on what you're building, your specific requirements, and other factors, either database may offer advantages over the other. Here are some common scenarios for each to help you choose:

MongoDB use cases

1. Internet of Things (IoT): MongoDB is great for IoT applications due to its ability to handle large volumes of unstructured data and its flexible schema design. Its scalability allows for seamless integration with numerous IoT devices and the real-time processing of their data.
2. Big data applications: Its document-oriented model and scalability make MongoDB ideal for big data projects that require handling large datasets and fast processing. MongoDB's distributed architecture supports horizontal scaling, making it capable of managing the vast amounts of data typical in big data environments.
3. Real-time analytics: MongoDB's performance and horizontal scaling capabilities make it a good match for real-time data analysis and applications that need to process data quickly. Its ability to handle high-speed data ingestion and provide rapid query responses is essential for analytics platforms.
4. Content management systems: The flexible schema design of MongoDB makes it a good choice for content management systems where the data structure can change frequently. This enables easy updates and modifications to content without disrupting the system.
5. Mobile and social applications: MongoDB's ability to handle large amounts of unstructured data and provide fast query performance is perfect for mobile apps and social platforms that require scalability and flexibility. Its JSON-like document structure aligns well with the data formats used in these applications.

MySQL use cases

1. High-traffic websites: MySQL's structured data storage and ability to handle complex queries make it a good choice for high-traffic websites that need reliable performance and data consistency. Its robust transactional support ensures that high-volume transactions are processed accurately and efficiently.
2. Financial applications: The ACID-compliant transactions in MySQL ensure data integrity, making it suitable for financial applications that require precise and accurate data handling. MySQL's security features also help protect sensitive financial data.
3. Government and compliance-heavy industries: MySQL's strong security features and ability to ensure data consistency make it suitable for industries that need to comply with strict regulatory requirements. Its ability to maintain detailed audit trails helps organizations meet compliance standards.
4. Enterprise resource planning (ERP) systems: MySQL's structured data storage and ability to handle complex joins work well for ERP systems that require reliable data management. Its support for complex queries and transactions helps manage the diverse data needs of these applications.
5. Customer relationship management (CRM) systems: The robust transactional support in MySQL makes it ideal for CRM systems that need to manage and process customer data efficiently. Its scalability allows CRM systems to grow with the increasing volume of customer interactions and data with little effort.

Connect to your database with CData

Whether you choose MongoDB, MySQL, or any other database, integrating your data sources with other applications is vital for efficient data management. CData Connect Cloud offers seamless connectivity, providing real-time data access and secure connections, allowing you to easily link your database to cloud applications, BI tools, and more, ensuring your data is always up-to-date and accessible.