What’s the Difference Between a Graph Database and a Relational Database?

Beyond their differing data models, relational and graph databases have many differences that set them apart in function and utility.

Operations

You use graph traversal algorithms to query a graph data model. These algorithms are either depth-first or breadth-first, and this helps to find and retrieve connected data rapidly. Graph databases are useful for complex interconnections and queries as they can understand relationships between data. 

In contrast, relational databases employ SQL to retrieve and manipulate data. With SQL, users can perform various types of queries—such as SELECT, INSERT, UPDATE, and DELETE—on tables. Relational databases excel in handling structured data with well-defined relationships between tables. They're particularly effective for performing complex filtering, aggregations, and joins across multiple tables.

Scalability

When scaling relational databases, you typically scale vertically. Vertical scaling is where you upgrade hardware, such as CPU, storage, or memory, to increase the workload a server can handle. Vertical scaling has limitations, which can create challenges alongside the cost requirement. 

Relational databases can also use sharding to scale horizontally, where you distribute data across many servers. However, sharding increases the complexity of data storage and may lead to issues with consistency.

In contrast, graph databases are great at horizontal scaling and use partitioning to do so. Partitions are all on different servers, which allows many servers to parallelly process graph queries. By distributing across many nodes, the database engine can effectively query data, even at scale. 

Performance

Graph databases offer index-free adjacency, which increases performance. Index-free adjacency allows the system to traverse between related entities. As graph databases store relationships as references or pointers between nodes, a database can follow a memory pointer and rapidly navigate between entities. In this case, the database doesn’t need need indexes or mapping tables.

This index-free adjacency system allows graph databases to achieve constant-time relationship traversal. Constant time means you can consistently traverse a relationship in a graph database in the same amount of time, no matter the data size. The direct connection between nodes allows for immediate access, so you can rapidly query and trace relationships. These features make graph databases very efficient. 

Alternatively, relational databases use index lookups and must scan tables to identify relationships between entities. You can join multiple tables, but it’s time-consuming as the system has to scan larger indices over more data. Due to this, a relational database doesn’t offer the same performance as a graph database.

Ease of use

Graph databases are relationship-centric, which makes them easy to work with when you’re using connected data. These databases excel at multi-hop queries, where you traverse paths with multiple relationships. You can also use graph query languages like Gremlin or Cypher to express relationships visually. You can explore interconnected data with these languages, and this simplifies the syntax you use to explore nested and joined data.

Relational databases use SQL, which can feel unnatural when you manage multi-hop queries. If a query has multiple joins and spans over nested subqueries, the SQL becomes challenging to write. If you aren’t careful, this can easily translate into bulky queries that are hard to read and maintain.

That said, relational databases are mature and popular in various use cases. There are several tools and resources as well as community support that you can access to optimize your system. Equally, they excel when managing structured data in a reliable and ACID-compliant manner. The ACID properties are atomicity, consistency, isolation, and durability and help ensure data validity.

When to use: graph database vs. relational database

Graph and relational databases have many effective use cases. As they have different data models and several core distinctions, they excel in different areas. 

Graph database

Graph databases provide a flexible schema that allows for dynamic changes and adaptations to data. The focus on data relationships makes it useful in analytics, semantic searches, or recommendation engines. A graph database is the better choice in these scenarios:

• You’re working with data that has complex relationships, like in social networks, fraud detection, knowledge graphs, and search engines
• You need an evolving schema, as you can modify edges, nodes, and properties without disturbing the rest of the database structure
• You are working with interconnected data and need to conduct three or more hops between relationships (friend-of-friend type queries)

Graph databases are flexible, scalable, dynamic, and excellent at showing relationships between data.

Relational database

Relational databases offer a structured schema with great support for data integrity. A relational database is the better choice in these scenarios:

• You need ACID compliance and high levels of data integrity and consistency, like in financial transactions
• You’re working with highly structured data that fits well into the tabular data model, like in enterprise resource management
• Your data has limited relationships

	Relational Databases	Graph Databases
Model	Tabular with rows and columns.	Interconnected nodes with data represented as JSON documents.
Operations	SQL operations like create, read, update and delete (CRUD).	Operations include CRUD and graph traversal operations based on mathematical graph theory.
Scalability	Traditional relational databases can scale vertically but struggle with horizontal scaling.	A graph database excels at scaling horizontally. It can use partitioning to distribute data across many nodes.
Performance	Relational databases face complex queries when traversing relationships that can slow down performance.	A graph database excels in representing and querying relationships between data.
Ease of Use	Relational databases work well with large datasets and structured data. They struggle when it comes to multi-hop queries.	A graph database is easy to use when dealing with relationship-centric data. Using a graph query language, you can rapidly query multiple hop data.