Software Development

SQL and NoSQL Integration

In today’s data-driven world, businesses often find themselves managing a diverse range of data types, from structured information requiring traditional SQL databases to unstructured data suited for NoSQL databases. This dynamic data landscape has led to an increased interest in combining the strengths of both SQL and NoSQL databases to achieve more comprehensive and flexible data management solutions.

One promising avenue for achieving this synergy is the compatibility between MariaDB, a popular open-source relational database, and MongoDB, a widely-used NoSQL database. This compatibility opens the door to a new era of data integration, allowing organizations to harness the power of structured and unstructured data seamlessly. In this exploration, we’ll delve into the potential of merging MariaDB and MongoDB, highlighting the advantages, use cases, and considerations for effectively integrating SQL and NoSQL databases. Let’s gain insight into SQL and NoSQL Integration.

1. Introduction to Maria DB

In the realm of web development, choosing the right database management system can significantly impact the performance, scalability, and efficiency of your applications. MariaDB, an open-source relational database system, has garnered significant attention as an alternative to MySQL, offering a robust and feature-rich option for developers. In this article, we’ll introduce you to MariaDB, explore its advantages and disadvantages, and help you make an informed decision for your web development projects.

1.1 Use Cases

  • Enterprise Resource Planning (ERP) Systems
  • Customer Relationship Management (CRM) Software
  • Content Management Systems (CMS)
  • Financial and Accounting Applications
  • Inventory Management Systems

1.2 Advantages

  • Cost-Effective: MariaDB is open-source and free to use, making it budget-friendly for startups and small businesses.
  • Community-Driven: MariaDB has an active and passionate community that ensures frequent updates and enhancements.
  • Compatibility with MySQL: It’s a drop-in replacement for MySQL, simplifying migration for existing MySQL users.
  • High Performance: MariaDB offers advanced storage engines for improved data compression and query execution speed.
  • Security Features: MariaDB provides various authentication plugins and supports data-at-rest encryption.
  • Dynamic Columns: It allows for flexible schema design with dynamic columns.
  • Active Development: MariaDB continually evolves, staying up-to-date with industry requirements.

1.3 Disadvantages

  • Smaller Ecosystem: MariaDB may have fewer third-party tools and integrations compared to MySQL.
  • Limited Enterprise Support: Some advanced features and support may require a paid subscription to MariaDB Corporation.
  • Migration Challenges: Complex database structures may face challenges during the migration process.
  • Learning Curve: While similar to MySQL, MariaDB introduces unique features that may require developers to adapt and learn.

2. Introduction to MongoDB

In the world of modern data-driven applications, the need for flexible and scalable database solutions has never been more critical. MongoDB, a NoSQL database management system, has emerged as a powerful and versatile tool for developers and organizations seeking to harness the potential of unstructured or semi-structured data. In this article, we’ll explore MongoDB, its fundamental principles, and the advantages it offers in the realm of data storage and retrieval. Here are some of the key features that make MongoDB stand out:

  • Flexible Schema: MongoDB’s schema-less approach allows you to store data without a predefined structure, making it adaptable to changing data requirements.
  • Scalability: MongoDB is horizontally scalable, meaning you can distribute your data across multiple servers to handle high loads effortlessly.
  • High Performance: With support for indexing and advanced querying, MongoDB delivers fast read and write operations.
  • Rich Query Language: MongoDB offers a powerful query language for filtering, sorting, and aggregating data.
  • Automatic Sharding: MongoDB can automatically distribute data across multiple servers for horizontal scaling.
  • Geospatial Capabilities: It provides geospatial indexing and querying, making it ideal for location-based applications.

2.1 Use Cases

MongoDB finds application in various scenarios, including:

  • Content Management Systems (CMS)
  • Real-time Analytics
  • Internet of Things (IoT) Applications
  • Catalogs and Product Information Management
  • Mobile App Backends
  • Logging and Event Tracking

2.2 Advantages

  • Flexible Schema: MongoDB’s schema-less approach allows for flexible and dynamic data modeling.
  • Scalability: It is horizontally scalable, making it suitable for handling large datasets and high loads.
  • High Performance: MongoDB supports indexing and advanced querying, providing fast read and write operations.
  • Rich Query Language: It offers a powerful query language for filtering, sorting, and aggregating data.
  • Automatic Sharding: MongoDB can automatically distribute data across multiple servers for horizontal scaling.
  • Geospatial Capabilities: Ideal for location-based applications with geospatial indexing and querying support.
  • Document-Oriented: Data is stored in JSON-like documents, making it easy to work with for developers.
  • Community and Ecosystem: MongoDB has a thriving community and a wide range of tools and libraries.

