Choosing the Right Database: A Guide to Relational vs. Non-Relational Options

A database is essentially a well-organized collection of data. This data can exist in either an unorganized or organized form and can include numbers, strings or even binary digits. As a user, you interact with various databases in your day-to-day life without even knowing. When scrolling through reels on social media, what are those? They are data stored in the app’s database.

In this Blog, we will be covering in depth-

1. What is database?
2. Types of databases — Relational and Non-Relational Models
3. Factors to consider when choosing a Database

What is Database?

As we are familiar with the word “Data” meaning information or facts, Database is a systematic collection of data which is electronically stored. Electronic data storage system stores information digitally that enables categorization amongst data such as file type, creation date, file size etc.

To easily access data, we require database management systems (DBMS), which are designed for creation and manipulation of database. DBMS logically organizes each data and provides a user-interface path to the database. The user has free access to store, delete, update or retrieve data.

Types of Database Models

There are two different type of database models namely,

A. Relational Database Models aka SQL

Relational database is a model, where data is stored in table with predefined schema and relationships between them i.e. the data is stored in a systematic table where rows and columns are interconnected with each other.

Primary key acts as a unique identifier for a column or a set of columns that are designated to each row. Ex — employee’s id, course code etc. There are numerous keys that aid in identifying and naming columns for an enhanced experience, as well as functions that improve efficiency.

ACID properties:

• Atomicity: All the transactions are considered as a single unit which result in either complete success or failure. For instance, if you transfer $100 from account X to account Y, the entire transaction must complete to maintain consistency. If it fails after deducting from X but before adding to Y, the database would be inconsistent.
• Consistency: After any transaction, the data will remain consistent from beginning to end, devoid of any irregularities. For example, if the total amount before a transaction is $700, it should remain the same after the transaction completes.
• Isolation: Any intermediate transaction is hidden to others, to smooth the concurrent transaction. Changes made by one transaction are not visible to others until committed. Isolation ensures that concurrent execution results in an equivalent state as if executed serially.
• Durability: After the completion of the transaction, changes made in data become permanent. Even if there’s a system failure, the database remains consistent. Durability ensures that committed data persists.

SQL (Structured Query Language) -

SQL is a query language which communicates with the database, to retrieve information for which the query is raised.

This makes the database more scalable that means after creation of database we can vertically or horizontally scale easily while ensuring ACID properties.

Functions such as JOIN, SELECT, allows us to link multiple dataset and access conditional information retrieval.

1. MySQL: MySQL is an open-source, highly scalable, and flexible RDBMS that offers robust ease of use and strong community support.

2. PostgreSQL: It is an open-source RDBMS that focuses on extensibility, compliance, and performance, supporting advanced features like custom data types, full-text search, and spatial data operations.

3. Microsoft SQL Server: It is designed for enterprise-level applications, offering tight integration with Microsoft products, advanced security features, and robust business intelligence capabilities.

B. Non-Relational Database Models aka NoSQL

A non-relational database is a database that cannot be categorized in the tabular form in a predefined schema. It can be stored as JSON document, key/value pairs, or as a graph etc. Selection of the storage is done based on the specific requirements for optimization.

SQL is not the primary query language.

Key Features:

· It has greater flexibility in handling data.

· It provides scalability and flexibility to meet specific requirements.

· It’s easier to work with specific workloads like high write load etc.

· It supports horizontal scalability which makes it easier to distribute data across multiple servers.

Types of Non-Relational DB -

1. Document DB: Stored in semi-structured formats like JSON (JavaScript Object Notation) or BSON (Binary JSON), they offer flexibility with nested fields and arrays, ideal for dynamic data models in apps like content management and user profiles; examples include MongoDB and CouchDB.

2. Key-Value Store: Key-value stores use unique keys to identify, and access associated data, offering high read and write performance ideal for caching, session management, and real-time analytics. Examples include Redis and Amazon DynamoDB.

3. Column-Family Store: Also known as wide-column stores, they store data in columns rather than rows, enhancing efficiency for read and write operations on specific columns. Ideal for large-scale applications with high write loads and time-series data, such as IoT systems and log analysis; examples include Apache Cassandra and HBase.

4. Graph Database: Graph databases like Neo4j and Amazon Neptune store data as nodes and edges, perfect for handling complex relationships in applications such as social networks, fraud detection, and knowledge graphs.

A key advantage of NoSQL databases is their schema-less design, allowing for greater flexibility in handling diverse and dynamic data models. This makes it easy to adapt to changing requirements and add new data types without extensive modifications, unlike SQL databases. NoSQL also excels in horizontal scalability, distributing data across multiple servers with built-in support for replication, sharding, and partitioning, making it ideal for large-scale applications with high write loads.

