Relational Database Vs NoSQL: 7 Critical Aspects

The question, Relational Database vs NoSQL is one of the most critical questions a solution architect has to answer while designing an application in the modern Big Data era. NoSQL Databases excel in their ability to store data in a non-structured form as documents or key-value pairs. They allow for denormalized storage. Relational Databases, on the other hand, require the data to be stored in a structured and normalized way. 

While it may seem that NoSQL Databases save a lot of time in database definition initially in the development process, the fact remains that a well-defined database schema with a Relational Database can give a sizable performance advantage in some cases. This article will highlight the key factors to keep in mind while comparing Relational Databases and NoSQL Databases to help you make a decision.

Relational Databases?

• Structured Data Storage: Relational Databases excel at storing structured data efficiently, using denormalization to minimize storage footprint.
• Comprehensive Querying: They offer robust querying capabilities but accessing denormalized data often requires complex joins.
• Proven Track Record: Relational Databases have a long history and are well-established for various applications, available as licensed and open-source solutions.
• ACID Compliance: They strictly adhere to ACID guarantees, making them suitable for transactional data.
• Single Node Design: Traditionally, Relational Databases operate on a single node architecture.
• Challenges with Scaling: While some support clustering via sharding, it’s less elegant compared to native partition tolerance in NoSQL databases, leading to higher costs and management overhead.
• Consistency and Availability: Relational Databases prioritize consistency and availability over partition tolerance, which limits their scalability for handling large datasets and multi-node operations.

What are NoSQL Databases?

• Flexible Data Model: NoSQL databases excel in storing semi-structured or non-structured data without enforcing rigid schemas. This allows for dynamic addition of data attributes without affecting the entire dataset.
• Limitations in Joins: Due to their schema-less nature, NoSQL databases are not optimized for complex join queries.
• Data Model Optimization: Data is recommended to be stored in a format that aligns closely with how it will be accessed or used, often tied closely to the application’s UI or reporting needs.
• Horizontal Scalability: NoSQL databases are designed for horizontal scaling, leveraging partition tolerance inherent in their architecture. This makes them ideal for scenarios requiring high throughput and sub-second response times with large data volumes.
• Consistency Trade-offs: NoSQL databases typically prioritize availability and partition tolerance over strong consistency and referential integrity. Most support eventual consistency, making them less suitable for transactional operations.
• Advancements in Consistency: Some newer NoSQL databases like MongoDB are making strides in supporting stronger consistency models, addressing traditional limitations in transactional use cases.

NoSQL is an umbrella term to describe a whole set of databases that do not conform to the structured data format. It consists of the following:

1. Document Databases
2. Key-Value-Based Databases 
3. Column-Oriented Databases
4. Graph Databases

1. Document Databases

Document Databases store data as objects in JSON form. MongoDB is an excellent example of such a database. Documents are considered as independent units. They allow for a seamless mapping from the object world of programming languages to data storage.

2. Key-Value-Based Databases

Key-Value-based Databases store data as a collection of key-value pairs. While they are not very popular in persistent storage, they deserve a mention here because of the widespread use in modern architectures. Key Value-based storage solutions are widely used as caching providers.

They are also used in cases where quick data sharing across multiple services is required. Redis, Memcache, etc are examples of Key-Value-based Databases.

3. Column-Oriented Databases

Column-Oriented Databases store data as a collection of columns and perform great when specific columns are accessed. Data rows can extend across multiple nodes or partitions in these databases.

They work based on the assumptions are rows are large enough to scale across multiple nodes and all columns are accessed rarely together. Hbase and Cassandra are good examples of Column-oriented Databases. 

4. Graph Databases

Graph Databases store data as nodes and relationships. They help users to express complex relationships that exist between data elements and query them using specialized Graph Query Languages. Neo4j, Titan, etc are good examples of Graph databases that can be scaled horizontally. 

Factors that Drive the Relational Database Vs NoSQL Decision

Now that you have a basic idea of both worlds, let us attempt to answer the Relational Database vs NoSQL question of how to make a decision. There is no one-size-fits-all answer here and the decision has to be taken based on attributes of your use cases. The following are the key attributes that drive the Relational Database vs NoSQL decision:

Schema Flexibility

The biggest advantage offered by NoSQL Databases is the flexibility of schema. They allow for attributes to be added or forgotten at will. So the very question that you should ask yourself is whether your use case can take advantage of this schema flexibility. 

Let’s say for example you are trying to implement an IoT platform that stores data from different kinds of sensors, you will be better off choosing a NoSQL Database because you do not have an upfront idea about the attributes of your data and it is bound to change as the application evolves.

