One of the most critical choices when selecting a database system is deciding whether to use a relational or non-relational (Relational/SQL vs NoSQL) data structure. Both are viable options, but they differ significantly from each other, so users need to be aware of these differences before deciding which one suits their needs best. We look at some key differences between relational databases (RDBMS or SQL) and non-relational databases (NoSQL), including their strengths and weaknesses.
What is SQL or Relational Database?
Relational databases have been used in many enterprise applications for decades. Their usage technology has evolved, improved, and become widespread. Relational databases store and provide access to data points that relate to each other.
Relational databases manage their data using a “relational model” developed by IBM research scientist Edgar F. Codd in the early 1970s. A relational database stores data using tables that contain rows and columns. SQL allows programmers to access these databases by writing queries that specify what they want from them.
SQL is strongly aligned to ensure data integrity within transactions in Relational Database by providing ACID guarantees. The SQL ACID (Atomicity, Consistency; Isolation, Durability) refers to four properties of database transactions. Whatever happens during the middle of the transaction won’t affect the integrity of your database at any point later on.
What is NoSQL or Non-relational database?
When people mention “NoSQL database,” it is referred to as the non-relational database. Non-relational databases (NoSQL) are database systems that don’t use traditional row/column structures for storing data.
NoSQL was first introduced during the late 2000s when the price for storing large amounts of data dropped significantly. Nowadays, there aren’t any longer the days when we need to create complicated, hard-to-maintain data models so that we don’t end up duplicating our data.
With increasing importance placed upon software development by companies, programmers started becoming the primary expense associated with application development. So, companies began looking at ways to optimize their database systems to enhance developers’ productivity.
It focuses on scalability, speed, allowing for frequent application updates, and simplifying development for programmers.
A NoSQL database holds unstructured or semi-structured data, usually in key-value pairs or JSON documents. Most no-SQL databases don’t offer any transaction support (ACID guarantees) beyond the scope of one database partition. For high-volume services requiring quick responses, NoSQL databases are preferred because they can handle large amounts of transactions per second (TPS).
NoSQL model saves data in the below formats:
Document Store or Document Database
It is a non-relational database that stores information as structured documents. It is a modern approach to storing data in JSON format rather than simple rows and columns. A document can be any of the following: a PDF, a document file, an XML, or a JSON file.
Key-Value database type of NoSQL database stores data as a collection of numerous key-value pairs. The keys act as unique identifiers for the respective values. The values can be of any type — a number or a String, or even another key-value pair, in which case, the structure of the database becomes more complex.
A wide-column store or extensible record store is a NoSQL database wherein data is stored in nested key-value pairs. Though It uses tables, rows, and columns, it is not like a relational database. The names and format for the columns can vary from one row to the next in the same table. A wide-column database can be interpreted as a 2-dimensional key-value store.
A graph database focuses on the relationship between data elements. Each element is stored in a node, and its association is referred to as a link. Data is stored in a graph as nodes, edges, and properties.
Compare SQL vs NoSQL Database
Here are a few differences between SQL and NoSQL databases.
|SQL Databases||NoSQL Databases|
|Data Storage Model||Tables with fixed rows and columns. Tables are related to each other||Different Data Storage types – Document store, Key-Value pairs, Wide-column storage, graph storage with nodes and edges|
|Supporting Databases||Microsoft SQL Server, Oracle, MySQL, and PostgreSQL||Document: MongoDB, CouchDB Key-value: Redis, DynamoDB Wide-column: Cassandra, HBase Graph: Neo4j, Amazon Neptune|
|Primary Purpose||Data Storage without duplication||Document: Data storage Key-value: large amounts of data with simple lookup queries Wide-column: large amounts of data with predictable query patterns, Graph: analyzing and traversing relationships between connected data|
|Database Schema||Fixed or Rigid||Flexible|
|Scaling||Supports vertical scaling with a larger database server||Supports horizontal scaling|
|ACID Transactions||Supported||NoSQL databases are not designed to support multi-record ACID transactions. However, few NoSQL databases like MongoDB support|
|Joins||Relational data is collated with each other using Joins||Not required|
|Data to Object Mapping||Requires ORM (object-relational mapping)||Many of them do not require ORMs|
What are the Benefits of NoSQL Databases?
NoSQL databases offer many benefits over relational databases. NoSQL databases have flexible data models, scale horizontally, have swift queries, and are easy for developers to work with.
Quick Response time to queries
NoSQL database queries return results quickly when compared to SQL databases. This happens as data in SQL databases are normalized to avoid data duplication and data storage space. So, developers must join multiple tables to fetch data for a single object or entity. With the increase in data, tables grow in size leading to more expensive joins. NoSQL databases save data to optimize it for queries. As complex joins and subqueries are not required, queries return resultsets faster.
One of the significant benefits of the NoSQL database is that they have dynamic or flexible schemas. This allows you to make quick database changes as your application requirements change without compromising other features. This helps developers to develop and continuously deploy new features for users quickly.
If you use a Relational database and want to scale the database for size, you need to provide additional infrastructure to support it. This is referred to as Vertical scaling and has a significant impact on costs.
With NoSQL databases, you can scale your database horizontally. You do not need to provision bigger servers here, and you can replicate and add similar cheaper, commodity servers as required.
Most of the NoSQL databases are easier to use for developers. e.g., MongoDB maps its data structure to programming languages. This mapping allows developers and data scientists to store their data similarly to how they use it in their applications. While it may seem like a trivial thing to do, this mapping can allow your code to be more concise, leading to shorter development times and fewer bugs.
What are the Drawbacks of NoSQL Databases?
One of the most crucial drawbacks of NoSQL is that they don’t provide ACID (atomicity, consistency, isolation, durability) transactions across multiple documents. MongoDB has addressed this challenge and added support for ACID transactions in recent versions.
NoSQL can support single record atomicity along with appropriate schema design. However, many applications require ACID across multiple records.
NoSQL databases are comparatively larger in size than relational databases as NoSQL databases are optimized for queries and not for reducing data duplication. Data storage costs have dropped significantly in the last couple of years. Hence, database size-related shortcomings are hardly having any impact on applications. Also, few NoSQL databases support compression to reduce the storage footprint on the server/cloud.
As discussed in the previous section, different NoSQL databases target a specific use case. If you decide to use a NoSQL database for developing your application, you will have to choose the NoSQL database type that best suits your use case. For example, graph databases are excellent for analyzing relationships between your data but may not provide what you need for simple data retrieval, such as range queries. When selecting a NoSQL database, consider your use cases and if a general-purpose database like MongoDB would be a better option.
We hope you enjoyed our blog post about choosing a data structure for your website. NoSQL vs SQL is one of the most critical choices you will make when selecting a database system. We’ve outlined the pros and cons of each and hope that it will help you make the decision. We hope you found this blog post helpful! If you have any questions or feedback, please feel free to contact us.