MongoDB vs. MySQL: Which database to use

In today’s data-driven world, choosing the right database management system (DBMS) is a make-or-break decision for your application. MySQL, a well-established relational database, has been used by developers for a while now. However, with the increasing need for scalability and flexibility, NoSQL databases like MongoDB have gained significant attention. Both MongoDB and MySQL offer high performance and robust functionalities, but they cater to different needs and use cases.

This guide explores MongoDB and MySQL, breaking down their features, use cases, limitations, and key differences to help you choose the best fit for your needs.

Now, let’s dive deeper into each database, starting with MongoDB.

What is MongoDB?#

Developed in 2007, MongoDB is a popular NoSQL, non-relational database management system. Instead of using tables and rows as in traditional relational databases, MongoDB uses JSON-like documents with optional schemas.

This data model allows for the manipulation of related data in a single database operation. MongoDB documents are JavaScript supported and the document fields can vary, making it easy to change the structure over time.

MongoDB: Key features#

1. Document-oriented storage: Stores data in flexible, JSON-like documents (using BSON format), allowing for complex data structures and hierarchical relationships within a single record.

2. Dynamic schema: Eliminates the need for a predefined schema, enabling developers to modify the data structure without downtime or complex migrations.

3. Replication and high availability: Provides redundancy and failover capabilities by maintaining multiple copies of data across different servers. In case of node failure, MongoDB can automatically elect a new primary node to maintain availability.

4. Horizontal scalability: Distributes data across multiple servers using shard keys, allowing for linear scalability as data volume grows.

5. Load balancing: Automatically balances data and load across shards to optimize performance.

MongoDB: Uses#

1. Flexible data models: Ideal for applications that handle diverse data types and structures, such as product catalogs.

2. Content management systems (CMS): Ideal for applications that handle diverse data types and structures, such as CMS and product catalogs.

3. Real-time analytics: Processes and analyzes data in real-time, beneficial for fraud detection, recommendation engines, and personalized user experiences.

4. Mobile and IoT applications: Ideal for IoT applications that enable local data storage and synchronization when connectivity is limited. Collects and stores data from numerous devices for processing and analysis.

MongoDB: Limitations#

• MongoDB does not support joins in the traditional SQL sense. While it allows the $lookup operator in the aggregation pipeline to perform join-like operations—essentially a left outer join—however, it may not be as efficient or straightforward.

• MongoDB does not support traditional flow control structures like IF-ELSE, WHILE, or FOR within its queries, which are available in MySQL’s stored procedures and functions. For example, you cannot directly implement conditional logic or looping inside a MongoDB query. For simple control flows, you can use MongoDB's aggregation pipeline that can handle some conditional operations using stages like $cond and $switch or for more complex control flow, you can implement the logic at the application level instead of within the database.

• It may have high memory usage because of the key-value pairs that can result in data redundancy. To mitigate this, you need to optimize the schema to reduce unnecessary memory consumption.

• Each document has a maximum size of 16MB.

• MongoDB encourages denormalization by embedding related data within documents, which can lead to data redundancy.

What is MySQL?#

Developed in 1995, MySQL is an open-source, relational database management system (RDBMS) that stores data in tables and rows. It uses Structured Query Language (SQL) to transfer and access data. JOIN operations simplify querying and correlation. It follows the client-server architecture and supports multi-threading.

Since the MySQL database was established, it has had a huge community, extensive testing, and stability. It is available for all major platforms, along with connectors to many languages, including C, Java, and C++.

Key features of MySQL#

1. Structured schema: Uses tables, rows, and columns with predefined data types, enforcing a structured schema and relationships between tables using foreign keys.

2. ACID compliance: Supports transactions that ensure data integrity through Atomicity, Consistency, Isolation, and Durability (ACID) properties.

3. High performance: Provides advanced optimization strategies for executing SQL queries efficiently.

MySQL: Uses#

1. Backend database: MySQL is commonly used as the backend database for dynamic websites and web applications. It stores and manages data for platforms built with languages like PHP, Python, and Ruby.

