Choosing a database is no easy feat. You must consider your organization’s current requirements and anticipate its future needs. Also, there are plenty of databases to choose from, and each type has its pros, cons, and use cases.
To help you decide, we’re diving into a MongoDB vs. MySQL comparison in this article. We’ll review each database to help you decide which one might meet your needs. We’ll also compare their performance to give you a deeper understanding of what each database offers.
An overview of MongoDB and MySQL
Before comparing MongoDB and MySQL, let’s discuss each database individually.
MongoDB explained
Developed by MongoDB, Inc., MongoDB is an open-source, non-relational database. Instead of using tables and rows, MongoDB stores individual records as JavaScript Object Notation (JSON)-like documents, which may vary in structure.
This flexibility makes it a popular choice for applications managing large amounts of unstructured or semi-structured data. Plus, its schema-less nature allows for dynamic and flexible document models; this can be particularly advantageous for agile development environments or when dealing with complex data that does not fit neatly into a relational model.
Since MongoDB can scale horizontally and includes out-of-the-box replication and sharding, it is well-suited for big data and cloud-based applications that need to handle rapid growth. MongoDB’s ability to distribute data across multiple servers makes it ideal for use cases requiring real-time data processing and high availability, such as e-commerce, content management, and the Internet of Things (IoT).
MySQL explained
MySQL’s development began in 1994, and it was released in 1995. This open-source, relational database management system is currently distributed and supported by Oracle®. MySQL has become one of the most widely used databases globally, particularly for web applications, due to its superior performance, reliability, and ease of use.
It uses structured query language (SQL) for database access, allowing users to query, insert, update, and manage data efficiently. It stores individual records as rows in a table, with the data structure defined by a fixed schema. This rigid schema ensures consistency and integrity, making MySQL a preferred choice for applications where the relationships between data are well-defined and structured queries are essential.
MySQL is popular because it ensures data reliability through atomicity, consistency, isolation, and durability (ACID) transactions. This makes it an excellent solution for businesses requiring high data integrity, such as financial systems and e-commerce platforms. Additionally, MySQL supports replication, partitioning, and clustering, allowing for scalable solutions to handle growing amounts of data and traffic.
Key differences between MongoDB and MySQL
The two databases, MongoDB and MySQL, represent distinct approaches to data storage and management, each suited to different types of applications. Some significant differences between MongoDB and MySQL include:
| Feature | MongoDB | MySQL |
| Database structure | Offers flexibility with its document-based structure and schema-less design, making it ideal for handling unstructured or semi-structured data. | Provides a structured approach to data storage with its table-based architecture and strict schemas. |
| Schema | Schema-less, meaning it does not enforce a fixed structure for documents within a collection. This flexibility allows for frequent and rapid changes to data models without needing to restructure the database, making it a good fit for dynamic, evolving applications. | Strict schema definitions for data integrity and consistency, making it well-suited for applications with fixed data models. |
| Query language | Uses MongoDB Query Language (MQL). | Follows the standard SQL, making it compatible with many applications. |
| Scalability | Designed for horizontal scalability, so it can handle large amounts of data by distributing it across multiple servers using sharding. This enables MongoDB to manage growing datasets more efficiently, particularly in cloud-based and big data applications. | While capable of scaling vertically by adding more resources to a single server, it does not natively support horizontal scaling as effectively as MongoDB. Instead, MySQL’s scalability is generally achieved through replication and partitioning, which can be more complex to manage in distributed environments. |
| Transactions | Supports multi-document transactions in its recent versions, but its transaction capabilities are not as robust or mature as MySQL’s. As a result, MongoDB is better suited for applications where strict ACID compliance is not the highest priority. | Fully ACID-compliant and excels in handling complex transactions, making it a preferred choice for financial, e-commerce, or any system requiring robust consistency and reliable transaction management. |
| Performance | Often outperforms MySQL in use cases involving high-volume, unstructured data and write-heavy workloads thanks to its ability to handle large datasets and its flexible nature. | Offers better performance in read-heavy environments and applications with complex queries that involve multiple joins. It is optimized for structured data and transactional operations, making it ideal for applications requiring consistent, real-time data integrity and retrieval. |
Pros and Cons of MongoDB and MySQL
When deciding between MongoDB and MySQL, it is essential to thoroughly compare them, for each offers distinct features, advantages, and disadvantages. To help you get started, we’ve compiled some notable pros and cons of MongoDB and MySQL, ranging from scalability to performance.
MongoDB
Pros
- Flexibility: Thanks to its schema-less design, MongoDB is incredibly flexible. You can easily store data without a predefined structure, making it perfect for managing unstructured or rapidly changing data. For example, MongoDB could be excellent for content management and real-time analytics.
- Horizontal scalability: Since MongoDB is designed to scale horizontally through sharding, it is capable of handling high traffic volumes and large datasets by distributing data across several servers.