2.3 Disadvantages

  • No ACID Transactions: MongoDB sacrifices ACID (Atomicity, Consistency, Isolation, Durability) transactions for flexibility and performance, which may not be suitable for all use cases.
  • Memory Usage: MongoDB can be memory-intensive, and the working set should fit in RAM for optimal performance.
  • The complexity of Queries: Complex queries, such as multi-join operations, can be challenging to implement in MongoDB compared to traditional relational databases.
  • Learning Curve: Developers accustomed to relational databases may face a learning curve when transitioning to MongoDB’s document-oriented approach.
  • Size and Storage Overhead: The BSON (Binary JSON) format used by MongoDB documents can lead to some storage overhead.
  • Limited Transactions: While MongoDB supports multi-document transactions, they are not as robust as traditional relational database transactions.

3. Setting up MariaDB on Docker

In the present world, Docker is an important term –

  • Often used in CI/CD platform that packages and runs the application with its dependencies inside a container
  • Is a standard for Linux Containers
  • A Container is a runtime that runs under any Linux kernel and provides a private machine-like space under Linux

If someone needs to go through the Docker installation, please watch this video.

3.1 Creating MariaDB container

Use the below docker-compose.yml file that will help to set up the MariaDB on Docker.


version: '3.8'

    image: mariadb
    container_name: my-mariadb-container
      MYSQL_USER: your_username # Add your desired username here
      MYSQL_ROOT_PASSWORD: my-secret-pw
      - "3306:3306"
      - ./data:/var/lib/mysql

To get the Maria db up and running we will trigger the following command – docker-compose -f /docker-compose.yml up -d. If the images are not present in the host environment then they will be downloaded from the Dockerhub repository and the whole process might take a minute or two.

Once done you can use the – docker ps command to confirm whether the containers are running or not as shown in the below image. The Maria db will be updated on its default port number – 3306 and you’re free to change the port number as per your setup.

SQL and NoSQL Integration
Fig. 1: Running Maria DB container

You can also use the following command – docker-compose -f /docker-compose.yml up -d to clean up the created environment.

4. Creating a SQL Table in MariaDB

Connect to the Maria db server with the help of the mysql client.

mysql -u your_username -p

Replace your_username with your actual database username. You will be prompted to enter your password.

If you want to create the table in a specific database, select that database using the USE statement. If you don’t specify a database, the table will be created in the default database.

USE your_database;

Replace your_database with the name of the database you want to use.

Use the CREATE TABLE statement to define your table’s structure. You need to specify the table name, column names, data types, and any constraints (e.g., primary key, foreign key). Here’s a basic example of creating a table for storing user information:

   username VARCHAR(50) NOT NULL,

In this example:

  • users is the name of the table.
  • user_id is an integer column with auto-increment and primary key constraints.
  • username is a VARCHAR column with a maximum length of 50 characters and is required (NOT NULL).
  • email is a VARCHAR column with a maximum length of 100 characters, required (NOT NULL), and has a unique constraint to ensure email uniqueness.
  • created_at is a TIMESTAMP column with a default value of the current timestamp.

To verify that the table was created successfully and view its structure, you can use the DESCRIBE or SHOW COLUMNS statement:


This will display the details of the table’s columns and their properties.

That’s it! You’ve successfully created a SQL table in MariaDB. You can now insert data into the table and perform various SQL operations on it as needed.

5. Creating a JSON Collection in MariaDB

MariaDB, being a relational database, does not natively support JSON collections like NoSQL databases such as MongoDB. However, it does offer support for JSON data in the form of JSON columns, allowing you to store and query JSON data within a relational table. Here’s how you can create a table with a JSON column in MariaDB:

Connect to the Maria db server with the help of the mysql client.

mysql -u your_username -p

Replace your_username with your actual database username. You will be prompted to enter your password.

If you want to create the table in a specific database, select that database using the USE statement. If you don’t specify a database, the table will be created in the default database.

USE your_database;

Replace your_database with the name of the database you want to use.

You can create a table with a JSON column using the CREATE TABLE statement with the JSON data type. For example, let’s create a table named json_data with a JSON column named data:

CREATE TABLE json_data (
data JSON

In this example:

  • json_data is the name of the table.
  • id is a standard integer column and the primary key.
  • data is a JSON column where you can store JSON-formatted data.

You can insert JSON data into the JSON column using the INSERT INTO statement. For example:

INSERT INTO json_data (data) VALUES ('{"name": "John", "age": 30}');

This inserts a JSON object into the data column.

Keep in mind that while MariaDB supports JSON data, it’s not a NoSQL database, so querying and indexing JSON data may not be as efficient as with dedicated NoSQL databases like MongoDB or Elasticsearch.