2. E-commerce platforms: Online stores use MySQL to manage product catalogs, customer data, shopping carts, and transaction histories.

3. Data warehousing and analytics: MySQL is used to store large volumes of data for analysis, reporting, and decision-making processes.

4. Logging applications: Stores logs from applications and servers for monitoring performance, security, and troubleshooting.

Limitations of MySQL#

The limitations of MySQL are that of any other RDBMS, including:

• Since data is stored in tables, if you have data that does not fit into any table, you will have to re-design your database structure to accommodate it.

• The database has to be distributed across multiple servers, which can be difficult to manage.

• MySQL becomes less efficient when it comes to large databases due to scaling issues.

• MySQL is vulnerable to SQL injection, a common cybersecurity attack that exploits improperly sanitized user inputs. However, it provides built-in mechanisms like prepared statements and parameterized queries to mitigate this risk effectively.

MySQL vs MongoDB#

In this section, we’ll explore the key differences between MongoDB and MySQL, providing detailed explanations to help you understand how each database operates.

Data representation#

MySQL uses a relational data model where data is stored in tables consisting of rows and columns. Each table represents an entity, and each row represents a record within that entity. Columns define the attributes of the entity, and each column has a specific data type. Relationships between tables are established through foreign keys, allowing for complex queries and data integrity across the database.

To visualize, this is how the table will look.

MongoDB, conversely, uses a document-oriented data model. Data is stored as BSON (Binary JSON) documents within collections. Each document can have a unique structure, and related data can be embedded within the same document as nested fields or arrays. This allows for storing complex hierarchical data in a single document, reducing the need for JOIN operations.

Schema flexibility#

In MySQL, the schema must be defined before any data can be inserted. This means specifying tables, columns, data types, and relationships upfront. Any changes to the schema, such as adding a new column or altering a data type, require executing certain commands and may involve downtime or impact application functionality.

MongoDB offers a dynamic schema, allowing documents within the same collection to have different structures. Developers can add or remove fields without affecting other documents or requiring schema migrations. This flexibility accelerates development cycles and adapts to changing data requirements.

Query language#

MySQL uses SQL, whereas MongoDB uses MQL, the MongoDB query language. We will compare some common database operations on the Employee table in this section.

MySQL utilizes SQL, a powerful and widely used language for database management. SQL provides a set of commands for querying, updating, and managing relational databases. It supports complex operations like joins, subqueries, and transactions, enabling us to perform intricate data manipulations and aggregations. Some of the common operations in MySQL are:

Selecting data#

To select all the data from the table, we can use an Asterisk * along with a FROM command followed by the table name.

To select data from a specific record, you can use the WHERE clause to filter by a condition.

Inserting data#

To insert data into any column, we can use an INSERT INTO a command followed by the table name and column names, proceeded by the VALUES commands, the values for those columns.

Updating data#

To update specific rows, we can use the UPDATE command followed by the table name, the SET clause to define new values, and a WHERE clause to specify the rows to update.

Deleting data#

We can use the DELETE statement to remove records from a table. We can specify which records to delete by using the WHERE clause. If no WHERE clause is provided, all rows in the table will be deleted.

MongoDB uses MQL (MongoDB Query Language), which is JavaScript-based and leverages JSON-like syntax. MQL is designed to work seamlessly with the hierarchical nature of documents, making it intuitive for developers familiar with JSON and JavaScript. Some of the common operations in MongoDB are:

Selecting data#

To retrieve documents from a collection, we can use the find method.

We can also use the find() method with a query object to specify the record to retrieve.

Inserting data#

To add a single document to a collection, we can use the insertOne method.

However, if we want to insert multiple records, we can use the insertMany() method. This method allows us to insert an array of documents (records) into a collection at once.

Updating data#

We can use the updateOne method to update specific fields in a document.

Deleting data#

We can use the deleteOne() method to delete a single document and the deleteMany() method to delete multiple documents from a collection.

