GraphQL vs REST API: Which is a Natural Fit for Graph Databases?

Choosing between GraphQL and REST APIs is more than just a technical preference. It’s about deciding how your application will interact with your data.

APIs act much like waiters in a restaurant: they take your request, deliver it to the kitchen (your database), and return exactly what you asked for.

The way this exchange is handled can significantly impact everything from system performance to developer experience, especially when dealing with interconnected data, common in graph databases.

This blog examines how GraphQL and REST compare in that context, and why GraphQL is a natural fit for graph data.

A Brief Overview of REST and GraphQL

Let’s examine what REST and GraphQL actually mean before diving deeper into how they interact with graph databases.

REST API

For over a decade, REST (Representational State Transfer) has been the go-to method for building APIs. It operates on the idea of "resources" (distinct objects accessed via unique URLs).

Imagine a well-organised filing cabinet. Each folder has a specific label, and you must know exactly where to look for the information you need. REST APIs work similarly, mapping directly to standard HTTP methods:

• GET to retrieve data
• POST to create new entries
• PUT or PATCH to update existing data
• DELETE to remove data

For instance, Imagine you want to fetch a user and the posts they’ve written. In a RESTful design, you’d likely make two separate requests:

GET /api/users/42
→ returns: { id, name, email }
 
GET /api/users/42/posts
→ returns: [ { id, title, publishedAt }, {...} ]

This approach, while straightforward, means that retrieving related data often requires multiple trips back and forth between your application and the server. This can become inefficient, especially with complex data relationships or when you only need a small portion of the available information.

GraphQL

GraphQL, developed by Facebook in 2012, takes a fundamentally different approach. Instead of relying on multiple endpoints with predefined responses, it uses a single endpoint where clients can specify their exact data needs.

The core innovation of GraphQL is that it lets you request multiple resources in a single query, allowing you to define precisely which fields you want. This gives front-end developers the ability to fetch everything they need in one go, eliminating over-fetching (getting more data than you need) and under-fetching (requiring multiple requests) problems common in REST.

So, using our previous example, a GraphQL query would look like:

query {
  User(id: 42) {
    name
    email
    posts {
      title
      publishedAt
    }
  }
}

And the response you receive:

{
  "data": {
    "User": {
      "name": "Jane Doe",
      "email": "jane@example.com",
      "posts": [
        { "title": "Graph Databases 101", "publishedAt": "2024-10-01" },
        { "title": "Query Optimization Tips", "publishedAt": "2025-01-15" }
      ]
    }
  }
}

This single query retrieves all relevant data in the desired format. This is especially useful when working with graph data, where data is interconnected.

What makes GraphQL particularly interesting for graph databases is how it naturally mirrors graph data structures. Plus, the ability to nest queries lets you traverse these connections, mirroring the underlying data relationships.

GraphQL vs REST API: Quick Comparison

GraphQL vs REST API: The Graph Database Context

In the early days of graph databases, REST was the default choice for API design simply because specialised query languages hadn’t yet matured. These early implementations required developers to work around REST limitations when trying to express graph traversals and relationship-based queries.

As graph technology evolved, specialised query languages like Cypher emerged alongside protocols like Bolt, designed specifically for efficient graph data traversal. These advancements highlighted REST’s limitations when dealing with the dynamic, relationship-driven queries that graph databases excel at.

REST endpoints typically map to specific resources rather than relationships between them. This creates a fundamental mismatch when working with graph data, where the connections between entities often hold as much value as the entities themselves. Retrieving complex graph structures via REST means either creating numerous specialised endpoints or forcing clients to make multiple sequential requests, neither of which is ideal.

GraphQL, by contrast, mirrors the connected nature of graphs in its very design. Its query structure allows for multi-hop nesting and relationship traversal that closely resembles how we think about and visualise graph data. This structural similarity means developers can fetch deeply nested, relationship-based data in a single query, following the natural shape of the underlying graph database.

This alignment between GraphQL’s query structure and graph data becomes especially valuable as data models grow more complex. Instead of forcing graph data through the flat resource model of REST, GraphQL allows applications to work with connected data in its natural form, creating a more intuitive developer experience and more efficient data retrieval pattern.

That’s why many modern applications, including ones built at Facebook and GitHub, lean toward GraphQL for querying connected data.

Memgraph + GraphQL: The Natural Fit

When you pair GraphQL with Memgraph, the advantages of GraphQL become even more pronounced. Memgraph’s architecture and real-time capabilities create a powerful synergy with GraphQL.