- High availability: Thanks to its automatic failover and built-in replication features, MongoDB can ensure data availability—even if hardware fails. Consequently, it is ideal for distributed systems.
- Fast development cycles: Developers can iterate more quickly using MongoDB thanks to its flexibility in managing data models.
Cons
- Lack of ACID compliance in distributed systems: MongoDB supports ACID transactions at the document level, but its eventual consistency model can result in temporary data inconsistencies in distributed environments.
- Complex queries: MQL is more than suitable for basic document queries but does not have the same depth or complexity as SQL.
- Memory usage: Since MongoDB takes a document-based storage approach, it generally consumes more memory, leading to higher infrastructure costs.
MySQL
Pros
- Reliability and maturity: MySQL has been in use since the 1990s, and its long development history has led to it becoming a highly stable and reliable platform. This makes it a trusted choice for applications requiring robust performance and proven reliability, such as financial systems and e-commerce platforms.
- Compatibility: MySQL’s adherence to the SQL standard makes it compatible with various applications, tools, and frameworks. This broad support makes it easier to integrate into existing software ecosystems.
- ACID compliance: MySQL is fully ACID-compliant, ensuring transactional integrity with features such as atomicity, consistency, isolation, and durability. This makes it a preferred choice for applications requiring precise data consistency and reliability, such as banking or inventory management.
- Complex queries: MySQL can handle more complex queries than MongoDB, particularly regarding relational queries, joins, and transaction handling.
Cons
- Vertical scalability: While MySQL can scale, it typically relies on vertical scalability, requiring additional server resources (CPU and RAM) to handle higher loads. Horizontal scaling (across multiple servers) can be more complex than MongoDB’s built-in sharding.
- Rigid schema: MySQL enforces strict schema definitions, which can limit flexibility when handling evolving or unstructured data. Changing the schema often requires significant effort, making it less suitable for projects in need of agile data models.
- Performance with high write loads: MySQL can struggle with write-heavy workloads, especially when dealing with large volumes of data requiring frequent updates. MongoDB often outperforms MySQL in environments where high-speed write operations are critical.
- Limited JSON support: Although MySQL has added JSON support in recent versions, it is not as naturally suited to working with unstructured or semi-structured data as MongoDB, designed specifically for document-based data storage.
Use cases of MongoDB and MySQL
MongoDB is best suited for applications requiring flexible, unstructured, or rapidly changing data. For instance, content management systems often deal with varied data types, such as images, text, and metadata, making MongoDB a strong choice due to its schema-less architecture. Additionally, its ability to scale horizontally and distribute data across multiple servers makes it ideal for real-time analytics, social media platforms, and IoT applications where large amounts of data are generated and need to be processed and stored quickly.
MongoDB’s flexibility and high availability features make it perfect for cloud-native applications and microservices architectures. These environments demand rapid development cycles and efficient handling of big data—and MongoDB’s schema flexibility allows developers to iterate quickly without worrying about data model restrictions. Its built-in replication and failover capabilities make it a reliable choice for distributed systems and global applications where uptime and data consistency are critical, even in the face of hardware failures.
In contrast, MySQL is widely used in applications requiring structured data and transactional consistency. It is often the go-to choice for traditional web applications, such as e-commerce platforms, financial systems, and enterprise resource planning tools, where relationships between data are vital and transactions need to be processed with precision and accuracy. MySQL’s ACID-compliant transactional capabilities maintain data integrity, which is crucial when complex queries, joins, and transactional operations occur frequently.
MySQL is well-suited for applications where performance, reliability, and mature query handling are critical. With its robust support for SQL, MySQL is ideal for complex reporting and data analysis tasks, often seen in business intelligence applications, customer relationship management systems, and inventory management. In these use cases, MySQL’s structured schema and ability to handle large volumes of relational data make it an appropriate choice, offering developers the consistency and precision to maintain strict data structures and relationships across large datasets.
Concluding thoughts on MongoDB and MySQL
Deciding which database to use is a major decision—and choosing between MongoDB and MySQL ultimately depends on your project’s specific requirements. MongoDB offers unmatched flexibility and horizontal scalability and is ideal for applications dealing with unstructured or semi-structured data. In contrast, MySQL excels in handling structured data with its robust ACID-compliant transactions, making it perfect for applications requiring strict data integrity and complex queries. Both databases have strengths, and understanding your data needs is crucial to selecting the right tool.
Regardless of the database you choose, monitoring and optimizing performance is vital to maintaining long-term reliability. Database observability tools, such as SolarWinds Database Observability, can help you monitor and optimize MongoDB and MySQL environments. With MongoDB monitoring and MySQL monitoring capabilities from SolarWinds® Observability, you can ensure your databases operate efficiently and support your application’s needs as they grow.