Indexing and optimization#

Both databases rely on indexing to enhance performance, but the way indexes are applied can differ due to the data models.

In MySQL, indexes are based on table columns with predefined data types. Common data types include numeric types: (INT, BIGINT, SMALLINT, FLOAT, DOUBLE, DECIMAL), string types (CHAR, VARCHAR, TEXT, BLOB), date/time types (DATE, DATETIME, TIMESTAMP, TIME). These data types help MySQL determine how to store and index the data for efficient querying. If MySQL does not find a relevant index for a query, it searches the entire table.

MongoDB allows us to create indexes on fields within documents, including nested fields and arrays. It searches through every document in a collection if there are no indexes.

Replication and high availability#

MySQL supports replication through source-replica and multi-primary configurations. In source-replica replication, the source handles write operations, and replicas handle read operations.

MongoDB provides high availability through replica sets, which are groups of mongod processes that maintain the same data set. One node is the primary, and the others are secondaries. If the primary fails, the replica set automatically elects a new primary, ensuring minimal downtime. Combined with sharding, MongoDB offers fault tolerance and scalability.

Scalability and performance#

MySQL is traditionally vertically scalable, meaning performance improvements are achieved by adding more resources (CPU, RAM, SSD) to a single server. While MySQL can handle significant workloads, scaling beyond a single server (horizontal scaling) requires complex configurations like partitioning or using external tools. MySQL excels in applications with read-heavy workloads and complex transactions.

MongoDB is designed for horizontal scalability through sharding, which partitions data across multiple servers. This allows MongoDB to handle massive datasets and high-throughput applications by distributing the load. MongoDB automatically balances data and query loads across shards, providing near-linear scalability. It performs well in write-intensive applications and those handling large volumes of unstructured data. However, challenges such as selecting an appropriate shard key and managing query complexity require careful planning and monitoring.

Security#

Security in MySQL is managed through user authentication and privilege assignment. Users are granted specific permissions on databases, tables, or even columns. MySQL supports SSL/TLS encryption for secure data transmission. Advanced security features like auditing and transparent data encryption are available in enterprise editions.

MongoDB employs role-based access control (RBAC) with fine-grained permissions. Authentication can be integrated with external systems like LDAP and Kerberos. MongoDB supports SSL/TLS for encrypted communication and offers field-level encryption, allowing sensitive data to be encrypted at the application level before storage.

To summarize the differences between MySQL and MongoDB, refer to the following table:

MySQL vs MongoDB

When to use: MongoDB vs MySQL#

When it comes to choosing between the two, there is no clear winner, as both cater to different fields. Your choice will depend on your project needs and goals. In this section, we will look at when you can use MySQL vs. MongoDB.

Choose MySQL when:

• Your application relies heavily on complex SQL queries involving multiple tables and relationships.

• Your data structure won’t change for a long time, or you have a fixed schema.

• You have a high transaction rate.

• Data security is your top priority.

• You need better support. MySQL has been around for a long time, so, it’s a lot easier to find solutions to common problems

MySQL is a good choice if you are working with a legacy application that requires multi-row transactions and has structured data with a clear schema.

Choose MongoDB when:

• You want high data availability along with automatic and instant data recovery.

• You are working with an unstable schema and want to lower the cost of schema migration.

• Your services are cloud-native, and you need to distribute data across multiple geographic locations.

MongoDB can be the right choice if you are working with real-time analytics, mobile applications, the internet of things, etc., where you may have structured or unstructured data with the potential for rapid growth. Ultimately, MongoDB's document model is ideal for flexible, hierarchical data, while MySQL's structured approach supports complex relationships.

Remember, the best database is the one that aligns with your application's unique requirements, growth trajectory, and operational priorities.

Continue learning MySQL and MongoDB#

Explore these projects for hands-on practice with MySQL and MongoDB:

• Build a Custom Form Maker with MERN Stack

• Recipe Sharing Application with Laravel and Alpine.js

• Build a Location Tracker Using Leaflet, ReactJS, and Express.js

• Develop a Microservice Using CakePHP