On the other hand, if you are implementing a simple web application with all the user attributes known upfront, there really is no reason to look beyond RDBMS.

Workload Volume

As discussed above, NoSQL Databases prioritize partition tolerance. This means they are great at handling large amounts of data and executing typical queries over them. So if your application requires data processing over TBs of data, it is better to go with a NoSQL Database from the start itself.

This is not to say that Relational Database systems do not support TBs of data. Most of them like Oracle can very well handle TBs of data. But if most of your queries touch upon this entire data, then it may be better to think about a NoSQL distributed alternative. 

Another critical factor is that some of the NoSQL Databases need a minimal hardware level to perform acceptably. For example, Cassandra performs best when there is at least a 3 node cluster. But if your data is not enough to fill even a single node, you may have to spend more in the initial phase without using up the resources effectively.

Data Consistency

Relational Databases are great at enforcing consistency. NoSQL Databases mostly go by eventual consistency when it comes to writes. This means, there is a chance that your application will read old data till the time writes are propagated to all the nodes. If your application cannot afford such scenarios, you should use a classic Relational Database.

This limitation of NoSQL Databases makes them a non-starter for transactional loads. Databases like MongoDB have started providing transactional support recently and even then, it is limited to short-duration transactions. 

Storage Requirements

Relational Database systems perform best when data can be expressed in a denormalized form. This allows you to optimize your storage requirements. 

Comprehensive SQL layers with complex joining abilities allow the database to make the most of denormalized data. If your data cannot be expressed like this, your use case may be better served using a NoSQL Database. On the other hand, if your data has well-formed relationships that can be used in denormalizing into multiple levels, you should consider using an SQL database.

Write Performance Requirements

NoSQL Databases compromise consistency to achieve fast write performance. SQL databases offer to write safely with consistency but at the expense of a bit of speed. Eventual consistency may be a strict nonstarter in some use cases but may be acceptable in others.

A good answer to the question ‘Can we afford to let go of strict consistency for faster writes ?’ can help you arrive at the RDBMS vs NoSQL decision quickly.

Read Requirements

RDBMS possesses a great ability to query data and execute complex joins. NoSQL Databases perform best when data is stored in the same form in which they are to be consumed. 

For example, let’s say you are creating a reporting solution. You can choose to implement it by storing data for specific reports in different tables and access it through a simple select statement, in this case, you are better off with a NoSQL Database.

The other choice is to store the base data in a small number of related tables and execute various queries and aggregate them to form different reports and this use case points to using a Relational Database.

Infrastructure Constraints

NoSQL Databases are well known for their ability to run using cheap general-purpose hardware and scale horizontally. Since the cost of a high-end special-purpose instance is more than multiple cheap general-purpose instances, there is the possibility of cost advantage in case you use a NoSQL Database.

This becomes valid only when your data volume is significant enough for a distributed database to make sense. For handling TBs of data, Relational Databases often require high-end special-purpose hardware. 

NoSQL Database	Relational Database
NoSQL Database has no fixed schema.	Relational Database has a fixed schema.
NoSQL Database is only eventually consistent.	Relational Database follows acid properties. (Atomicity, Consistency, Isolation, and Durability)
NoSQL databases don’t support transactions (support only simple transactions).	Relational Database supports transactions (also complex transactions with joins).
NoSQL Database is used to handle data coming in high velocity.	Relational Database is used to handle data coming in low velocity.
The NoSQL?s data arrive from many locations.	Data in relational database arrive from one or few locations.
NoSQL database can manage structured, unstructured and semi-structured data.	Relational database manages only structured data.
NoSQL databases have no single point of failure.	Relational databases have a single point of failure with failover.
NoSQL databases can handle big data or data in a very high volume .	NoSQL databases are used to handle moderate volume of data.
NoSQL has decentralized structure.	Relational database has centralized structure.
NoSQL database gives both read and write scalability.	Relational database gives read scalability only.
NoSQL database is deployed in horizontal fashion.	Relation database is deployed in vertical fashion.

Learn more about : MongoDB Schema Designer.

Conclusion

Choosing one between Relational and NoSQL Databases is often a tough challenge and you will have arguments in favor of both in most cases. Even in the modern Petabyte-scale data architecture, Relational Databases find their place in specific scenarios. This is why most data architectures have both Relational and NoSQL Databases splitting the storage duty and this is where Relational Database vs NoSQL becomes Relational Database and NoSQL.