Here’s a breakdown of why this combination excels:

1. GraphQL Mirrors Memgraph’s Graph Model

Memgraph is a property graph database, optimised for highly connected data and graph algorithms. GraphQL’s query structure naturally maps to Memgraph’s nodes and relationships, making it easy to fetch deeply connected entities in one query.

In REST, getting related data (for example, a user, their orders, and the products in those orders) often means multiple round-trips to the server, one for each piece of the puzzle. GraphQL handles this in a single query by traversing graph paths dynamically (e.g., user → orders → products).

2. Optimised with GQLAlchemy

Memgraph integrates with GQLAlchemy to create a smooth connection between GraphQL operations and Memgraph’s Cypher query language. This integration layer handles the translation from frontend GraphQL queries to backend Cypher commands efficiently.

This reduces the development burden of manually mapping between API endpoints and database queries. Instead, developers can focus on defining the right graph schema and let the integration layer handle the translation work, which simplifies the development process.

3. Supporting Real-Time Graph Applications

Many graph applications require near-instantaneous updates as the underlying data changes. Memgraph supports streaming data through platforms like Kafka and Pulsar, while GraphQL offers subscription capabilities that reflect these changes to client applications. This combination works particularly well for use cases like fraud detection systems, where pattern changes in the graph must trigger immediate alerts, or for social network analysis that need to update as new connections form. The GraphQL subscription model provides a clean interface for these real-time graph updates.

4. Flexibility for Frontend Teams

GraphQL's client-driven approach offers significant advantages for frontend teams developing applications with Memgraph. Instead of relying on predetermined REST endpoints, UI developers can query exactly the graph data needed for their components.

This enables frontend teams to define complex graph traversals without requiring backend changes, which is particularly valuable when building interactive graph visualisation tools, dashboards that analyse network connections, or any interface where the required data shape might change based on user interaction.

5. Simplifying Complex Graph Traversals

Graph databases like Memgraph are designed for complex relationship traversal, a task that REST struggles with. For example, finding all connections between two people within a three-hop radius is straightforward in a graph database but would require complex query parameters or multiple requests in REST.

GraphQL allows clients to express these multi-hop traversals directly in its query structure. This simplifies the API by offering a single, flexible interface instead of numerous specialised endpoints for various traversal patterns.

However, keep in mind, GraphQL is not a graph database query language. The standard lacks certain advanced functionalities, such as deep-path traversals.

6. Schema-Driven Development

While Memgraph is primarily a schema-less DBMS, it supports data types, constraints, and even user-defined types through GQLAlchemy. This aligns well with GraphQL’s strongly typed schema system, creating a smooth and consistent developer experience from database to API.

A defined GraphQL schema offers immediate benefits like autocompletion, validation, and self-generating documentation. It acts as a single source of truth for your data model, helping teams easily understand the structure of the data and how different entities are connected.

7. Enhanced Developer Tooling

The GraphQL ecosystem offers robust tools like Apollo Studio, GraphiQL and GraphQL Codegen that work seamlessly with Memgraph-backed applications. These tools provide interactive query building, performance monitoring, and schema exploration.

For teams already using Memgraph Lab for database visualization, adding GraphQL creates a complementary API exploration layer. Developers can visually explore the graph in Memgraph Lab, and then craft the precise GraphQL queries needed to access that data in their applications.

Final Thoughts

The best choice between GraphQL and REST for your graph database API depends on what your application needs to do. If your system focuses on complex relationships and requires flexible ways to query data, GraphQL is a natural fit. It aligns well with how graph databases like Memgraph store and process information, making it easy to retrieve interconnected data with a single query.

REST remains a solid option for applications that primarily handle fixed, simple resources where relationships are less important. However, as your data becomes more connected, REST’s limitations can lead to complicated workarounds. GraphQL avoids these issues.

Graph databases like Memgraph excel at representing the real-world complexity of interconnected data. GraphQL complements this by providing an API layer that respects these relationships, resulting in simpler APIs, more efficient queries, and a better experience for developers working with graph data.

Memgraph integrates smoothly with GraphQL through various tools and direct Cypher query support. This combination offers the performance advantages of a specialised graph database alongside GraphQL’s flexibility and ease of use, providing a powerful foundation for building modern applications that rely on connected data.

Ready to try GraphQL with Memgraph? Start with our documentation on how to integrate GraphQL with Memgraph. If you want to dive straight into implementation, you can check out Memgraph’s E2E tests.

If you want to just play with Memgraph without the added complexity of GraphQL, the best place to start is GQLAlchemy, especially if you are developing Python applications.