Breaking the Chains of Relational Databases: A Guide to Making the Move to NoSQL: Architect Guide

Relational databases and NoSQL databases are two different types of database management systems, each with their own strengths and weaknesses. Relational databases have been around for decades and are based on the relational model, where data is organized into tables with predefined relationships between them. They are highly structured and use a standardized query language (SQL) to manipulate the data. 

On the other hand, NoSQL databases are designed to handle unstructured or semi-structured data and do not use the traditional tabular schema seen in relational databases. They are highly flexible and can accommodate large volumes of data with high performance, making them an ideal choice for modern applications that require scalability and agility. In today's blog post in Architect Guide, we will discuss on to decide when to use a NoSQL database.

When considering the usage of NoSQL databases versus relational databases, it's important to understand the specific strengths of NoSQL. NoSQL excels in situations where quick access to vast amounts of data is necessary. NoSQL is also highly adaptable to flexible data models that are common in modern applications. In such scenarios, the data model may not be fully defined at the outset, but developers can still utilize NoSQL to create flexible data models that accommodate both semi-structured and unstructured data, allowing them to begin programming the application immediately.

When transitioning to the production phase, a NoSQL database provides replicated data that is highly available. Unlike traditional relational databases, NoSQL data replication doesn't require primary read/write and secondary read-only nodes. By design, replicated data is kept consistent and NoSQL databases use a shared-nothing architecture to ensure high availability. While both relational and NoSQL databases can scale through sharding, achieving consistency during horizontal scaling requires significant planning and effort with a relational database.

Sharding is the process of dividing data into smaller pieces that can be distributed across multiple computing resources. This method is commonly used to improve performance and scalability. For example, data can be sharded based on geographic location, with the goal of reducing latency by storing the data in a data center near the users who access it most frequently. Another example could be: when data is partitioned based on a specific attribute, such as customer ID or transaction date. Each shard would contain a subset of the data, and queries would be distributed across all the shards to retrieve the required information. By dividing the data in this way, sharding can help distribute the workload and improve the overall performance of the system.

Compared to a traditional relational database system, NoSQL databases typically exhibit faster read/write speeds. NoSQL databases are particularly well-suited to handling unstructured data, whereas a relational database requires a schema to be defined before any data can be added to the system. Making changes to the schema to accommodate new requirements can be a complex and time-consuming process in a traditional database. Queries involving a normalized relational data model can be computationally intensive and resource-consuming, making them unsuitable for real-time applications. These complex queries also do not scale well.

NoSQL databases are highly suitable for modern application development and can easily operate at Internet scale. When working with frequently changing unstructured data or large volumes of data that require scaling up and out, it is advisable to consider NoSQL databases as the first option.

Here is the summary of the key points we have discussed so far:

1. Relational databases and NoSQL databases are two different types of database management systems, each with their own strengths and weaknesses.
2. Relational databases are based on the relational model, where data is organized into tables with predefined relationships between them, and use SQL to manipulate the data.
3. NoSQL databases are designed to handle unstructured or semi-structured data and do not use the traditional tabular schema seen in relational databases.
4. NoSQL databases are highly flexible and can accommodate large volumes of data with high performance, making them an ideal choice for modern applications that require scalability and agility.
5. NoSQL databases provide highly available replicated data, without requiring primary read/write and secondary read-only nodes, and use a shared-nothing approach to ensure high availability.
6. Sharding is a means to partition or split data into smaller pieces that are distributed to different computing resources, and can improve performance and scalability.
7. NoSQL databases have faster read/write speeds compared to traditional relational databases.
8. NoSQL databases are particularly well-suited to handling unstructured data that changes frequently or involves large volumes of data that require scaling up and out.
9. Relational databases require a schema to be defined before any data can be added to the system, and making changes to the schema can be a complex and time-consuming process.
10. Queries involving a normalized relational data model can be computationally intensive and resource-consuming, making them unsuitable for real-time applications.

Here are some key points while comparing SQL and NoSQL:

SQL (Relational Databases)

1. Based on the relational model
2. Data is organized into tables with predefined relationships
3. Highly structured
4. Uses a standardized query language (SQL)
5. Schema changes due to new requirements can require significant rework
6. Queries with a normalized relational data model can be computationally intensive and resource-consuming

NoSQL

1. Does not use the traditional tabular schema seen in relational databases
2. Designed to handle unstructured or semi-structured data
3. Highly flexible and can accommodate large volumes of data with high performance
4. Provides highly available replicated data and uses a shared-nothing approach to ensure high availability
5. Sharding can be used to improve performance and scalability
6. Well-suited to handling unstructured data that changes frequently or involves large volumes of data that require scaling up and out

When it comes to Microservices, NoSQL databases are well-suited to this architecture, which is a popular approach to building modern, scalable, and distributed applications. Microservices are small, independent services that work together to accomplish a larger goal, and they require flexible, scalable, and highly available data storage solutions. NoSQL databases offer several benefits for microservices, including their ability to scale horizontally, their flexible data model that accommodates different types of data, and their support for high availability and fault tolerance. With NoSQL databases, microservices can operate efficiently and effectively, supporting the demands of modern, cloud-native application development.

By understanding the key differences between these two types of databases, you can make an informed decision on when to use NoSQL databases for your modern application development needs. We hope this article has provided some helpful insights into this topic.