Additionally, NoSQL performs well under specific conditions, such as high write loads and large-scale data retrieval, optimizing performance while maintaining data consistency and reliability. However, NoSQL can be less versatile than SQL for complex queries and data analysis, which can be a limitation for applications requiring complex querying, joining, and aggregation of data. Developers may also need to learn multiple query languages and paradigms when working with NoSQL.

Factors to consider when choosing a database

The selection of correct database for any model or application is the most essential to ensure optimal solution, accuracy, and performance.

It is best to evaluate certain factors while choosing database such as:

1. Data model and structure

2. Scalability requirement

3. Consistency and reliability

4. Query complexity and frequency

5. Performance and latency

6. Maintenance and operational complexity

1. Data model and Structures:

The data model is the primary factor while choosing a database. It provides us with the basic structure of the database i.e. how the data and queries are presented or organized. It helps us to identify database type by analysing the complexity and diversity and relationship within the data.

• Tabular Data (structured data): If the data model is tabular with well-defined relationship, Relational/SQL database is suitable to maintain data integrity.
• Hierarchical or Dynamic Data (unstructured data): If the data is schema-less i.e. unstructured or no well-defined relationship, non-relational or document-oriented database is appropriate. NoSQL often uses alternative data models such as document, key-value, column-family or graph for better accommodation of diverse and evolving data.

2. Scalability Requirements:

The factor “scalability” comes into play when it is expected that the model or application may have a growing database. With the increases in digital user traffic, it is important to look for such database which offers scalability.

There are two types of scalabilities-

• Vertical Scalability: It involves expanding the memory space by adding more resources such as CPU cores, RAM, powerful CPUs to a single base server. There might be performance and efficiency limitations with growing resources requirements.
• Horizontal Scalability: It means scaling horizontally by adding more servers. The data is spread across these multiple servers to handle heavy write load or massive data where SQL fails. It is done to support data replication with *sharding* and partitioning.

3. Consistency and Reliability:

Data Consistency has always been a major factor in choosing the database. With the increase in use of the model or application, it is crucial to ensure that all the changes and modifications in the database are applied consistently. This ensures reliability of the transactions that the data is up to date.

Here are the key factors to ensure the same:

• ACID Properties and Robust Consistency: If the database has adherence to the ACID properties (Atomicity, Consistency, Isolation, and Durability) and have robust consistency, Relational/SQL database will be more pertinent. Relational/SQL enforce reliability of transactions and the consistency of data.
• Eventual Consistency and trade-offs: Eventual Consistency gives a guarantee that when you update data in a distributed database, all the nodes that store that data will eventually catch up with the change. In the end, you’ll get the same response from any node you query. Non-Relational/NoSQL will be more effective in this case.

4. Query Complexity and Frequency:

As previously mentioned, when determining the appropriate database solution, it’s essential to consider the specific needs of your application.

• SQL for Complex Operations: SQL enables the performance of intricate operations such as filtering, sorting, grouping, and joining multiple tables based on specific conditions. SQL is a powerful query language, making it ideal for applications with complex querying requirements.
• NoSQL for Simple Lookups/Updates: If your application primarily performs simple lookups or updates, using NoSQL can enhance performance.NoSQL databases are advantageous for high read-write performance, particularly with simple data models.

5. Performance and Latency

As previously mentioned, when determining the appropriate database solution, it’s essential as they are critical aspects of an application

• For high performance and low latency: Choose a NoSQL database if your workload or data access patterns demand it, as NoSQL is optimized for these scenarios. Also provides superior performance over large-scale data and complex relationships.
• General-purpose performance: Choose a SQL database can provide a robust interface. It may not optimized for specific workload or data access pattern but still offers reliability and consistent performance.

6. Operational Complexity and Maintenance

a. Deployment:

• Ease of deployment and integration with existing infrastructure. Some
• setups may be more complex than others.

b. Monitoring:

• Includes performance metrics, error tracking, and logs.
• Helps identify and address issues for smooth operations.

c. Backup and Recovery:

• Easy to create and restore backups.
• Handles disaster recovery scenarios effectively.
• Essential for protecting data and maintaining business continuity.

d. Security:

• Features like encryption and access controls.
• Protects sensitive data and reduces the risk of breaches.

e. Community and Open-source:

• Provides valuable resources such as documentation, tutorials, and forums.
• Offers professional assistance and guidance when needed.

f. Cost:

• Consider licensing, hardware, and operational expenses.
• Align with the budget.

Conclusion

NoSQL databases, like MongoDB, offer flexibility and scalability, handling unstructured data and high write loads efficiently. They are ideal for dynamic data models but not for complex queries and transactions. In contrast, MySQL, a relational database, ensures data consistency and reliability through ACID properties, supporting structured data and complex operations. When choosing a database, consider data structure, scalability, consistency, and performance needs. MongoDB suits flexibility and scalability, while MySQL is optimal for structured data and complex transactions.

If you’re interested in diving deeper, consider reading an article on the differences between MongoDB and SQL to further expand your understanding of database technologies.