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

What’s the difference between a graph database and a relational database?

Both graph databases and relational databases store data items with predefined relationships between them. However, they represent the data relationships very differently. Relational databases store data in a tabular format with rows and columns. Related data is also stored in tables, and the data points link back to the original table. Operations related to data relationships become inefficient as they require multiple data table lookups. In contrast, a graph database stores data as a network of entities and relationships. It uses mathematical graph theory to store and perform operations on data relationships. Graph databases are much more efficient in relationship modeling. They improve application performance significantly for use cases with complex data interconnections.

Read about relational databases »

Read about graph databases »

Data model: graph database vs. relational database

Both graph and relational databases store information and represent relationships between data. However, the relational model prioritizes data entities while the graph model prioritizes relationships between the entities.

Relational database model

Relational database use data tables that organize information into rows and columns. Columns hold specific attributes of the data entity, while rows represent the individual data records.

The fixed schema of relational databases requires that you outline relationships between tables upfront with primary and foreign keys.

Example

Consider a social media application with customer profiles that can be friends with each other. You would need two tables to model the data.

The customer table could look like this:

 ID Name Location C1 Alejandro USA C2 Ana USA C3 Kwaku USA C4 Pat USA

The friends table could look like this:

 Customer ID Friend ID C1 C2 C1 C3 C2 C4 C2 C1 C3 C1 C3 C4

As you can see, there’s redundancy and duplication when representing complex relationships. It can increase storage requirements and decrease performance at scale.

Graph database model

On the other hand, a graph database uses a graph structure with properties, edges, and nodes to represent data. Nodes are objects, edges demonstrate the relationship between those nodes, and properties describe the attributes of the nodes and edges. This dynamic structure makes a graph database useful for connected data representation. It offers more flexibility regarding relationships and data types.

Example

The data for the social media application from the previous section would now be represented like this:

{customer_id: “C1”
name: “Alejandro”
location:”USA”

friends:”C2,C3”}

There’s no more duplication or redundancy of data records while modeling relationships.

Key differences: graph database vs. 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

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

Summary of differences: relational database vs. graph database

 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.

How can AWS help with your relational and graph database requirements?

Amazon Web Services (AWS) has solutions for both relational and graph database use cases.

Amazon Relational Database Service (Amazon RDS) is a collection of managed services that makes it simple to set up, operate, and scale a relational database in the cloud. Amazon RDS supports several database engines, like these:

Similarly, Amazon Neptune is a purpose-built, high-performance graph database engine. It’s optimized to store billions of relationships and query the graph with milliseconds of latency.

Neptune supports the popular graph models—property graph and W3C's Resource Description Framework (RDF). It also supports query languages like Gremlin and SPARQL, so you can build queries that navigate highly connected datasets.

Neptune offers several features:

• It’s highly available with read replicas, point-in-time recovery, continuous backup, and replication across Availability Zones.
• It’s secure with support for encryption at rest.
• It’s fully managed. So, you no longer need to worry about database management tasks such as hardware provisioning, software patching, setup, configuration, or backups.

Get started with graph and relational databases on AWS by creating an account today.