6. Using JSON Functions in MariaDB

MariaDB provides a set of JSON functions that allow you to work with JSON data stored in JSON columns or manipulate JSON data in various ways within your SQL queries. Here are some commonly used JSON functions in MariaDB:

JSON_EXTRACT(json_object, path)Extracts a JSON value from a JSON object at the specified path.SELECT JSON_EXTRACT('{"name": "John", "age": 30}', '$.name') AS name;
JSON_UNQUOTE(json_string)Removes the quotes from a JSON string.SELECT JSON_UNQUOTE('"John"') AS name;
JSON_SET(json_object, path, new_value)Sets a new value at the specified path in a JSON object.SELECT JSON_SET('{"name": "John", "age": 30}', '$.age', 35) AS updated_json;
JSON_ARRAY(element1, element2, ...)Creates a JSON array from one or more elements.SELECT JSON_ARRAY('apple', 'banana', 'cherry') AS fruits;
JSON_OBJECT(key1, value1, key2, value2, ...)Creates a JSON object from key-value pairs.SELECT JSON_OBJECT('name', 'John', 'age', 30) AS person;
JSON_ARRAYAGG(expression)Aggregates values into a JSON array.SELECT JSON_ARRAYAGG(name) FROM employees;
JSON_OBJECTAGG(key, value)Aggregates key-value pairs into a JSON object.SELECT JSON_OBJECTAGG(name, salary) FROM employees;
JSON_SEARCH(json_object, one_or_all, search_key, escape_char)Searches for a specified key within a JSON object and returns the path to the matching key.SELECT JSON_SEARCH('{"name": "John", "age": 30}', 'one', 'age') AS path;

These are just a few examples of the JSON functions available in MariaDB. You can use these functions in your SQL queries to manipulate, query, and aggregate JSON data stored in JSON columns or provided as JSON literals.

7. MariaDB vs. MongoDB Comparison

Data ModelRelational Database (SQL)NoSQL (Document-Oriented)
SchemaFixed Schema (Structured)Dynamic Schema (Schema-less)
Query LanguageSQL (Structured Query Language)MongoDB Query Language (JSON-like)
ScalabilityVertical (Scaling Up)Horizontal (Scaling Out)
ACID ComplianceFull ACID ComplianceEventual Consistency (Configurable ACID)
Use Cases
  • Structured Data
  • Transactions
  • Complex Queries
  • Join Operations
  • Data Integrity
  • Unstructured or Semi-Structured Data
  • Scalable Web Applications
  • Real-time Analytics
  • Flexible Schema
  • High Write Throughput
Community/SupportStrong Open-Source CommunityActive Community and Commercial Support
Example Use Cases

Content Management Systems (CMS), Enterprise Applications, Traditional RDBMS Use Cases

Content Management, IoT Data Storage, Real-time Analytics, Mobile App Backends

  • Optimized for complex queries and JOIN operations.
  • Supports indexing and query optimization techniques.
  • Well-suited for OLAP (Online Analytical Processing) workloads.
  • Designed for high write throughput and horizontal scaling.
  • Supports sharding for distributing data and improving read/write performance.
  • Well-suited for OLTP (Online Transaction Processing) workloads.
Memory Usage
  • Tends to be more memory-intensive, requiring sufficient RAM for optimal performance.
  • Well-suited for systems with ample memory available.
  • Memory usage depends on the data working set size and read patterns.
  • May be less memory-intensive compared to MariaDB for some use cases.

8. Conclusion

Combining MariaDB, a structured SQL database, with MongoDB, a flexible NoSQL database, presents a compelling strategy for addressing the diverse data storage and retrieval needs prevalent in modern applications. This compatibility harnesses the individual strengths of both systems, offering a versatile solution for various use cases. MariaDB excels in applications with well-defined schemas and complex relationships, leveraging SQL’s querying power and ACID transactions. MongoDB, on the other hand, embraces schema-less flexibility, making it ideal for accommodating unstructured or semi-structured data and providing horizontal scalability. This hybrid approach offers adaptability to evolving data requirements, enabling seamless integration of structured and unstructured data, and catering to a wide range of use cases, from traditional business applications to real-time analytics and high-write-throughput web-scale applications. Careful consideration of performance, memory usage, and community support is essential when choosing to combine these two database systems effectively. Overall, the compatibility of MariaDB and MongoDB provides a dynamic approach to meet the complex data management demands of modern applications.


An experience full-stack engineer well versed with Core Java, Spring/Springboot, MVC, Security, AOP, Frontend (Angular & React), and cloud technologies (such as AWS, GCP, Jenkins, Docker, K8).
Notify of

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Inline Feedbacks
View all comments
Back to top button